BenMAP User’s Manual


Abt
Abt Associates Inc. ¦ 4800 Montgomery Lane ¦
Bethesda, MD 20814 ¦ vwvw.abtassociates.com
Abt Associates Inc.
Environmental Benefits Mapping and Analysis Program
User's Manual

al
*1
June 2005
Prepared for
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Bryan Hubbell, Project Manager
Prepared by
Abt Associates Inc.

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Welcome to BenMAP, the Environmental Benefits Mapping and
Analysis Program
BenMAP is a new tool that allows you to perform customized health benefits analyses for
changes in air quality in a powerful, yet easy-to-use program. It was originally developed by the
US Environmental Protection Agency (US EPA) to support the development of national-scale
environmental regulations involving air quality and has been used in a number of recent
regulations in the United States addressing both mobile and stationary sources. To support more
specialized urban/regional analyses and to provide a tool for international use, EPA has
developed a more flexible version of BenMAP, designed to accommodate a wide range of data
inputs.
Overview of BenMAP & Benefits Assessment
BenMAP is primarily intended as a tool for estimating the health impacts, and associated
economic values, associated with changes in ambient air pollution. It accomplishes this by
running health impact functions, which relate a change in the concentration of a pollutant with a
change in the incidence of a health endpoint. Inputs to health impact functions typically include
(a) the change in ambient air pollution level, (b) health effect estimate, (c) the baseline incidence
rate of the health endpoint, and (d) the exposed population. For example, in the case of a
premature mortality health impact function, we might have the following:
Mortality Change = Air Pollution Change * Mortality Effect Estimate * Mortality Incidence
* Exposed Population
^ Air Pollution Change. The air quality change is calculated as the difference between the
starting air pollution level, also called the baseline, and the air pollution level after some
change, such as that caused by a regulation. In the case of particulate matter, this is
typically estimated in micrograms per meter cubed (|_ig/m3).
^ Mortality Effect Estimate. The mortality effect estimate is an estimate of the percentage
change in mortality due to a one unit change in ambient air pollution. Epidemiological
studies provide a good source for effect estimates.
Mortality Incidence. The mortality incidence rate is an estimate of the average number of
people that die in a given population over a given period of time. For example, the
mortality incidence rate might be the probability that a person will die in a given year.
Mortality incidence rates and other health data are typically collected each country's
government. In addition the World Health Organization is a good source for data.1
^ Exposed Population. The exposed population is the number of people affected by the air
pollution reduction. The government census office is a good source for this information.
In addition, private companies may collect this information and offer it for sale.
1 See World Health Organization: http ://www.who.int/research/en/
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Introduction
BenMAP also calculates the economic value of health impacts. After the calculation of the
mortality change, you can value these premature deaths by multiplying the change in mortality
reduction by an estimate of the value of a statistical life:
Value Mortality = Mortality Change * Value of Statistical Life
^ Value of Statistical Life. The value of a statistical life is the economic value placed on
eliminating the risk of one premature death.
BenMAP also serves as a Geographic Information System (GIS), allowing users to create, utilize,
and visualize maps of air pollution, population, incidence rates, incidence rate changes, economic
valuations, and other types of data.
BenMAP can thus be used for a variety of purposes, including:
^ Generating population/community level ambient pollution exposure maps;
^ Comparing benefits associated with regulatory programs;
^ Estimating health impacts and costs of existing air pollution concentrations;
^ Estimating health benefits of alternative ambient air quality standards; and
^ Performing sensitivity analyses of health or valuation functions, or of other inputs.
A wide range of persons can use BenMAP, including scientists, policy analysts, and decision
makers. Advanced users can explore a wide range of options, such as using the map querying
features and exploring the impacts of different health impact and valuation functions.
Exhibit 0-1 summarizes the flow of calculations in BenMAP, and the types of choices that you
make regarding the modeling of population exposure, the types of health effects to model, and
how to place an economic value on these health effects. This exhibit also highlights that
BenMAP does not have air modeling capabilities, and instead relies on modeling and monitoring
inputs.
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Introduction
Exhibit 0-1. BenMAP Flow Diagram
Population
Data
Population
Estimates
Air Quality
Monitoring
Air Quality
Modeling
Population
Exposure
Incidence
and
Prevalence
Rates
Healtfi
Functions
Adverse
Health
Effects
Valuation
Functions
User Input Choice
Economic
Costs
Result from Inputs
How to Use this Manual
Chapters 1 through 9 of this manual provide step-by-step instructions on how to use BenMAP.
New users should start with Chapters 1 and 2, which are both very short, but provide a good
overview of the model and how it works, and explain some potentially confusing terminology.
You can then use Chapter 3 to get started using the model. The tutorial in Chapter 3 will show
you how to define two simple scenarios, calculate the change in health effects between them,
assign economic valuations, and create reports and maps. Once you have gone through this
simple tutorial, you can go on to try more advanced functions. Use the rest of the manual to
answer any specific questions you may have, or to walk you step-by-step through the various
model functions. Chapter 4 discusses how to enter data into BenMAP, Chapters 5 through 8
cover each of the four main buttons, and Chapter 9 covers mapping.
Each chapter is introduced by a short section which describes what you can find within the
chapter, and provides an outline of the chapter's contents. This is a good place to go if the Table
of Contents does not provide enough detail for you to find the section you need. The end of most
chapters has a series of "Frequently Asked Questions," which may also be helpful in answering
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Introduction
specific questions. In sections that provide instructions on navigating the model, the following
conventions are observed: menu items, buttons, and tab and selection box labels are in bold type;
prompts and messages are enclosed in quotation marks; and drop-down menu items, options to
click or check, and items that need to be filled in or selected by the user are italicized.
Throughout the chapters you will also see boxes that say ^ TIP. These boxes present common
mistakes and important things to remember when working with BenMAP. Common terms are
defined in Chapter 1, but you can also find definitions in boxes along the right margins inside
other chapters.
There is also a set of Technical Appendices to provide more detailed information on model
functions, data, and underlying assumptions:
Appendix A: Function Editor Syntax
Appendix B: Monitor Rollback
Appendix C: Air Pollution Exposure Estimation Algorithms
Appendix D: Types of Concentration-Response Functions & Issues in the Estimation of
Adverse Health Effects
Appendix E: Sources of Prevalence and Incidence Data
Appendix F: Particulate Matter Concentration-Response Functions
Appendix G: Ozone Concentration-Response Functions
Appendix H: Economic Value of Health Effects
Appendix I: Uncertainty & Pooling
References
Computer Requirements
The computer hardware requirements for BenMAP are typically modest, though this will again vary
depending on the complexity of the analysis. BenMAP requires a Windows platform, and can be used on
machines running Windows2000, as well as more recent versions of Windows. In particular, BenMAP
requires a computer with:
^Windows 2000 or greater.
^ Adobe Acrobat Reader
>^128 megabytes of RAM or greater. In general, the amount of RAM required depends on the
scope of the Grid Definitions defined for the model (specifically, the number of cells within the
Grid Definitions). If the largest grid contains hundreds of cells, 128 MB is required. If the
largest grid contains thousands of cells, 256-512 MB is required. If the largest grid contains tens
of thousands of cells, 1 GB is required.
lintel® or compatible processor, Pentium 166 MHz or higher. 1 GHz processor or greater
recommended for optimum performance.
CD-ROM drive for CD based installation. Alternatively, an internet connection might be
used to download the (-75 MB) installer.
^75 MB free hard disk space, plus room for all data loaded into the application. 250 MB free
space recommended.
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Introduction
Installing Ben MAP
BenMAP 2.2 uses the Microsoft SQL Server Desktop Edition (MSDE) as its underlying database
technology. As such, the installation process for BenMAP 2.2 has two steps - installing MSDE, and
installing BenMAP 2.2.
Get started by double clicking Setup.exe in your installation directory. This will bring up the following
dialog:
Hi
BenMAP 2.0 Installer
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Click on the Install BenMAP 2.2 Database button. This will bring up the MSDE installer. Normally
this installer will complete its work without any input from you. In certain cases, however, it may require
you to reboot your computer after it is finished. If this occurs, simply reboot your computer and then
return to the BenMAP 2.2 installation process by double clicking Setup.exe in your installation directory
again.
Additionally, there are two known issues with the MSDE installer. If you have trouble with this installer,
see the additional notes below.
Once MSDE is installed, the installation dialog will return, with the second button enabled.
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Introduction
1 £ BenMAP 2.0 Installer
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Install BenMAP 2.0
Database
Install BenMAP 2.0




Click on the Install BenMAP 2.2 button. A progress bar will appear as the BenMAP 2.2 database is built
in the installed instance of MSDE. When this process is complete, the BenMAP 2.2 installer will come
up. Follow its directions to install BenMAP 2.2.
Uninstalling BenMAP
To uninstall BenMAP, go to Control Panel, Add/Remove Programs and remove both
Microsoft SQL Server Desktop Edition (BenMAP), and BenMAP 2.2.
MSDE Installation Issues
If you have trouble installing MSDE, the most likely problems are that:
(1)	You don't have File and Print Sharing for Microsoft Networks enabled, or
(2)	You have disabled unsigned driver installation disabled.
For more information on how to fix these issues to enable a successful installation of MSDE, see
section 3.2, Installation Prerequisites, in the file ReadmeMSDE2000A.htm in the MSDE
directory within your installation directory.
Contacts for Comments and Questions
For comments and questions, please contact Bryan Hubbell at the United States Environmental
Protection Agency.
Address: C339-01, USEPA Mailroom, Research Triangle Park, NC 27711
Email: hubbell,brvan@epa.gov
Telephone: 919-541-0621.
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Introduction
Sources for More Information
For files that you can use in BenMAP:
^U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards
(OAQPS), BenMAP website. Available at:
http://www.epa.gov/ttn/ecas/analguid.html
For more information on conducting benefit analysis, see the following documents:
^U.S. Environmental Protection Agency, National Center for Environmental Economics
(NCEE), Guidelines for Preparing Economic Analyses. Available at:
http: //vosemite .epa. gov/ee/epa/ eed .nsf/page s/guideline s
^U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards
(OAQPS), Economic Analysis Resource Document. Available at:
http://www.epa.gov/ttn/ecas/analguid.pdf
^U.S. Environmental Protection Agency, Office of Air and Radiation, Costs and Benefits of the
Clean Air Act. Available at: http://www.epa.gov/oar/sect812/
^U.S. Environmental Protection Agency, Office of Air and Radiation, Draft Regulatory Impact
Analysis: Control of Emissions from Nonroad Diesel Engines, Chapter 9. EPA420-R-03-008,
April 2003. Available at: http://www.epa.gov/nonroad/r03008.pdf
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Table of Contents
Welcome to BenMAP, the Environmental Benefits Mapping and Analysis Program	 -i-
Overview of BenMAP & Benefits Assessment 	 -i-
How to Use this Manual 	 -iii-
Computer Requirements	 -iv-
Installing BenMAP	-v-
Uninstalling BenMAP	 -vi-
MSDE Installation Issues 	 -vi-
Contacts for Comments and Questions	 -vi-
Sources for More Information	-vii-
1.	Terminology and File Types 	1-1
1.1	Common Terms	1-1
1.2	File Types	1-4
2.	Overview of BenMAP Components	2-1
2.1	Modify Setup	2-1
2.2	Main Buttons 	2-2
2.2.1	Create Air Quality Grids	2-3
2.2.2	Create and Run Configuration 	2-4
2.2.3	Aggregation, Pooling, and Valuation 	2-5
2.2.4	Generate Reports	2-6
2.3	Menus	2-8
2.3.1 Tools	2-8
3.	BenMAP Quick Start Tutorial	3-1
Step 1. Start BenMAP 	3-1
Step 2. Create Air Quality Grids for the Baseline and Control Scenarios	3-2
Step 3. Specify Configuration Settings 	3-6
Step 4. Select Health Impact Functions	3-8
Step 5. Specify Aggregation, Pooling and Valuation	3-11
Step 6. Generate Reports 	3-17
Step 7. View Your Reports 	3-20
Step 8. Map Your Results 	3-21
4.	Loading Data	4-1
4.1	Add and Modify Setups	4-1
4.1.1	Manage Grid Definitions 	4-2
4.1.2	Manage Pollutants	4-5
4.1.3	Manage Monitor DataSets 	4-12
4.1.4	Manage Incidence/Prevalence DataSets	4-18
4.1.5	Manage Population DataSets 	4-21
4.1.6	Manage Health Impact Function DataSets 	4-24
4.1.7	Manage Variable DataSets 	4-32
4.1.8	Manage Inflation DataSets 	4-35
4.1.9	Manage Valuation DataSets 	4-37
4.2	Modify and Delete Setups 	4-41
4.3	Export and Import Setups 	4-42
4.3.1	Export Setups 	4-42
4.3.2	Import Setups 	4-43
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Table of Contents
5.	Creating Air Quality Grids 	5-1
5.1	Model Direct 	5-2
5.2	Monitor Direct	5-3
5.2.1	Closest Monitor for Monitor Direct 	5-4
5.2.2	Voronoi Neighbor Averaging (VNA) for Monitor Direct 	5-4
5.2.3	Other Monitor Direct options 	5-7
5.3	Monitor and Model Relative 	5-10
5.3.1	Spatial Scaling 	5-11
5.3.2	Temporal Scaling 	5-11
5.3.3	Spatial and Temporal Scaling	5-11
5.3.4	Examples	5-12
5.4	Monitor Rollback	5-13
5.4.1 Example: Combining Three Rollback Approaches in Different Regions .... 5-17
5.5	Questions Regarding Creating Air Quality Grids 	5-21
6.	Create and Run Configurations	6-1
6.1	Create New Configuration	6-1
6.1.1	Configuration Settings 	6-2
6.1.2	Selecting Health Impact Functions for Configuration 	6-4
6.1.3	Running the Health Effects Incidence Configuration	6-8
6.2	Open Existing Configuration	6-8
6.3	Questions Regarding Configurations	6-9
7.	Aggregation, Pooling, and Valuation 	7-1
7.1	Creating a New Configuration for Aggregation, Pooling, and Valuation 	7-2
7.1.1	Pooling Incidence Results	7-2
7.1.2	Valuing Pooled Incidence Results 	7-14
7.1.3	APV Configuration Advanced Settings 	7-18
7.2	Running the APV Configuration	7-19
7.3	Opening an Existing Aggregation, Pooling, and Valuation (APV) Configuration File
	7-20
8.	Create Reports	8-1
8.1	Incidence and Valuation Results: Raw, Aggregated, and Pooled 	8-1
8.1.1	Incidence and Valuation Results: Incidence Results	8-2
8.1.2	Incidence and Valuation Results: Aggregated Incidence Results	8-4
8.1.3	Incidence and Valuation Results: Pooled Incidence Results 	8-5
8.1.4	Incidence and Valuation Results: Valuation Results 	8-6
8.1.5	Incidence and Valuation Results: Aggregated Valuation Results	8-8
8.1.6	Incidence and Valuation Results: Pooled Valuation Results 	8-9
8.2	Raw Incidence Results	8-10
8.3	Audit Trail Reports 	8-11
8.4	Questions Regarding Creating Reports 	8-12
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Table of Contents
9. Mapping	9-1
9.1	Overview of Mapping Features 	9-1
9.1.1	Display Options 	9-3
9.1.2	Taskbar Buttons 	9-4
9.1.3	Mapping Different File Types with the Tools Menu 	9-6
9.2	Viewing Maps in a BenMAP Analysis 	9-11
9.3	Questions Regarding Mapping	9-17
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Exhibit List
Exhibit 0-1. BenMAP Flow Diagram	 -iii-
Exhibit 1-1. File Types Generated by BenMAP	1-5
Exhibit 4-1. BenMAP Pollutant Definitions	4-6
Exhibit 4-2. Examples of Metric Names	4-7
Exhibit 4-3. Available Functions and Variables for Custom Metrics	4-10
Exhibit 4-4a. Air Monitor Definition Variables - Column Format 	4-15
Exhibit 4-4b. Sample Air Monitor Definition File - Column Format 	4-15
Exhibit 4-4c. Air Monitoring Data File Variables - Column Format	4-16
Exhibit 4-4d. Sample Air Monitoring Data File - Column Format 	4-16
Exhibit 4-5a. Air Monitoring Data File Variables - Row Format 	4-17
Exhibit 4-5b. Sample Air Monitoring Data File - Row Format	4-17
Exhibit 4-6a. Health Incidence and Prevalence Data File Variables	4-20
Exhibit 4-6b. Sample Health Incidence and Prevalence Data File 	4-21
Exhibit 4-7a. Population Data File Variables	4-24
Exhibit 4-7b. Sample Population Data File	4-24
Exhibit 4-8a. Health Impact Function Data File Variables	4-29
Exhibit 4-8b. Sample Health Impact Function Data File 	4-31
Exhibit 4-9a. Variable Data File Variables	4-35
Exhibit 4-9b. Sample Health Impact Function Data File 	4-35
Exhibit 4-10a. Inflation Data File Variables	4-36
Exhibit 4-10b. Sample Inflation Data File 	4-37
Exhibit 4-1 la. Valuation Data File Variables	4-40
Exhibit 4-1 lb. Sample Valuation Data File 	4-41
Exhibit 5-1. Air Modeling Data File Variables	5-2
Exhibit 5-2. Sample Air Modeling Data File 	5-3
Exhibit 5-3. Air Monitoring Data File Variables - Row Format 	5-8
Exhibit 5-4. Sample Air Monitoring Data File - Row Format	5-8
Exhibit 5-5. Air Monitor Definition Variables - Column Format 	5-9
Exhibit 5-6. Sample Air Monitor Definition File - Column Format 	5-9
Exhibit 5-7. Air Monitoring Data File Variables - Column Format	5-9
Exhibit 5-8. Sample Air Monitoring Data File - Column Format 	5-9
Exhibit 7-1. Pooling Approaches for Incidence and Valuation Results 	7-6
Exhibit 8-1. Summary of the Reports Generated from APVR Files	8-2
Exhibit 8-2. Selected Variables in the Reports Based on the APVR file 	8-4
Exhibit 9-1. Description of Mapping File Types 	9-1
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CHAPTER 1
In this chapter...
>- Find definitions for terms
used in the model and in this
manual that may be
confusing.
See the formats for the
externally-generated air
quality modeling or
monitoring files that BenMAP
needs to create air quality
grids.
>- Fins descriptions of the
various types of files used in
BenMAP.
Terminology
and File
Types
Chapter Overview

1.1	Common Terms	
1.2	File Types	
	 1-1
	 1-4

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1.
Terminology and File Types
The first section of this chapter explains common terms used in this user's manual and in the
model, and references, where possible, other sections in this manual to find more detailed
information. Section 1.2 describes in detail the necessary format for externally-generated model
and monitor data files that can be read into BenMAP and used to generate the air quality grid
files.
1.1 Common Terms
^Aggregation. The summing of grid cell level results to the county, state and national levels.
^APV Configuration (Aggregation, Pooling, and Valuation). APV Configurations store the
aggregation levels, pooling options, and valuation methods used in the analysis. In particular, you may
specify the aggregation level for incidence estimates, how to pool incidence estimates, the valuation
estimates to use, the aggregation level for the valuation estimates, and how to pool the valuation
estimates. APV Configurations are stored in files with an ".apv" file extension. The results derived from
an APV Configuration have an ".apvr" file extension. APV files are typically stored in the
Configurations folder, and APV Results files are typically stored in the Configuration Results folder.
>^Air Quality Grid. An air quality grid contains air pollution data. BenMAP uses one air quality grid
for the baseline scenario and a second for the control scenario, in order to estimate the change in the
number of adverse health effects between the two scenarios. Air Quality Grids are stored in files with an
"aqg" file extension. AQG files are typically stored in the Air Quality Grids folder.
^Air Quality Metric. One of the measures typically used for air pollution. These are daily values
calculated directly from daily observations, or through various mathematical manipulations of hourly
observations. Typical ozone metrics include the highest hourly observations during the course of each
day, such as the average of all twenty four hourly observations. These daily values may be summarized
to characterize seasonal and annual values.
>"Binning. The process of summarizing data by sorting the data values from low to high, dividing the
sorted data into a pre-determined number of groups, and then choosing a representative value for each
group, by either averaging the values in each group, or picking the mid-point of each group.
^Configuration. A Configuration stores the health impact functions and model options used to
estimate adverse health effects. Configurations are stored in files with a "cfg" file extension. CFG files
are typically stored in the Configurations folder. The results derived from a Configuration have a "cfgr"
file extension. CFGR files are typically stored in the Configuration Results folder.
^C-R (Concentration-Response) Function. A health impact function estimates the relationship
between adverse health effects and ambient air population, and is used to derive health impact functions
(defined below).
^"Cost of Illness (COI). The cost of illness includes the direct medical costs and lost earnings
associated with illness. These estimates generally understate the true value of reductions in risk of a
health effect, as they include just the direct expenditures related to treatment and lost earnings, but not the
value of avoided pain and suffering from the health effect.
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Chapter 1. Terminology and File Types
"^Endpoint. An endpoint is a subset of an endpoint group, and represents a more specific class of
adverse health effects. For example, within the endpoint group Mortality, there might be the endpoints
Mortality, Long Term, All Cause and Mortality, Long Term, Cardiopulmonary.
^Endpoint Group. A endpoint group represents a broad class of adverse health effects, such as
premature mortality, chronic bronchitis, and hospital admissions. BenMAP only allows pooling of
adverse health effects to occur within a given endpoint group, as it generally does not make sense to sum
together the number of cases of disparate health effects, such as premature mortality and chronic
bronchitis.
Fixed Effects Pooling. Fixed effects pooling is used to combine two or more distributions
(represented by Latin Hypercube points) into a single new distribution. Fixed effects pooling assumes
that there is a single true underlying relationship between these component distributions, and that
differences among estimated parameters are the result of sampling error. Weights for the pooling are
generated via inverse variance weighting, thus giving more weight to the input distributions with lower
variance and less weight to the input distributions with higher variance.
-S^GIS. Geographic Information System. A GIS is a system of hardware and software used for storage,
retrieval, mapping, and analysis of geographic data.
^Grid Cell. One of the many geographic, or spatial components within Grid Definition.
^Grid Definition. A BenMAP Grid Definition provides a method of breaking a geographic region into
areas of interest (Grid Cells) in conducting an analysis. This can be done in two ways - by loading a
Shapefile (a particular type of Geographic Information System file) or by specifying a regularly shaped
grid pattern. These are referred to as Shapefile Grid Definitions and Regular Grid Definitions,
respectively. Typically a Shapefile Grid Definition is used when the areas of interest are political
boundaries with irregularly shaped borders, while a Regular Grid Definition is used when the areas of
interest are uniformly shaped rectangles.
^"Health Impact Function. A health impact function calculates the change in adverse health effects
associated with a change in exposure to air population. Based on a C-R function, a typical health impact
function has inputs specifying the air quality metric and pollutant, the age, race and ethnicity of the
population affected, and the incidence rate of the adverse health effect.
^ Incidence Rate. An incidence rate is the average number of adverse health effects per person per unit
of time, typically a day or a year.
^ Interpolation. The process of estimating the air quality level in an unmonitored area by using one or
more nearby air quality monitors. BenMAP uses twi types of interpolation procedures: one is to simply
choose the closest monitor, the other is to use a technique called Voronoi Neighbor Averaging. These
interpolation methods are discussed in more detail in Appendix C.
^Latin Hypercube. A series of points generated by using specified percentiles in a given distribution,
such as that of a health impact coefficient. It is a short-cut method designed to represent a distribution,
while at the same time saving on computation time. For example, when using 20 Latin Hypercube points,
BenMAP would use the 2.5th, 7.5th, 12.5th, ..., and 97.5th points from the distribution. The Latin
Hypercube points are used when combining the results of different health impact functions (discussed in
Chapter 7), and in presenting confidence intervals for the incidence estimates (discussed in Chapter 8).
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Chapter 1. Terminology and File Types
^Modeling. Estimating air pollution levels through the use of air quality models. The EPA website
discusses a wide range of air quality models: htto://www.epa.gov/ebtpages/airairauaairqualitvmodels.html
and at http://www.epa.gov/ttn/scram/.
^Monitoring. Actual measurements of air pollution levels. The U.S. Environmental Protection Agency
has monitoring data, as well as other information related to monitoring, available through its Air Quality
System (AQS): http://www.epa.gov/air/data/aasdb.html.
^Particulate Matter. Includes PM2 5 (particles less than 2.5 microns in aerodynamic diameter), PMin
(particles less than 10 microns in aerodynamic diameter), and PMC (particles between 2.5 and 10 microns
in aerodynamic diameter).
^Pooling. The combining of different sets of data. BenMAP has several pooling methods, including
fixed effects, fixed/random effects, and subjective weighting. Appendix I discusses the pooling
approaches available in BenMAP.
Point Mode. When defining the configuration, you may choose to either estimate adverse health
effects in point mode or using a Latin Hypercube. The point mode simply means that BenMAP will use
the mean value of the coefficient in the health impact function.
^Population Exposure versus Personal Exposure. Population (or ambient) exposure refers to the
average air pollution level measured in a grid cell. In contrast, personal exposure keeps track over the
course of a day the exposure individuals encounter in different micro-environments, such as the freeway,
outdoors and indoors. BenMAP only keeps track of population exposure.
^Prevalence. A prevalence rate specifies the percentage of individuals in a given population with a
given adverse health effect.
^Random Effects Pooling. Random effects pooling is an alternative to the fixed effects model (see
Fixed Effects Pooling, above), and allows the possibility that the estimated parameter from different
studies may in fact be estimates of different parameters, rather than just different estimates of a single
underlying parameter.
^Relative Risk. Relative risk typically is used as a measure of the change in risk of an adverse health
effect associated with an increase in air pollution levels. More specifically, it is the ratio of the risk of
illness with higher pollution to the risk of illness with a lower pollution level, where the "risk" is defined
as the probability that an individual will become ill.
5^Setup. A BenMAP Setup encapsulates all of the data needed to run analyses for a particular
geographic area - a city, an entire country, etc. These data consist of grid definitions, pollutants, monitor
data, incidence and prevalence rates, population data, health impact functions, variables, inflation rates,
and valuation functions.
^Shapefile. A shapefile is a particular type of Geographic Information System (GIS) file, and has a .shp
extension. These files are accompanied by companion files with .shx and .dbf extensions, and can be used
to create Shapefile Grid Definitions. See http://www.esri.com/librarv/whitepapers/pdfs/shapefile.pdf for
more information.
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Chapter 1. Terminology and File Types
^Subjective Weighting. Subjective weights let you specify the weights that you want to use when
combining two or more distributions of results. The weights should sum to one. If not, BenMAP
normalizes the weights so that they do.
^Unit Value. A unit value is the estimated mean value of avoiding a single case of a particular health
effect.
^Valuation Function. Valuation functions are used by BenMAP to estimate the economic values of
changes in the incidence of health effects. These are selected within an Aggregation, Pooling, and
Valuation Configuration (APV Configuration).
-S^VNA (Voronoi Neighbor Averaging). An algorithm used by BenMAP to interpolate air quality
monitoring data to an unmonitored location. BenMAP first identifies the set of monitors that best
"surround" the center of the population grid cell, and then takes an inverse-distance weighted average of
the monitoring values. This is discussed in detail in Appendix C.
^WTP (Willingness to Pay). The willingness of individuals to pay for a good, such as a reduction in
the risk of illness. In general, economists tend to view an individual's WTP for a improvement in
environmental quality as the appropriate measure of the value of a risk reduction. An individual's
willingness-to-accept (WTA) compensation for not receiving an improvement is also a valid measure.
However, WTP is generally considered to be a more readily available and conservative measure of
benefits.
1.2 File Types
BenMAP has a number of file types that you can use to store the settings used in a BenMAP
analysis, the results of an analysis, as well as maps and reports. Exhibit 1-1 presents the names of
the different file types, their functions, and their default folder locations.
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Exhibit 1-1. File Types Generated by BenMAP
File Extension
Description
Default Folder Location
*.aqg
Air quality grid.
Air Quality Grids
*.cfg
Configuration specifying the health impact functions and other
options used to generate incidence estimates.
Configurations
*.cfgr
Configuration results, containing incidence results at the grid cell
level.
Configuration Results
*.apv
Aggregation, Pooling, and Valuation configuration specifying the
aggregation levels, pooling options, and valuation methods used to
generate aggregated incidence estimates, pooled incidence estimates,
valuation estimates, aggregated valuation estimates, and pooled
valuation estimates.
Configurations
*.apvr
Aggregation, Pooling, and Valuation configuration results,
containing incidence results at the grid cell level, aggregated
incidence results, valuation results, aggregated valuation results, and
pooled valuation results.
Configuration Results
*.shp
Shape files generated by BenMAP's mapping capabilities. These
files can be viewed within BenMAP or within shape file viewers,
such as Arc View.
Maps
*.csv
Reports are exported as *.csv files, which may be viewed in a text
editor, or in programs such as Excel.
Reports
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CHAPTER 2
In this chapter...
Learn about the BenMap
Model Setup
Get an overview of the
functions available with each of
BenMAP's main buttons and
menu items.
Overview of
BenMAP
Components
Learn about the different
options for each function.
Chapter Overview
2.1	Modify Setup		2-1
2.2	Main Buttons		2-2
2.2.1	Create Air Quality Grids		2-3
2.2.2	Create and Run Configuration 		2-4
2.2.3	Aggregation, Pooling, and Valuation	2-5
2.2.4	Generate Reports		2-6
2.2 Menus	2-8
2.2.1 Tools 		2-8

-------
2.
Overview of Ben MAP Components
Upon starting BenMAP for the first time, you will see the following main window.
S BenMAP 2.
Tools Help

Environmental Benefits Mapping and Analyas Program
/ a i
\ Create Air Quality Grids ;

Create and Run
Configuration

Aggregation, Pooling,
and Valuation

Create Reports
Active Setup:

BenMAP's main functions may be accessed by using the four large buttons, which allow you to
perform all of the actions needed to estimate the health benefits of a change in air quality. The
Tools menu at the top of the screen is for less frequently used functions, such as importing and
exporting data and mapping. Section 2.1 describes one of the Tools menu options which will
typically be the first action taken by new users. Section 2.2 describes the main buttons and their
functions, and Section 2.3 describes the remaining Tools menu functions. All of these topics are
covered in greater detail in subsequent chapters of this manual.
2.1 Modify Setup
Before you can get started running analyses with BenMAP, you will need to define and load
various types of data. BenMAP encapsulates the data needed to run analyses for a particular
geographic area - a city, country, etc. - within a Setup. This data consists of grid definitions,
pollutants, monitor data, incidence and prevalence rates, population data, concentration-response
functions, variables, inflation rates, and valuation functions. Later sections will go into more
detail on all of these types of data.
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Chapter 2. Overview of BenMAP Components
To begin loading data into BenMAP, go to the Tools menu and select Modify Setup. This will
bring you to the Setup Manager window.
Available Setups:
Grid Definitions

Pollutants

Monitor DataSets




Edit

Edit

Edit
Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets




Edit

Edit

Edit
Variable DataSets

Inflation DataSets

Valuation DataSets




Edit

Edit

Edit
Cancel	OK
	3
Delete	Add
From the Setup Manager window, you may add new Setups by clicking on the Add button.
You may also edit the contents of a particular Setup by selecting it in the Available Setups drop-
down list and clicking on the various Edit buttons.
2.2 Main Buttons
Returning to the main BenMAP window, you
can choose the Setup you want to use for an
analysis by selecting it from the Active Setup
drop-down list. You are then ready to begin
using the functions available through the four
main buttons.
These four buttons take you through the steps
of an analysis. The first button allows you to
create air quality grids which contain
estimates of population-level exposure to air
pollution. The second button lets you choose
the air quality grids for a particular analysis,
jnjxj
loots Help
Environmental Benefits Mapping and Analysis Program
- __ I
MM
Nfr. —-wwr
Create Aii Quality Grids

Create and Run
Configuration


Aggregation, Pooling,
and Valuation

Create Reports
Active Setup:

City One
•
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Chapter 2. Overview of BenMAP Components
and then to choose the health impact functions to estimate the incidence of adverse health effects.
The third button gives you different options for combining the health effects estimates and
placing an economic value on them. Using the fourth button, you can generate several different
kinds of reports.
2.2.1 Create Air Quality Grids
BenMAP is not an air quality model, nor can it generate air quality data independently. Instead it
relies on the air quality inputs given to it. To estimate population exposure to air pollution,
BenMAP combines population data with an Air Quality Grid, which it generates using some
combination of air quality modeling and/or monitoring data. The Create Air Quality Grids
button allows you to put your air quality data into the format that is used by BenMAP, the first
step in conducting an analysis within BenMAP.
Grid Definitions
Air quality grids contain air pollution exposure estimates for a particular Grid Definition, as
defined in the Setup Manager window. Grid Definitions are typically comprised of either
regularly shaped rectangles covering the region of analysis, or irregularly shaped polygons
corresponding to political boundaries.
Modeling and Monitoring Data
To generate air quality grids, you can use air quality modeling data and air quality monitoring
data in three different ways, as discussed below. However, once generated, the air quality grids
all have the same structure, and have the same .aqg extension that BenMAP uses to designate
these file types.
^ Model Direct. Model Direct grid creation simply
takes raw model data and converts it into a file that
BenMAP recognizes as an air quality grid. This type
of grid definition allows you to directly specify the air
pollution values for each grid cell in a Grid
Definition.
^Monitor Direct. Monitor Direct grid creation uses
air pollution monitoring data to estimate air pollution
levels for each grid cell in the selected Grid
Definition. This may be done using one of two interpolation procedures - Closest Monitor, or
Voronoi Neighbor Averaging (VNA). With closest monitor, BenMAP simply uses the data of the
monitor closest to each grid cell's centroid. With VNA, BenMAP first identifies the set of
monitors that most closely "surround" each grid cell, and then calculates an inverse-distance
weighted average of the data from these neighboring monitors.
^Monitor and Model Relative. Monitor and Model Relative grid creation allows you to
combine information from both monitor and modeling data files. BenMAP uses the modeling
data to scale the monitoring data, to compensate for incomplete monitoring data coverage, and
the inability of monitor data to predict future conditions. Like the Monitor Direct approach, you
choose an interpolation method (Closest Monitor or VNA), but a scaling approach is also chosen,
v Air Quality Grid Creation Method

Choose Grid Creation Method
(* !Modei Direct;
C Monitor Direct
C Monitor and Model Relative
C Monitor Rollback
Cancel
Go!
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Chapter 2. Overview of BenMAP Components
either temporal, spatial, or both. This approach is described in more detail Chapter 4, as well as
in Appendix C.
^Monitor Rollback. Monitor Rollback grid creation allows you to reduce, or roll back, monitor
data using three methods: percentage rollback, incremental rollback, and rollback to a standard.
Percentage rollback reduces all monitor observations by the same percentage. Incremental
rollback reduces all observations by the same increment. Rollback to a standard reduces monitor
observations so that they just meet a specified standard. After the monitor data is rolled back, it
may be directly interpolated (as in Monitor Direct grid creation) or combined with modeling data
(as in Monitor and Model Relative grid creation). This approach is described in more detail in
Chapter 4, as well as in Appendix B.
2.2.2 Create and Run Configuration
The Create and Run Configuration button allows you to run calculate the change in the
incidence of adverse health effects associated with changes in air quality. There are several steps
in the process.
^ Step 1. Specify the baseline and control air quality grids that you created using the Create
Air Quality Grids button.
Configuration Settings
Cancel
Previous
"Select Air Quality Grids
Baseline File:
J C:\Program FilesSAbt Associates I nc\BenMAP 2.2\Air Quality Grids\PM 2.5 City One County Open | Create
Control File:
| C: \Program Files\Abt Associates I nc\B enMAP 2.2SAir Q uality G rids\PM 2.5 CityO ne County 0 pen | Create
Map Grids I
-Settings
Latin Hypercube Points:
Population DataSet:
I CityO ne T ract Population
Run In Point Mode:
Threshold:
Population Year:
^ Step 2. Specify whether BenMAP should make a "point estimate", or a set of "Latin
Hypercube" points. The point estimate is the change in incidence, generated using the mean
value of the coefficient in the health impact function. The Latin Hypercube points are a series of
points generated by using evenly spaced percentiles in the distribution of the health impact
coefficient - these points represent the distribution of incidence values. (The Latin Hypercube
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Chapter 2. Overview of BenMAP Components
points can later be used when combining the results of different health impact functions, and in
presenting confidence intervals for the incidence estimates.)
3"*" Step 3. Specify the threshold, or a lowest value for air quality data. Any observations which
fall below this threshold will be replaced with the threshold value in all calculations.
Step 4. Choose the health
impact functions that will be
used in the estimation, and
hit the Go! button to start
estimating the change in
incidence.
BenMAP can store
configuration choices in a
user-named file with a .cfg
extension, and can store
incidence change estimates in
a user-named file with a .cfgr
extension.
Configuration Settings
^ISix]
Available CR Functions:
Tree
Data
D ataS et J E ndpoint G roup | E ndpoint
Metric
| Seasonal Metric | Metric Statistic
EBj CityOne Health li|

-------
Chapter 2. Overview of BenMAP Components
Step 2. Choose the
desired pooling and
aggregation options for the
incidence results.
1
| v Incidence Pooling and Age
regation
^~jxj
"Available Incidence Results
Select Pooling Methods
0 Mortality
Pooling Window N ame: | Pooling Window 1
, Endpoint Group
Endpoint
Author Qualifier
L:
Pooling Method
Mortality
Mortality, Long-Term
Krewski et al.
ACS, Mean
e:

Chronic Bronchitis
Chronic Bronchitis
Abbey et al.

None

27-44
Sf

45-G4
Sf

S5+
SF

E
[--Window to Delete-- Delete | Add
Configuration R esults Filename: | C: \Program FilesSAbt Associates 1 nc\B enM AP 2.2\Configuration R esu B rowse |
Advanced | Cancel j Next
5^ Step 3. Choose the
economic valuation functions
to apply to the pooled and
aggregated incidence results.
5^ Step 4. Choose the
desired pooling and
aggregation options for the
economic valuations, and hit
the Go! button to start
generating results.
BenMAP can store APV
Configuration choices in a
user-named file with an
"apv" extension, and can
store APV Configuration results in a user-named file with an "apvr" extension. As needed, you
can access both files for later use.

-J.giiii I
| ^ Select Valuation Methods, Pooling, and Aggregation

Valuation Methods
Variable DataSet:

El- CityOne Valuation Functions
~ Chronic Bronchitis
El Chronic Bronchitis
'¦¦¦¦ WTP: average severity 130-99
El Mortality
[=]•• Mortality
I 5
j Pooling Window Name: |Pooling Window 1
Endpoint Group
Endpoint
Ai
I Valuation Method Pooling Method
Mortality

f 11 1

VSL,based on 26 v

Chronic Bronchitis
Chronic Bronchitis
Al

Sum (Dependent)

WTP: average seve

Advanced | Cancel j Previous | Run
2.2.4 Generate Reports
The Generate Reports
button allows you to generate
several types of reports.
^ Select Report Type
Jnjxj
r Incidence and Valuation Results: Raw.. Aggregated, arid Pooled. (Created from x.apvr files)
C Raw Incidence Results. (Created from x.cfgr files)
C Audit Trail Reports (Created from x.aqg files, x.cfg files, x.cfgr files, x.apv files, or x.apvr files)
Cancel
OK
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Chapter 2. Overview of BenMAP Components
S*8 Incidence and Valuation Results reports use an
Aggregation, Pooling, and Valuation Results file (with the
"apvr" extension) to create reports for incidence, aggregated
incidence, pooled incidence, valuation, aggregated valuation,
or pooled valuation results. These reports are comma
separated values (CSV) files (*.csv) which can be read using a
text editor, or by various spreadsheet and database programs,
such as Microsoft Excel.
S Choose a Result Type
Result Type-
JSlilJ
C Incidence Results
C Aggregated Incidence Results
C Pooled Incidence Results
r Valuation Results
C Aggregated Valuation Results
C Pooled Valuation Results
Cancel
OK
P* Raw Incidence
Results use a
Configuration
Results file (with the
'".cfgr" extension) to
create reports for
incidence results.
These reports are
CSV files.
APV Configuration Results Report
E
File
Column Selection
Grid Fields:
Pooled C-R Function Fields:
0 Column
0 Row

Result Fields:
0 Endpoint Group
~ Function
~ Endpoint
~ Pollutant
~ Author
L Metric
~ Qualifier
~ Seasonal Metric
~ Location
~ Metric Statistic
~ Start Age
~ DataSet
[ ] End Age
~ Version

¦ ~ Pooling Window
~ Other Pollutants

~ Reference

~ Race

~ Gender

0 Point Estimate
Q Mean
W Standard Deviation
~nsHi
0 Latin Hypercube Points
^Grouping Options
(• Group by Gridcell, then by Pooled C-R Function,
r Group by Pooled C-R Function, then by Gridcell.
Display Options-
Digits After Decimal Point: jo
Elements in Preview: j25
"If
Column Row ]Endpoint Group Point Estimate Standard Deviation Percentiles
Mortality
Chronic Bronchitis 69

9**1 Audit Trail
Reports provide a
summaries of the
assumptions
underlying each of
five types of files
generated by
BenMAP: Air
Quality Grid
C.aqg").
Configuration
O'.cfg").
Configuration
Results (".cfgf'),
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Chapter 2. Overview of BenMAP Components
Aggregation, Pooling, and Valuation (" apv"), and Aggregation, Pooling, and Valuation
Results (".apvr"). These reports can be viewed within BenMAP in an expandable tree structure,
or can be exported to tab-delimited text files.
OK
U: ''¦.Program Files'A-bt Associates inc'\B enM AP 2 ^Configuration R esults\0 ther\F'M 2.5 CityO ne County 2003 VNA Annual 5 td 12 -- Age:;: 30i
& Configuration Results: C:\Prograrn Files\Abt Associates IncSBenMAP 2.2\Configuration Results\PM2.5 CityOne County 2003 VNA Annuc
j- Latin Hypercube Points: 10
!-• Year: 2000
| j~- Threshold: 0
S- Grid Definition
j | !•••• Name: Counties
;•••• Columns: 42
j | j- Rows: 101
j-- Grid Type: Shapefile
: Shapefile Name: Counties CityOne
IB- Selected Studies
El - Baseline Air Quality Grid: C:\Program FilesSAbt Associates lnc\BenMAP 2.2\Air Quality Grids\PM2.5 CityOne County Baseline 2003 V
El Control Air Quality Grid: C:\Program FilesSAbt Associates lnc\BenMAP 2.2\Air Quality Grids\PM2.5 CityOne County 2003 VNA Annua
EJ- Advanced
& Incidence Pooling Windows
S Incidence Pooling Window 1
Mortality, Mortality, Long-Term, All Cause, Krewski et al., ACS, Mean, G3 cities, 30,99,2000, None,,,, -(EXP(-BetaxDELTAQ)-1):
El- Chronic Bronchitis, Chronic Bronchitis, Abbey et al. [Pooling Method: Sum (Dependent)]
& Valuation Pooling Windows
Valuation Pooling Window 1
: Mortality, CityOne Normal: []
Chronic Bronchitis, City One Analysis: []
£ Audit Trail Report
2.3 Menus
There are two menu options found at the top of the main window: Tools and Help. The Tools
menu provides access to data import and export functionality and mapping. The Help menu
provides access to information about BenMAP - the version, contact information, and a suggested
citation.
2.3.1 Tools
The Tools menu has several options: Database Export,
Database Import, GIS/Mapping, and Modify Setup.
5s-Database Export. This tool allows you to export all
or part of BenMAP's internal database to a file which
can later be used (on another computer, for example)
with the Database Import tool. Manually loading data
into BenMAP can be time and labor intensive, so this
tool can be quite useful in sharing data with other users
or computers.
l.-UJI.lJaU
Tools Help
GIS I Mapping ,5
Environmental Benefits Mapping and Analysis Program
Create Air Quality Grids
Active Setup:
| CityOne
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Chapter 2. Overview of BenMAP Components
^Database Import. This tool allows you to import data exported using the Database Export
tool.
y^GIS/Mapping. This tool allows you to generate a wide variety of maps, including maps of
monitor data, air quality grids, population data, incidence rates, and both incidence and valuation
results. In addition, you can export the maps you have generated and view them in a shapefile
viewer, such as ArcView. Note that BenMAP also has context specific mapping capabilities in
various parts of the application. Chapter 8 provides additional details on BenMAP's mapping
capabilities.
Modify Setup. This tool allows you to view, edit, add, and delete data from BenMAP's internal
database. It is discussed in more detail in Chapter 3.
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CHAPTER 3
In this chapter...
Work through the steps of a
simple health benefits analysis,
from creating air quality grids to
generating reports and maps.
See each step clearly explained
and illustrated.
BenMAP
Quick Start
Tutorial
Become familiarized with each
of BenMAP's four buttons.
Chapter Overview
Step 1. Start BenMAP 3-1
Step 2. Create Air Quality Grids 3-2
Step 3. Specify Configuration Settings 3-6
Step 4. Select Health Impact Functions 3-8
Step 5. Specify Aggregation, Pooling and Valuation 3-11
Step 6. Generate Reports 3-17
Step 7. View Your Reports 3-20
Step 8. Map Your Results 3-21
-------
3. Ben MAP Quick Start Tutorial
The best way to understand what BenMAP does is to start BenMAP and work through the following
simple tutorial. The tutorial is based on a hypothetical scenario where ambient PM25 concentrations are
reduced by 25 percent in 2003. The steps in this analysis are as follows:
Step 1. Start BenMAP
Step 2. Create Air Quality Grids for the Baseline & Control Scenario
Step 4. Specify Configuration Settings
Step 5. Select Concentration-Response Functions
Step 6. Specify Aggregation, Pooling and Valuation
Step 7. Generate Reports
Step 8. View Your Reports Outside of BenMAP
Step 9. Map Your Results
Each step is explained in detail below.
Step 1. Start BenMAP.
Double-click on the BenMAP icon on your desktop, and the following window will appear:
Environmental Benefits Mapping and Analysis Program
Create Air Quality Grids

Create and Run
Configuration

Aggregation, Pooling,
and Valuation

Create Reports
Active Setup:

CityOne
z!
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Chapter 3. BenMAP Quick Start Tutorial
Step 2. Create Air Quality Grids for the Baseline and Control Scenarios
Click on the Create Air Quality Grids button to begin inputting the air quality data needed by
BenMAP. This will open up the window where you will input the air quality data. In general,
you need two air quality grids to conduct a benefits analysis, one for a baseline scenario and one
for the policy you are evaluating (the control scenario). We will be creating our baseline and
control scenarios together, through the Monitor Rollback air quality grid creation method.
5 Air Quality Grid Creation Method

Choose Grid Creation Method
C Model Direct
C Monitor Direct
C Monitor and Model Relative
(• IMonitor Rollback]
Cancel
Go!
Select Monitor Rollback from the list and click on Go!
This will take you to the Monitor Rolback Settings: (1) Select Monitors screen where you will
enter the information about the air quality monitoring data you want to use.
Choose PM2.5 from the Pollutant drop-down menu. On the Library tab, choose CityOne
Monitors from the Monitor DataSet drop-down menu, and choose 2003 from the Monitor
Library Year drop-down list. Finally, in the Rollback Grid Type choose Metropolitan Area.
iJ Monitor Rollback Settings: (1) Select Monitors
^Jnj2<|
Pollutant:
IPM2.5
"3
Library | Database, Columns ] Database, Rows | Text File ]
Monitor DataSet:
|CityOne Monitors
Monitor Library Year:
~3
2003
~3
Rollback Grid Type:
| Metropolitan Area|
3
Cancel
Next
When your window looks like the window above, click Next.
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Chapter 3. Ben MAP Quick Start Tutorial
This will take you to the Monitor Rolback Settings: (2) Select Rollback Regions and Settings
window where you will choose the type of rollback for the City One metropolitan area.
Monitor Rollback Settings: (2) Select Rollback Regions and Settings

-Rollback Regions-
»KKKI
J
j Region | Delete Region j |- Region to Delete -- V Export After Rollback
Select All Deselect All
J
Click Add Region. This will bring up the Select Region Rollback Type window.
v Select Region Rollback Type
Rollback Type-

(* percentage Rollback!
C Incremental Rollback
Rollback to a Standard
Cancel
OK
In the Select Region Rollback Type window you may select from three rollback options. Select
the Percentage Rollback option as shown above. Click OK.
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£ Monitor Rollback Settings: (2) Select Rollback Regions and Settings
JOJxl
"Rollback Regions"

EH Region 1
"Rollback Parameters-
Percent: [2jf
Background: |0-00
Add Region | Delete Region j |- Region to Delete -- f~ Export After Rollback
Seiect All
Deselect All
Back | Next
In the box of Rollback Parameters for Region 1, type 25 in the Percent box. (This will reduce
each of the monitors in the CityOne area by 25 percent.) Then click on the Select All box. When
your window looks like the window above, click Next.
This will take you to the Monitor Rolback Settings: (3) Additional Grid Settings window, the
final step in creating rollback grids.
Choose the Voronoi Neighborhood Averaging interpolation method. Leave the scaling method
as None. From the Grid Type drop-down list choose County. Leave the box checked next to
Make Baseline Grid (in addition to Control Grid). This option will cause BenMAP to create a
baseline scenario air quality grid using the monitors selected in the previous step, but without
rolling their values back. BenMap will create a second grid with the rolled back monitors, which
will serve as our control scenario air quality grid.
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Chapter 3. Ben MAP Quick Start Tutorial
£ Monitor Rollback Settings: (3) Additional Grid Setti..

Select Interpolation Method
Select Scaling Method
C Closest Monitor
None
(* Voronoi Neighborhood Averaging
C Spatial Only
Grid Type:
Adjustment File:
Counties
"3
Browse
|7 Make Baseline Grid (in addition to Control Grid).
Advanced

Map

Back

Go!
When your window looks like the window above, click Go!.
BenMAP will now prompt you to save the baseline air quality grid. Make sure you are in the Air
Quality Grids subfolder in the BenMAP directory and then save the file as: PM2.5 CityOne
County Baseline 2003 VNA.aqg (you do not have to enter the ~\aqg" extension).
Isave the baseline Grid.
21*J
Save in:

|L~\I Air Quality Grids 4" ft] l-HIK

1 ft

«] PM2.5 CityOne County 25 Pet Rollback 2003 VNA.aqg

History
ei

1«]iPM2.5 CityOne County Baseiine 2003 VNA.aqg]

j WSJP
Desktop

I My Documents
m
My Computer

11
My Network P...

File name: J PM 2.5 City0 ne County B aseline 2003 VNA. aqg |
Save
S ave as type: 1 Air Q uality G rids (x. aqg) ^ 1
Cancel

BenMAP will now prompt you to save the control air quality grid. Again, make sure you are in
the Air Quality Grids subfolder in the BenMAP directory and then save the file as: PM2.5
CityOne County 25 Pet Rollback 2003 VNA.aqg (you do not have to enter the "aqg" extension).
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Chapter 3. Ben MAP Quick Start Tutorial
Save the rolled back Grid.
Savejn: | Air Qualitu Grids
"3
n*i
© o- HI'
My Documents
My Computer
My Network P...
File name: |2.5 City One County 25 Pot Rollback 2003 VN A y |
Save as type: jAir Quality Grids (x.aqg)
Save

BenMAP will now create baseline and control air quality grids that you can use in your benefits
analysis. When the progress bar is complete, BenMAP will return to the main BenMAP screen.
Step 3. Specify Configuration Settings
On the main BenMAP screen, click on the Create and Run Configuration button. In the
following box, select Create New Configuration and click Go!
1 v Configuration Creation Method
| -jpjxj

(• iCreate New Configurations
Go!

C Open Existing Configuration
Cancel

This will bring up the Configuration Settings form, where you will enter the basic information
about your analysis before selecting the health effects you wish to estimate.
In the Baseline File field, you can either enter the path for your baseline air quality grid, or click
Open. For this example, click Open and browse to the Air Quality Grids folder. Select PM2.5
CityOne County Baseline 2003 VNA and click Open.
Next, click on Open next to the Control File field and select PM2.5 CityOne County 25 Pet
Rollback 2003 VNA and click Open.
This specifies that you want to conduct a benefits analysis of the difference between the baseline
and control scenarios for which we created air quality grids in Step 2.
In the Settings section of this window, there are several fields which set the overall scope of the
analysis.
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In the Population DataSet field, select CityOne Tract Population from the drop down menu.
This tells BenMAP that you want your analysis to use tract-level population data from this dataset
when calculating health impacts.
In the Population Year field, enter 2000 or select 2000 from the drop down menu. This tells
BenMAP that you want your analysis to use 2000 populations when calculating health impacts.
In the Latin Hypercube Points field, enter 10 or select 10 from the drop down menu. This tells
BenMAP that you want to estimate the percentiles of the distribution of health endpoint incidence
using Latin Hypercube Sampling with 10 percentiles of the distribution, representing the 5th, 15th,
25th, and so on up to the 95th percentile.
Leave the Run in Point Mode box unchecked.
Leave the Threshold field blank. This tells BenMAP that you want to estimate benefits
associated with all changes in PM2.5, regardless of where those changes occur along the range of
PM2.5 concentrations. Selecting a non-zero threshold means that you would only want to
calculate benefits for changes occurring above the threshold.
v Configuration Settings
Select Air Quality Grids—
Baseline File:
lociates lnc\BenMAP 2.2V^ir Quality Grids\PM2.5 CityOne County Baseline 2003 VNA.aqg Open
Control File:
^jnjxj
Create
| lnc\BenMAP 2.2\Air Quality Grids\PM2.5 CityOne County 25 Pet Rollback 2003 VNA.aqg Open | Create
Map Grids
-Settings-
Latin Hypercube Points:
|l0
Population DataSet:
|CityOne Tract Population
Run In Point Mode:
i r*:
Threshold:
Population Year:
"T] 12000
3
Cancel
Previous
Next
When your window looks like the above, click Next.
This will bring up the next page of the Configuration Settings form, where you can select health
impact functions from a set of available health impact functions.
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Chapter 3. BenMAP Quick Start Tutorial
Configuration Settings
JSJxJ
Available CR Functions:
Tree
Data
DataSet
E ndpoint G roup | E ndpoint
Metric
S easonal M etric | M etric S tatistic
sjCityOne Health 1

-------
Chapter 3. Ben MAP Quick Start Tutorial
-ln| x|
Available CR Functions:
iTree
iData

|DataSet
Endpoint Group
Endpoint
Metric
Seasonal Metric
Metric Statistic

CityOne Health li

ife

Hospital Admissions.

—

Acute Bronchitis

D24HourMean
QuarterlyMean
Mean
hi!

"5
Acute Myocardial In

Cancel | Previous |
Run

For acute myocardial infarctions (AMI), drag the entire Endpoint Group titled Acute Myocardial
Infarction to the lower panel (do not drill down). This will include the full set of age-specific
health impact functions for AMI.
For asthma emergency room visits, also drag over the entire the Endpoint Group titled
Emergency Room Visits, Respiratory.
You should now have seven health impact functions listed in the lower panel: one acute
bronchitis function, five AMI functions, and one ER visit function.
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Chapter 3. Ben MAP Quick Start Tutorial
^ Configuration Settings
.-.Inl X|
Available CR Functions:
| T ree
|Data
3
| DataSet
Endpoint Group
Endpoint
Metric
Seasonal Metric
Meti

$¦

Hospital Admissions, Cardiovasci

Acute Bronchitis

fi-

Acute Myocardial Infarction

Acute Respiratory Symptoms

Chronic Bronchitis

fi
Emergency Room Visits, Respirail
1

FXl 1 f Un^nitail Po^nir ~Jrrn 1 1

1 ' """" °™"'" ' Jj

Selected CR Functions:
Function Ide
Function Parameters
-
DataSet
Race
Gender
Start Age
End Age
Incidence DataSet
Prevalence Data...
Variable DataSet

CityOne Hec

18
24
CityOne Incidence a

CityOne Hec

45
54
CityOne Incidence c

CityOne Hec

55
G4
CityOne Incidence c

CityOne Hec

65
99
CityOne Incidence c

CityOne Hec

25
44
CityOne Incidence c

CityOne Hec

0
17
CityOne Incidence c

~
<1 1

Cancel
Previous
Run
BenMAP will then prompt you to save your file. Click Save. Browse to the Configurations
subfolder within the BenMAP directory and save the file as:
PM25 Example Configiiration.cfg (you do not need to include the "Vcfg" extension).
When you have saved the configuration file, click OK to run the
BenMAP will prompt you to "Save Configuration Results to
File". Browse to the Configuration Results subfolder within
the BenMAP directory and save the file as: PM2.5 CityOne
County 25 Pet Rollback 2003 VNA Example, cfgr (you do not
need to include the ".cfgr" extension)
Once you have entered the filename, BenMAP will begin
calculating the change in incidence for the set of health
impact functions you have selected. The run may take a few
minutes to finish; a progress bar will let you know how it is proceeding. When BenMAP is
finished running your configuration, it will return to the main BenMAP screen.
configuration.
The first time you are prompted
to save is for your configuration
options and the health impact
functions you have chosen. The
second time you are prompted to
save is for your results- the grid-
cell level changes in incidence.
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Chapter 3. Ben MAP Quick Start Tutorial
Step 5. Specify Aggregation, Pooling and Valuation
This step allows you to take the incidence results that BenMAP just produced and place an
economic valuation on them. Although not covered in this tutorial* this is also where you can
select the geographic level of aggregation and combine individual incidence results into pooling
groups.
From the main screen, click on the Aggregation, Pooling and Valuation button. This will bring
up a menu screen with two choices: Create New Configuration for Aggregation, Pooling and
Valuation, or Open Existing Configuration for Aggregation, Pooling and Valuation (*.apv file).
£ APV Configuration Creation Method
|^|n]xj
C Create New Configuration tor Aggregation, Pooling, and Valuation.
C Open Existing Configuration file for Aggregation, Pooling, and Valuation (".apv file).
Cancel
Go!
Select Create New Configuration for Aggregation, Pooling and Valuation and click on Go!
BenMAP will prompt you to open a Configuration Results File. Browse to the Configuration
Results subfolder and select PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example, cfgr.
Then click on Open.
lopen

Look in:
Configuration Results 4" (^]

M
History
m
0 PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example.cfgr

u Efll
1 My Network P... 1
File name: | * |
~pen
Files of type: 1 Configuration Results (x.cfgr) T1
Cancel
*1
BenMAP will then open the Incidence Pooling and Aggregation window with the results from
running your configuration. You should see a window that looks like the following:
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Chapter 3. Ben MAP Quick Start Tutorial
| w Incidence Pooling and Aggregation
_|n|x|
Available Incidence Results-—
Select Pooling Methods
EE Acute Bronchitis
a Acute Myocardial Infarction
£]-• Emergency Room Visits, Re
<1 1 ~!
Pooling Window N ame: | Pooling Window 1
Endpoint Group I Endpoint DataSet Pollutanl
Ml Pooling Method

l<
~

| -- Window to D elete --
Delete | Add
Configuration Results Filename: |C:\Program Files\Abt Associates lnc\BenMAP 2.0\Configuration Resu
Browse

Advanced j
Cancel j Next i|

Click on each of the results groups (acute bronchitis, acute myocardial infarction, and emergency
room visits) and drag them to the right panel.
For this example, we are not pooling any of the incidence results (although we will pool
valuations in the next window), so just click on Next at the bottom of the window.
This will take you to the Select Valuation Methods, Pooling, and Aggregation window.
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Chapter 3. BenMAP Quick Start Tutorial
| v Select Valuation Methods, Pooling, and Aggregation
¦ ^JnJxj
Valuation Methods
Variable DataSet:

0 CityOne Valuation Functioi
1 dm
Pooling Window N arne: 1 Pooling Window 1
Endpoint Group
Endpoint | Author Qu^
I Valuation Method | Pooling Method
Acute Bronchitis

--Select--

Acute Myocardial In
Acute Myocardial Inl
Peters et al.

None

18-2
-Select--

45-E
--S elect-

55-E
--Select--

65+
--Select-

25-4
-Select--

Emergency Room V

--S elect-

<1 1 >1
F

1
Advanced |
Cancel | Previous
||j Run i|

A) Select a value for acute bronchitis
To select a valuation method for acute bronchitis, drill down the Acute Bronchitis valuation group
until you see individual valuation methods. Click on the WTP: 6 day illness, CVstudies \ 0-17
method and drag it onto the Acute Bronchitis endpoint group in the right hand panel. You should
see the method appear under acute bronchitis in the Valuation Method column in the right hand
panel.
B) Select values for acute myocardial infarctions (heart attacks)
To select valuation methods for acute myocardial infarctions, drill down the AMI valuation group
until you see a (long) list of individual valuation methods. You might find it easier to expand the
column width of the Valuation Methods column (drag the right hand edge of the column to the
right to make it wider). We will be working with the valuation estimates from two studies,
Wittels and Russell For each of the studies, there are a number of age specific valuations. There
are also two different discount rates (the discount rate is the rate at which future medical costs are
discounted to the present). Drag the age-specific valuation estimates from Wittels for the 3
percent discount rate (COI, 5 yrs med, 5 yrs wages, 3% DR, Wittels (1990) | age) to each
matching age-specific line in the right hand panel (Pooling Window 1). You may have to scroll
over in the right hand panel to see the Low Age column.
Note that you will need to drag some age-specific valuation estimates to multiple lines in the
pooling window, since there is not a perfect match between the available age-specific valuation
estimates and the age groups for which the incidence of heart attacks was estimated. For
example, you will have to drag the valuation estimate for the 25 to 44 age group to both the 25 to
35 age group and the 35 to 45 age group in the pooling window.
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Chapter 3. BenMAP Quick Start Tutorial
Now repeat this process using the Russell 3 percent discount rate valuation estimates. When you
are finished, you should have two valuation estimates for each AMI age group, and your pooling
window should look like the one below.
| £ Select Valuation Methods, Pooling, and Aggregation
-Inlxl
Valuation Methods
Variable DataSet:

E" CityOne Valuation Functions
B Acute Bronchitis
~ Acute Bronchitis
1 WTP: 6 day illne;
El Emergency Room Visits,
El Acute Myocardial Infarcti
El- Acute Myocardial Inf.
1 il
Pooling Window N ame: | Pooling Window 1
Endpoint Group
Endpoint
Author
Valuation Method
Pooling Method

Acute Bronchitis

WTP: G day illness,

; coi
!•••• COI
I" COI
COI
5 yrs med, E
5 yrs med, 5
5 yrs med, E
5 yrs med, E
Acute Myocardial In
Acute Myocardial Inl
Peters et al.

None

COI: 5 yrs med, 5 yrs

s yrs med, q

None
LUI
I"" COI
i COI
COI
L- COI
o yrs rned, -
5 yrs med, E
5 yrs med, 5
5 yrs rned, E
5 yrs med,!:

COI: 5 yrs med, 5 yrs

None

COI: 5 yrs med, 5 yrs

None

COI: 5 yrs med, 5 yrs

<\ 1 ~!
4

i :
Advanced |

Cancel | Previous | Run
J

Now you can pool the valuation results for heart attacks in each age group using the unit values
from both Wittels and Riisell. In order to do so, you must select a pooling method.
BenMAP lets you select from several different pooling methods. For this example, you will be
using subjective weights. In other applications, you may wish to use fixed or random effects
weights (see Chapter 6 for more information on pooling methods).
To set the pooling method for each age group result, click on the Pooling Method field in the
row ABOVE each pair of valuation methods (where it says None) and use the drop down menu to
select Subjective Weights. You must repeat this for EACH age group in order for pooling to take
place over all age groups.
In addition to pooling the results over the two valuation methods, we also need to aggregate the
results into a total estimate across age groups. In order to do so, in the row with Endpoint
Group (Endpoint Group = Acute Myocardial Infarction) click in the Pooling Method field and
select Sum (Dependent) from the drop down menu.
Your screen should look like the following:
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Chapter 3. BenMAP Quick Start Tutorial
1 £ Select Valuation Methods, Pooling, and Aggregation

Valuation Methods
Variable DataSet:

B CityOne Valuation Functions
& Acute Bronchitis
S Acute Bronchitis
'¦¦¦¦¦ WTP: G day illne:
EE Emergency Room Visits,
Acute Myocardial Infarcti
El Acute Myocardial Inf.
;•••• COI: 5 yrs med, E
I - COI: 5 yrs rned, E
;•••¦ COI: 5 yrs med.El
I— COI: 5 yrs med, E
I - COI: 5yrs rned, I
;•••• COI: 5 yrs med, E
j-COI: 5 yrs med, E
i - COI: 5yrs med, E
!•••¦ COI: 5 yrs med, E
=•••• COI: 5 yrs med, E
1 H|
Pooling Window N ame: | Pooling Window 1
Endpoint Group
Endpoint | Author
I Valuation M ethod | Pooling M ethod
r
Acute Bronchitis

WTP: 6 day illness.

Acute Myocardial In
Acute Myocardial Inl
Peters et al.

Sum (Dependent)

Subjective Weights

COI: 5 yrs med, 5 yrs

Subjective Weights

COI: 5 yrs med, 5yr?

COI: 5 yrs med, 5 yrs

Subjective Weights

COI: 5 yrs med, 5 yrs

COI: 5 yrs med, 5yrj

Subjective Weights

COI: 5 yrs med, 5 yrs

<1 1 >1
4

Advanced |

Cancel | Previous | Run
I

This pooling configuration for acute myocardial infarctions will assign a starting set of equal
weights to each valuation method for the set of five age groups, and then create an overall
estimate of acute myocardial infarctions by summing the age-specific pooled estimates, treating
the distributions for each age group as dependent (i.e. a draw from the 5th percentile of the 45 to
54 age group will be added to the draw from the 5th percentile of the 55 to 64 age group and so
on).
C) Select values for asthma emergency room visits
To select values for asthma ER visits, drill down the Emergency Room Visits, Respiratory
heading to the Emergency Room Visits, Asthma, and then to the valuation approach Standford et
al, 1999 | 0 - Max. Drag this to the Emergency Room Visits entry in the right hand panel.
D) Choose variable dataset
In the Variable Dataset drop-down menu, choose CityOne Variables. (BenMAP requires that
there be a variable dataset be chosen before going to the next step.
Your completed screen should look like the one below.
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Chapter 3. BenMAP Quick Start Tutorial
| v Select Valuation Methods, Pooling, and Aggregation

Valuation Methods
Variable DataSet:

CityOne Valuation Functions
~ Acute Bronchitis
| IE) Acute Bronchitis
"•¦¦WTP: G day illne:
El Emergency Room Visits,
j El- E mergency R oom Vi;
1COI: Standford e
EE Acute Myocardial Infarcti
|CityOne Variables
Pooling Window N ame: | Pooling Window 1
Endpoint Group
Endpoint
Author
Valuation Method
Pooling Method
Acute Bronchitis

WTP: G day illness.

Acute Myocardial In
Acute Myocardial Inl
Peters et al.

Sum (Dependent)

Subjective Weights

COI: 5yrs med, 5yrj

COI: 5yrs med, 5yrs

Subjective Weights

COI: 5yrs med, 5yr?

Subjective Weights

COI: 5yrs med, 5yr*

COI: 5yrs med, 5yrs

Subjective Weights

COI: 5yrs med, 5yrj

Subjective Weights

COI: 5yrs med, 5yrj

COI: 5yrs med, 5yr?

Emergency Room V

COI: Standford eta

<1 1 ~!

Advanced |

Cancel | Previous | Run

E) Entering subjective weights
Once you have completed this step, click on Run. BenMAP will now bring up a window to
allow you to enter subjective weights.
BenMAP assigns a default equal weight to each selected valuation method. You can change
these weights by clicking in the weight cells. However, for this exercise, you should leave them
at 0.5 for each study. Click on OK at the bottom of the screen. You should see a save dialog
box. Click on Save to save your APV configuration. Save the file as PM25 Direct example APV.
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Chapter 3. BenMAP Quick Start Tutorial
1« Select Subjective Weights
-Inl x||
Pooling Window Name:
sling Window 1
Endpoint Group
Endpoint | Author | Qualifier
Valuation Method | Pooling Method Weights
-
Acute Bronchitis

WTP: 6 day illness.

Acute Myocardial In
Acute Myocardial Inl
Peters et al.

Sum (Dependent)

18-24

Subjective Weights

CO I: 5 yrs med, 5 yrs

0.50

CO I: 5 yrs med, 5 yrs

0.50

45-54

Subjective Weights

CO I: 5 yrs med, 5 yrs

0.50

CO I: 5 yrs med, 5 yrs

0.50

55-64

Subjective Weights

CO I: 5 yrs med, 5 yrs

0.50

CO I: 5 yrs med, 5 yrs

0.50

65+

Subjective Weights

CO I: 5 yrs med, 5 yrs

0.50
-
«
~
Cancel OK
Click on OK to start the pooling and aggregation. First you will be prompted to enter a filename
for the aggregation, pooling, and valuation configuration file that you just created. Enter PM25
Example Configuration.apv and click Save.
Then you will be prompted to enter a filename for the aggregation, pooling, and valuation results.
Enter PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example.apvr and click Save. When
the progress bar disappears, you will be returned to the main BenMAP screen.
Step 6. Generate Reports
You may view your results within BenMAP, either in the preview window in the Create Reports
button or through the mapping functions. Alternatively, you may export the results to a comma
separated values file (*.csv), or a shape file (*.shp) which can be viewed in a GIS program such
as ESRI's ArcView product.
A) Generate a Pooled Incidence Results report.
A Pooled Incidence Results report contains the incidence results you previously generated, using
the aggregation level and pooling that you specified in the Incidence Pooling and Aggregation
window. Previously, in Step 5, we did not specify any pooling of incidence results (although
valuations were pooled), so in this case the Pooled Incidence Results report will look just like
the Aggregated Incidence Results report. If some incidence results had been pooled, the two
reports would be different.
Click on the Create Reports button from the main BenMAP screen. This will bring up the
Select Result Type window.
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Chapter 3. BenMAP Quick Start Tutorial
w Select Report Type
Jnjxj
(* [Incidence and Valuation Results: Flaw, Aggregated, and Pooled. (Created from x.apvr filesj
C Raw Incidence Results. (Created from x.cfgr files)
C Audit Trail Reports (Created from x.aqg files, x.cfg files, x.cfgr files, x.apv files, or x.apvr files)
Cancel
DK
Select Incidence and Valuation Results: Raw, Aggregated and Pooled. Click OK. This will bring
up a window where you can select a results file. Chose the PM2.5 CityOne County 25 Pet
Rollback 2003 VNA Example.apvr file, and click Open. This will bring up the Choose a Result
Type window.
S Choose a Result Type
Result Type

C Incidence Results
C Aggregated Incidence Results
(* Pooled Incidence Results
C Valuation Results
C Aggregated Valuation Results
C Pooled Valuation Results
Cancel OK
In the Choose a Result Type window, choose Pooled Incidence Results. Then click OK. This
will bring up the APV Configuration Results Report, where you can customize your report
display and select the fields you want to see in the report. In the Pooled C-R Function Fields
box, check off Endpoint Group and Qualifier.
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Chapter 3. BenMAP Quick Start Tutorial
^ APV Configuration Results Report
File
pColumn Selection-
^jnjxj
Grid Fields:
Pooled C-R Function Fields:
Result Fields:
0 Column
0 Row
0 Endpoint Group
D Function
~ Endpoint
~ Pollutant
~ Author
~ Metric
O Uualifier
~ Seasonal Metric
~ Location
~ Metric Statistic
D Start Age
~ DataSet
~ End Age
~ Version
~ Year
~ Pooling Window
~ Other Pollutants

~ Reference

~ Race

~ Gender

0 Point Estimate
0 Mean
0 Standard Deviation
0 Variance
0 Latin Hypercube Points
Add Sums
"Grouping Options
(* Group by Gridcell, then by Pooled C-R Function.
C Group by Pooled C-R Function, then by Gridcell.
"Display Options-
Digits After Decimal Point:
Elements in Preview: 25
"3
rPreview-
3
Column Row
En dp runt Group
Qualifier Point Estimate
Mean
Standard Deviation Variance
17
Acute Bronchitis
113.85
112.11
G4.14
4114.06
17
Acute Myocardial Infarction
18-24
0.04
0.04
0.01
0.00
17
Acute Myocardial Infarction
45-54
2.50
2.48
0.85
0.72

17
Acute Myocardial Infarction 55-64
I
5.94
5.89
2.02
4.06
Done
When your window looks like the window above, then go to the File menu and choose Save.
In the Save As window that appears, type in the file name and browse to the location where you
want to store the exported file. The Reports subfolder is a good location to keep exported
reports. Type in the name, PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example
Health.csv in the box and click Save. You can now open the report in another application, such
as a spreadsheet or database program.
B) Generate a Pooled Valuation Results report.
This report is similar to the Pooled Incidence Results
report, and uses the valuation pooling you previously
specified. Click on the Create Reports button from the
main BenMAP screen. This will bring up the Select
Report Type window. Select Incidence and Valuation
Results: Raw, Aggregated and Pooled. Click OK. This
will bring up a window where you can select a results
file. Chose the PM2.5 CityOne County 25 Pet Rollback
2003 VNA Example.apvr file, and click Open. This will
bring up the Choose a Result Type window.
Choose a Result Type
Result Type
-inlxl
C Incidence Results
C Aggregated Incidence Results
r Pooled Incidence Results
C Valuation Results
C Aggregated Valuation Results
(* Pooled Valuation ResuM
Cancel
OK
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Chapter 3. BenMAP Quick Start Tutorial
In the Choose a Result Type window, choose Pooled Valuation Results. Then click OK. This
will bring up the APV Configuration Results Report window, where you can customize your
report display and select the fields you want to see in the report. In the Pooled Valuation
Methods Fields box, check off Endpoint Group.
v APV Configuration Results Report
File
rColumn Selection—
^Jnjxj
Grid Fields:
Pooled Valuation Method Fields:
Result Fields:
0 Column
0 Row
0 Endpoint Group
~ Endpoint
~ Author
~i 3
~ ValuationMethod
~ Pooling Window
0 Point Estimate
0 Mean
0 Standard Deviation
0 Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
(* Group by Gridcell then by Pooled Valuation Method.
C Group by Pooled Valuation Method, then by Gridcell.
Display Options-
Digits After Decimal Point: 0
Elements in Preview: 25
"3
rreview
Column
Row
Endpoint Group
Point Estimate
Mean
Standard Deviation
Variance ±
42
17
Acute Bronchitis
40514
38882
28488
870167744 —'
42
17
Acute Myocardial Infarction
2105718
2088860
1328612
176521071820E
42
17
Emergency Room Visits, Respiratory
13487
13522
3324
11051578 j-J
<1 1 M
Done
When your window looks like the window above, then go to the File menu and choose Save.
In the Save As window that appears, type in the file name and browse to the location where you
want to store the exported file. The Reports subfolder is a good location to keep exported
reports. Type in the name, PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example
Valuation.csv in the box and click Save. You can now open the report in another application,
such as a spreadsheet or database program.
Step 7. View Your Reports
BenMAP generates comma separated values files (*.csv) that can be read by various spreadsheet
and database applications, such as Microsoft Excel.
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Chapter 3. Ben MAP Quick Start Tutorial
1 E3 Microsoft Excel - PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example Health.csv [ |

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Step 8. Map Your Results
You can also map any of the results that you have generated so far. This includes the air quality
grids, population data, incidence results, and valuation results. In this example, we will look at
the air quality grid for the base scenario, and view our incidence results. For more information on
these and other mapping functions, see Chapter 9.
To use the BenMAP mapping functionality, go to the Tools menu and choose GIS /Mapping.
The BenMAP GIS window will appear, with buttons at the top for managing files and navigating
the map.
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Chapter 3. BenMAP Quick Start Tutorial
| S BenMAP GI5
-ln|x|
^luLvl
• 1
0.|
ejQ|e|r?|©|
£ 1 | Geodetic
Layers

Close
To see the name of each button, simply hold the cursor over it. Click on the Open a file button,
and select Air Quality Grid from the drop-down menu. Browse to the file PM2.5 CityOne County
25 Pet Rollback 2003 VNA.aqg file and click Open.
The name of the file will appear in the left-hand panel under Layers. Double-click on the name
and a small box will appear with Display Options for viewing this layer.

-ln| x|

Variable: |

d
Start Color: | |
Min Value:
0

End Color: | |
Max Value:
0

Default Color:

Grid Outline: 17
Decimal Digits:
I2
±J

Cancel

Here you can select the variable contained in the layer (file) that you want to view. In the air
quality grid, the variables that are available are the Quarterly Mean and the Daily Mean
(D24HourMean). Select D24HonrMean for the annual mean of the Daily Mean in the Variable.
In this box, you can also change the colors in the map display, and the maximum and minimum
values displayed. You should see the map below.
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Chapter 3. Ben MAP Quick Start Tutorial
3 BenMflP GIS
- !~! x|
y ^ ®
—Layers-
PM2.5 City One Cour
11.09-11.20
11.20-11.31
11.31-11.42
11.42-11.53
11.53-11.64
11.64-11.76
11.76-11.87
11.87-11.98
11.98-12.09
12.09-12.20
© a
Q © O
Geodetic
"3
3
Close
To see tract outlines, select County from the Reference Layer drop-down menu at the top of the
screen. You may also use the other reference layers: Metropolitan Area and Tract. However,
since the results have been calculated at the county level in this example, the county reference is
generally most appropriate.
5 BenMflP GIS
-!~! x|
S ^ #
Layers
PM2.5 CityOne Cour
11.09-11.20
11.20
11.31
11.42
11.53
11.64
11176
11.87
11.98
12.09
11.31
11.42
11.53
11.64
11.76
11.87
11.98
12.09
12.20
©Jo,
q © ri o
| Geodetic
"3 I
3
Close
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Chapter 3. BenMAP Quick Start Tutorial
Now you can look at a geographical display of the incidence results you created for cases of
bronchitis, acute myocardial infarctions, and emergency room visits. Click on the Open a file
button at the top of the screen and select Al'V Configuration Results, then Incidence Results. In
the next window, select PM2.5 CityOne County 25 Pet Rollback 2003 VNA Example, apvr, then
click Open. BenMAP will load your incidence results and display them in a table. Because GIS
programs can typically only accommodate field names that are 10 characters or less, there is a
new column at the end of the table labeled Gis Field Name. Here you can name your variables,
as shown in the table below.
|Edit GIS Field Names
DataSet
Endpoint Group
Endpoint
Pollutant
Metric
Seasonal Metric
Gis Field Name
CityOne Health Impc
Acute Bronchitis
Acute Bronchitis
PM2.5
D24HourMean
QuarterlyMean
BrchSI 2
CityOne Health Impc
Acute Myocardial Inl
Acute Myocardial Inl
PM2.5
D24HourMean

AM 11824
CityOne Health Impc
Acute Myocardial Inl
Acute Myocardial Inl
PM2.5
D24HourMean

AM 14554
CityOne Health Impc
Acute Myocardial Inl
Acute Myocardial Inl
PM2.5
D24HourMean

AM 15564
CityOne Health Impc
Acute Myocardial Inl
Acute Myocardial Inl
PM2.5
D24HourMean

AMI65up
CityOne Health Impc
Acute Myocardial Inl
Acute Myocardial Inl
PM2.5
D24HourMean

AM 12544
CityOne Health Impc
Emergency Room Vi
Emergency Room Vi
PM2.5
D24HourMean

HI 1 M

OK
When you are satisfied with the variable names, click OK.
The new layer will show in the BenMAP GIS window on top
of the first layer. If the previous layer is still checked, then it
will appear, but underneath the new layer. Uncheck the box
next to the bottom (previous) layer to hide it. Your screen
should look like the one below.
You do not have to rename your
variables, but it will make it
easier to identify them when
selecting a variable to map. The
default names (ResultO, Resultl,
etc,) make it difficult to identify
individual results.
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Chapter 3. Ben MAP Quick Start Tutorial
S BenMflP GIS
- !~! x|
Layers-
y|^J •KKlg|e|y?|o| ^
PM2.5 CityOne Cour
17 PM2.5 City One Cour
11.09-11.20
11.20-11.31
11.31-11.42
11.42-11.53
11.53-11.64
11.64-11.76
111.76-11.87
111.87-11.98
111.98-12.09
112.09-12.20
Geodetic
"3
3
Close
Like the previous layer, double click on the name to bring up the Display Options box. Under
Variable you will see a list of the variable names you defined in the previous step. Select
AM/65iip. uncheck the Grid Outline box, and click OK. The viewer will now display the annual
increase in the number of acute myocardial infarctions for people ages 65 and up, as calculated
between the base and control scenarios. You can use the Display Options to select other
variables to view or change how the values are displayed.
S BenMflP GIS
-inl x|
& 0 ^ ®
"Layers-
f7 PM2.5 CityOne C
10.04-13.85
13.85-17.65
17.65-21.46
21.46-25.26
25.26-29.07
29.07-32.87
I 32.87-36.68
¦ 36.68-40.48
¦ 40.48-44.29
H 44.29-48.09
W PM2.5 CityOne C
11.09-11.20
11.20-11.31
11.31-11.42
11.42-11.53
11.53-11.64
11.64-11.76
11.76-11.87
®,Klq.|e|n|o| z I
Geodetic
3 \
d
/

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CHAPTER 4
In this chapter...
Create new data setups to load
and store your input data.
>• Edit and format each dataset.
Find details on the file structures
for data inputs.
Modify, delete, import and export
data setups.
Loading Data
Chapter Overview
4.1 Add and Modify Setups 4-1
4.1.1 Manage Grid Definitions 4-2
4.1.2 Manage Pollutants 4-5
4.1.3 Manage Monitor DataSets 4-12
4.1.4 Manage Incidence/Prevalence DataSets 4-18
4.1.5 Manage Population DataSets 4-21
4.1.6 Manage C-R Function DataSets 4-24
4.1.7 Manage Variable DataSets 4-32
4.1.8 Manage Inflation DataSets 4-35
4.1.9 Manage Valuation DataSets 4-37
4.2 Modify and Delete Setups 4-41
4.3 Export and Import Setups 4-42
4.3.1 Export Setups 4-42
4.3.2 Import Setups 4-43
-------
4. Loading Data
BenMAP encapsulates in a single dataset all of the data needed to run analyses for a particular
geographic area, such as a city, region, or nation. This dataset is called a "Setup," and consists of
grid definitions, pollutants, monitor data, incidence and prevalence rates, population data, health
impact functions, variables, inflation rates, and valuation functions. Grouping the data in this
way has a number of advantages. It makes it easier to organize and view the data, export the data
(either a whole setup or a portion of a setup), and import setups generated by others.
In this Chapter we discuss how to add, modify, and delete setups, as well as how to export and
import existing setups. There are a number of steps involved in loading the data, particularly in
the formatting of the data, so it is important to carefully review the steps in this Chapter. Also, it
is important to keep in mind that if you delete one part of a setup, you may be affecting other
parts of the setup. We discuss this further below.
4.1 Add and Modify Setups
To add a new setup or modify an existing
setup, go to the Tools drop-down menu, and
choose the Modify Setup option. This will
bring up the Manage Setups window.
To add a setup, click the Add button. The
New Setup window will appear, where you
can type the name of the setup.
£ New Setup
New Setup Name:
JSJx|
|CityOn
Cancel
OK
Available Setups:
I 3
Delete | Add
Grid Definitions

Pollutants

Monitor DataSets

Edit
Edit
Edit
Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets

Edit
Edit
Edit
Variable DataSets

Inflation DataSets

Valuation DataSets

Edit
Edit
Edit
Cancel OK
After naming a new setup, you can then define the elements that comprise a setup: Grid
Definitions, Pollutants, Monitor DataSets, Incidence and Prevalence DataSets, Population
DataSets, C-R Function DataSets, Variable DataSets, Inflation DataSets, and Valuation
DataSets.
Some of the elements of a setup are fundamental and should be entered before the others, namely
Grid Definitions and Pollutants. The Incidence and Prevalence, Population, and Variable
DataSets depend on the Grid Definitions, and the Monitor and C-R Function DataSets depend
on the Pollutants that you have defined. As a result, it is best to start by defining your Grid
Definitions and Pollutants, and then defining the other elements of the setup.
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Chapter 4. Loading Data
4.1.1 Manage Grid Definitions
A BenMAP Grid Definition provides a method of breaking a geographic region into areas of
interest (Grid Cells) in conducting an analysis. This can be done in two ways: by loading a
Shapefile (a particular type of Geographic Information System file) or by specifying a regularly
shaped grid pattern. These are referred to as Shapefile Grid Definitions and Regular Grid
Definitions, respectively. Typically a Shapefile Grid Definition is used when the areas of
interest are political boundaries with irregularly shaped borders, while a Regular Grid
Definition is used when the areas of interest are uniformly shaped rectangles.
Ideally, one Shapefile Grid Definition will be created using a shapefile containing an outline of
the area of interest (a city boundary, for example). Additional grid definitions will then be
created for each subdivision of that area for which (a) data is available (see the Air Modeling,
Population, Incidence and Prevalence, and Variables sections below), or (b) reports or maps are
desired.
For example, to conduct analyses for the United States, one might create the following grid
definitions:
^ United States Outline - this Shapefile Grid Definition would contain an outline of
the United States (probably just the lower forty eight states), defining the overall area of
interest.
^ County Outline - this Shapefile Grid Definition would contain county borders, for
use with county-based population and incidence rate data.
^ 36 Kilometer Grid - this Regular Grid Definition would contain rectangular grid
cells that are 36 kilometers on each side, for use with air quality modeling data.
^ State Outline - this Shapefile Grid Definition would contain state borders, for use in
generating reports and maps with results aggregated to the state level.
To start adding or modifying grid definitions, click on the Edit button below the Grid
Definitions box. The Manage Grid Definitions window will appear.
Manage Grid Definitions
Available Grid Definitions
Grid Type
Delete
Cancel
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Chapter 4. Loading Data
1 £ Grid Definition
-|n|x|
Grid ID:

| G ridD efinition 0

Shapefile Grid Regular Grid j

Columns: Rows:

1° n i° id

Minimum Longitude: Minimum Latitude:

1 0 1 0

Columns Per Longitude: Rows Per Latitude:

h n |i id

Preview |

Cancel | |i OK i|

Click on the Add button below the Available Grid Definitions box,. The Grid
Definition window will appear, with two tabs showing the two main types of grid
definitions that you may use in BenMAP: Shapefile Grid or Regular Grid.
Regular Grid
Regular Grid Definitions are defined by a lower left corner (specified as decimal degree latitude
and longitude, with West and South having negative values and East and North having positive
values), a total number of columns and rows, a number of columns per degree longitude, and a
number of rows per degree latitude. Individual cells within the resultant grid are numbered in
sequential order (columns from left to right, rows from bottom to top) starting at (1, 1). These
field values will be used to link the Regular Grid Definition with other sources of data, as
discussed in more detail below.
To define a Regular Grid, start by typing the name of the grid definition in the Grid ID box, and
then defining the number of Columns and Rows in the grid. To locate this grid geographically,
provide the decimal degree coordinates for the lower left-hand corner of the grid in the Minimum
Longitude and Minimum Latitude boxes.
To give the overall geographic size of the grid, provide the number of Columns Per Longitude
and Rows Per Latitude. For example, if there are 16 columns and 2 columns per degree
longitude, then the grid will span 8 degrees longitude. And if there are 25 rows and 4 rows per
degree latitude, then the grid will span 6.25 degrees of latitude.
Combining the numbers in this example, if the minimum longitude and latitude are -81 and 38
and the grid spans 8 degrees longitude and 6 degrees latitude, then the grid will run between -81
and -73 degrees longitude and between 38 and 44.25 degrees latitude.
After defining the grid, click the Preview button to see what the grid looks like. You may change
the parameters and click the Preview again to see how the grid changes. When you are satisfied
with the grid definition, click the OK button.
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Chapter 4. Loading Data
1 v Grid Definition
_jn|x||
Grid ID:

|Rectangular Grid

—
—
—
—

Shapefile Grid Regular Grid j

Columns: Rows:

|is %125 *A

Minimum Longitude: Minimum Latitude:
-811 | 38

Columns Per Longitude: Rows Per Latitude:

—
—
—
—

1 |2 A 1* A \

—
—
—
—
—
—
—
—
—
—
—
—

—
—

| Preview

—
—
—
—

1 1

Cancel OK
The name of your newly defined grid will then appear in the Manage Grid Definitions window.
You may click Edit to change the grid definition, Delete to permanently remove the grid that you
just defined, Add to define a new grid definition, or OK to get back to the Manage Setups
window.
Manage Grid Definitions
Available Grid Definitions
Rectangular Grid
Delete
Add
Edit

Cancel
OK

Shapefile Grid
Shapefiles used to create Shapefile Grid Definitions should be of the ESRI® Shapefile format.
Details on this format can be found at
http://downloads.esri.com/support/whitepapers/other_/shapefile.pdf. Shapefiles used must be
improjected - that is, they should store their geographic information as decimal degree latitude
and longitude values. Finally, any shapefiles used must contain integer fields named Column (or
Col) and Row, and each shape within the shapefile must contain a unique combination of values
for these two fields. These column and row values are used, just as the Column and Row field
values in Regular Grid Definitions, to link the Shapefile Grid Definition with other sources of
data, as discussed in more detail below.
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Chapter 4. Loading Data
To add a Shapefile Grid, click on the Add button in the Manage Grid Definitions window,
choose the Shapefile Grid tab, name the grid in the Grid ID, and browse for the correct shapefile
by clicking on the small open-file icon just to the right of the Load Shapefile box.
After locating the file in the Browse for a Shapefile window, click Open. This will choose the
file, and bring you back to the Grid Definition window. To view the shapefile, choose Preview.
r.
Grid Definition
jn]x]|
Grid ID:

|Metropolitan Region
Shapefile Grid | Regular Grid |
Load Shapefile:
lies CityOneSMetropolitan Area CityOne.shp" &|

I! ll

Cancel | OK
When you are satisfied that the shapefile looks correct, click OK. This will bring you back to
Manage Grid Definitions window. Note that the Grid Type box displays the type of grid for
each of your grid definitions.
Click OK when you are finished loading grid definitions. The Manage Setups screen will now
list the Grid Definitions that you have just created. At any time, you may click the Edit button
to add, modify, or delete grid definitions.
Note that if you delete a Grid Definition you will delete any data that is dependent on it, such as
any Incidence, Prevalence, Population, and Variable DataSets that use this particular Grid
Definition. As we discuss below each of these other elements of a setup, we will describe how
this might happen.
4.1.2 Manage Pollutants
The Pollutants section of a setup identifies the pollutants used in BenMAP and their associated
air quality metrics. Actual air pollution data is not entered here, and instead this section may be
best viewed as how you want to name your pollutants and define the measures or metrics used
with each pollutant. You many include any pollutant, though typically air pollutants such as
particulate matter, ozone, sulfur dioxide, and carbon monoxide are used in a BenMAP analysis.
A key concept for pollutants is the Metric. Air quality metrics are daily values calculated
directly from daily observations, or through various mathematical manipulations of hourly
observations. Typical ozone metrics include the highest hourly observations during the course of
each day, the average of all twenty four hourly observations, etc.
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Chapter 4. Loading Data
In general, air pollution data in BenMAP is hierarchical - a pollutant can have multiple Metrics,
each of which has multiple Statistics (these are automatically calculated by BenMAP) and which
can have multiple Seasonal Metrics. Similarly, Seasonal Metrics have multiple Statistics.
Furthermore, air pollution data can be provided to BenMAP at any of these levels, in addition to
the daily/hourly observation level, as described in more detail in Section 4.3.
To start adding pollutant definitions to
BenMAP, click on the Edit button below the
Pollutants box of the Manage Setups
window. The Manage Pollutants window
will appear. Here you may click Add to add
a new Pollutant, Delete to remove a
previously defined pollutant, or Edit to
modify an existing pollutant.
Add a Pollutant
Air pollution data in BenMAP is of two types - point source monitoring data, and Grid
Definition based modeling data. For both types, the data must be associated with a particular
pollutant. Exhibit 4-1 describes these variables used to define a pollutant in BenMAP.
Exhibit 4-1. BenMAP Pollutant Definitions
Pollutant Field Name Notes
Unique name for the pollutant which will be referenced in health impact functions, associated with
monitoring and modeling data, etc.
Pollutants may have hourly observations or daily observations. In the United States, Ozone has
hourly observations, while PM10 and PM2.5 have daily observations.
Daily values calculated directly from daily observations, or through various mathematical
manipulations of hourly observations. Typical ozone metrics include the highest hourly observations
during the course of each day, the mean of all twenty four hourly observations, etc.
Seasonal values calculated from metric values. In the United States, for example, quarterly means are
calculated for PM2.5 from daily means.
v Manage Pollutants
^JnJxJ
Available Pollutants
Delete
Add
Pollutant Metrics
Edit
Cancel OK
Pollutant ID
Observation Type
Metrics
Seasonal Metrics
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Chapter 4. Loading Data
To start defining a pollutant, click the
Add button and the Pollutant
Definition window will appear. In
the Pollutant ID box, you give a
unique name for the pollutant (e.g.,
PM10), and then define the
characteristics of this pollutant - the
Observation Type and Metrics.
The Observation Type identifies
whether a pollutant is measured
hourly or daily. In the United States,
ozone, sulfur dioxide, carbon
monoxide, and others have hourly
observations, while particulate matter
has daily observations. Use the
Observation Type drop-down menu
to choose either the Hourly or Daily
observation type.
1
(Pollutant Definition

Pollutant ID:

Hourly Metric Generation:

PM10
Fixed Window Moving Window ] Custom |
Observation Type:

Start Hour: |o

I Daily ~ |

End Hour: |23
2d
Metrics

D24HourMean
Statistic: iMean
H

Seasonal Metrics

Delete Add

j Advanced Pollutant Options i

Edit

Cancel
OK
Next you need to define a pollutant's Metrics. A pollutant has to have one or more metrics,
which are daily values calculated directly from daily observations, or through various
mathematical manipulations of hourly observations.
To add a Metric, click on the Add button below the Metrics box. A default name Metric 0 will
appear in the box. Since the default name is not very descriptive of a metric, it is best to change
the name. Typical names used for metrics given in Exhibit 4-2. These are provided just as an
example, and you may use any names that you like. However, keep in mind that the names that
you use for your metrics need to be consistent with the metric names that you include in your air
pollution monitoring and modeling data, as well as your health impact functions. (We will
discuss this further below.) Additionally, metric names are used to display pollutant
concentrations in BcnMAP's mapping window. As such, they must be consistent with GIS
naming conventions, meaning they must begin with a letter, and may only contain letters,
numbers, and underscores.
Exhibit 4-2. Examples of Metric Names
Name
Description
DIHourMax
Highest hourly value from 12:00 A.M. through 11:59 P.M.
D24HourMean
Average of hours from 12:00 A.M. through 11:59 P.M.
D8HourMax
Highest eight-hour average calculated between 12:00

A.M. and 11:59 P.M.
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Chapter 4. Loading Data
Hourly Metrics
Pollutants that are measured hourly
(Observation Type = Hourly), such
as ozone, sulfur dioxide, carbon
monoxide, and others, must be
characterized by a daily metric, which
mathematically summarizes the
hourly observations.
Exhibit 4-2 lists some of the ways that
metrics can be generated from hourly
values. Note that these metrics are
not arbitrarily chosen, and instead
match the metrics used in
epidemiological studies.
The Hourly Metric Generation
portion of the Pollutant Definition
window lets you define the metrics that you want to use. There are three tabs that you may
choose: Fixed Window, Moving Window, and Custom.
The Fixed Window tab lets you define simple metrics which are calculated as statistics
(Statistic) over a fixed window of hours (Start Hour and End Hour) within each day. The
Start Hour should be less than or equal to the End Hour, and both can range from 0 to 23,
where 0 stands for the period 12:00 am to 12:59 am, and 23 stands for 11:00 pm to 11:59 pm
The Statistic includes the Mean, Median, Max, Min, and Sum. Some examples follow:
D24HourMean: The mean of the
observations from 12:00 am
through 11:59 pm. Start Hour
= 0. End Hour = 23. Statistic
= Mean.
DIHourMax: The highest hourly value
of the observations from 12:00
am through 11:59 pm. Start
Hour = 0. End Hour = 23.
Statistic = Max.
D12HourMean: The mean of the
daylight observations, defined
as the period from 8:00 am
through 7:59 pm. Start Hour
= 8. End Hour =19. Statistic
= Mean.
Pollutant Definition
Pollutant ID:
| Ozone
Observation Type:
| Hourly
"3
Metrics
D24HourMean
DIHourMax
DSHourMax
Delete
Add
Advanced Pollutant Options
Hourly Metric Generation:
Fixed Window ] Moving Window | Custom]
Start Hour: [o~
End Hour: |23
Statistic: I
Median
Max
M
Sum
Seasona Metrics
Cancel
OK
Pollutant Definition
Pollutant ID:
|Ozone
Observation Type:
[Hourly

Metrics
D24HourMean
DIHourMax
D8HourMax
Delete
Add
Advanced Pollutant Options
Hourly Metric Generation:
Fixed Window Moving Window | Custom |
Window Size: 8
Window Statistic: [Me
"3
Daily Statistic:

Seasonal Metrics

Min

Sum

Cancel
Edit
OK
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Chapter 4. Loading Data
The Moving Window tab lets you consider metrics that are not based on the same set of hours
each day. The Window Size defines the number of hours that will be considered together. The
Window Statistic defines how the hours in the Window Size will be characterized. And the
Daily Statistic defines how BenMAP will use the statistics generated for each window.
For example, consider the highest eight-hour mean (D8HourMax) over the course of a day. You
would have the following settings: Window Size = 8. Window Statistic = Mean. Daily Statistic
= Max. BenMAP would calculate every possible eight-hour mean, starting with the eight-hour
mean from 12:00 am through 7:59 am, and ending with the eight-hour mean from 4:00 pm
through 11:59 pm. This would generate 17 possible eight-hour means. BenMAP would then
choose the eight-hour mean that has the highest value.
The Custom tab lets you define
Metrics using customized functions.
These functions can include measures
such as the sum of the number of
hours of ozone exposure about 60
parts per billion. The possibilities are
quite diverse, as evidenced by the
range of functions and variables
available for use in Exhibit 4-3.
However, the syntax for using these
functions is somewhat involved, so
we have reserved discussion of this
for Appendix A.
Pollutant Definition
Pollutant ID:
| Ozone
Observation Type:
I Hourly
Metrics
D24HourMean
DIHourMax
D8HourMax
SumOG
Delete
"3
Add
Advanced Pollutant Options
Hourly Metric Generation:
Fixed Window | Moving Window Custom |
Function:
Seasonal Metrics
Edit
Cancel
Edit
OK
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Chapter 4. Loading Data
Exhibit 4-3. Available Functions and Variables for Custom Metrics
Available
Functions
Description
Available Variables
Description
ABS(x)
Returns the absolute value of x.
Observations^]
All hourly observations for the year (index
begins at zero, typically ranging to 8,760).
EXP(x)
Returns e the power x, where e is the
base of the natural logarithm.
DailyObservations[i]
All hourly observations for the day (indexed
zero to twenty-three).
IPower(x,y)
Returns x to the power y (y an integer
value).
SortedObservations[i]
All hourly observations for the day, sorted
from low to high (indexed zero to twenty-
three).
LN(x)
Returns the natural logarithm of x.
Day
Index of the day whose metric value is being
generated (index begins at zero).
POWER(x,y)
Returns x to the power y (y a floating
point value).
Mean
Mean of the daily observations.
SQR(x)
Returns the square of x.
Median
Median of the daily observations.
SQRT(x)
Returns the positive square root of x.
Min
Max
Sum
NoObservation
Minimum of the daily observations.
Maximum of the daily observations.
Sum of the daily observations.
Flag value indicating a missing observation
(-345)
Seasonal Metrics
Seasonal Metrics allow users to aggregate daily Metric values. This has a number of uses. For
example, if pollutant values vary greatly by season, you can calculate separate pollutant measures
for each season of interest. You might be interested in dry season versus wet season, or
differences between Winter, Spring, Summer, and Fall.
To add seasonal metrics, click on the Edit button below the Seasonal Metrics box. The Manage
Seasonal Metrics window will appear.
To add a Seasonal Metric, click on the Add button below the Seasonal Metrics box. A default
name Seasonal Metric will appear in the box. Since the default name is not very descriptive of a
metric, it is best to change the name to something more informative such as the QuarterlyMean.
As with the Metric names, keep in mind that the Seasonal Metric names that you use need to be
consistent with the metric names that you include in your air pollution monitoring and modeling
data, as well as your health impact functions.
The next step is to define the seasons that you want associated with your Seasonal Metric name.
For example, in the case of a Quarterly Mean, you would want to define four seasons. To start
this process, click on the Add button below the Seasons box. Then, in the far right side of the
window under Selected Season Details, give the Start Date and End Date for each season.
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Chapter 4. Loading Data
S Manage Seasonal Metrics

Metric ID:
D24HourMean
Seasonal Metrics
QuarterlyMean
Delete
Add
Seasonal Metric Seasons:
Seasons
Season 1
Delete
Add
Selected Season Details:
Start Date:
January
01
- -1
End Date:
March
31

Statistic | Custom |
Statistic:
Mean
"31
Cancel
OK
Next, you need to choose the Statistic tab or the Custom tab to determine how the daily Metrics
will be combined in each season. For example, you might choose the Mean from the drop-down
list on the Statistics tab. This would calculate the mean of the daily metrics in each season. The
Custom tab allows seasonal metric values to be calculated using customized functions, similar to
those used to calculate daily metric values from hourly observations. Again, we have reserved
discussion of this topic for Appendix A.
Once you have finished defining the Seasonal Metrics, click OK to return to the Pollutant
Definition window.
Click OK after defining each Pollutant - this will return you to the Manage Pollutants window.
1 v Manage Pollutants

.JSl.
*1

Available Pollutants

Pollutant Metrics

PM2.5

D24HourMean

PM10

QuarterlyMean

Delete
Add

j Edit ]|

OK
Cancel
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Chapter 4. Loading Data
Advanced Pollutant Options
The Advanced Pollutant Options button allows you to associate Seasons with a Pollutant.
These seasons differ somewhat from the Seasonal Metrics discussed above. They are used to
define:
The portion of the year for which benefits are calculated for a Pollutant. For example, in the
United States ozone benefits are only calculated for the ozone season, which varies throughout
the country, but typically encompasses those months during which the average temperature is
above some threshold.
The portion(s) of the year for which missing pollutant concentrations are filled in by BenMAP.
That is, in order to calculate benefits, BenMAP in certain cases needs to generate complete sets of
metric values by estimating concentrations for those days which have missing observations. This
can be important if certain seasons tend to have more missing values than others.
The portion(s) of the year for which monitor data is scaled by modeling data when creating
Monitor Model Relative air quality grids (see section 5.3).
To define Seasons for a Pollutant, click the Advanced Pollutant Options button. This will
bring up the Define Seasons window. For each season desired, click the Add button, select the
appropriate Start Date and End Date, which define the days included in the season; the
appropriate Start Hour and End Hour, which define the hours included in Monitor Model
Relative scaling; and the appropriate Number of Bins, which define the number of bins used in
Monitor Model Relative scaling.
Once you have finished defining the Seasons, click OK to return to the Pollutant Definition
window.
If you later wish to View or Edit a particular Pollutant definition, simply select the appropriate
Pollutant within the Available Pollutants box and click the Edit button. When you are done,
click OK to return to the Manage Setups window.
After defining all of the pollutants that you
want to consider, click OK. This will return
you to the Manage Setups window.
4.1.3 Manage Monitor DataSets
The Monitor DataSets section of the Manage
Setups window allows to you to add air
pollution monitoring data to your setup. Air
pollution monitoring data may be used alone,
or in conjunction with air pollution modeling
data, to estimate ambient pollution levels in
each grid cell defined by a Grid Definition.
BenMAP uses a variety of procedures (such as
I .Jfll *l
Available Setups:
| Philadelphia j»jj|
Delete I Add
| Grid Definitions

Pollutants

Monitor DataSets
i Metropolitan Area

PM2.5
PM10
Ozone

Edit
Edit
Edit
Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets

Edit
Edit
Edit
Variable DataSets

Inflation DataSets

Valuation DataSets

Edit
Edit

Edit
Cancel | OK
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Chapter 4. Loading Data
Voronoi Neighbor Averaging, discussed later) to interpolate the monitor data points across the
area of interest.
NOTE: Air pollution data in BenMAP is of two types - point source monitoring data, and Grid
Definition based modeling data. Both types of data must be associated with a particular pollutant
that you have defined. Only the point source monitoring data is stored in the setup database. The
modeling data are loaded into BenMAP as you need them for a particular analysis.
To start, click on the Edit button
below the Monitor DataSets box.
The Manage Monitor DataSets
window will appear. From this
window you may Add monitoring
data, view and Edit existing datasets,
as well as Delete them. The section on
the left under Available DataSets
lists the monitor datasets that are
currently in the setup. The section on
the right under the DataSet Contents
identifies the number of monitors in
each dataset by pollutant and by year.
To start adding data, click the Add button. This will bring up the Monitor DataSet Definition
window. Give the dataset a name in the DataSet Name box, choose the appropriate pollutant
from the Pollutant drop-down menu, and then type the year of the data in the Year box.
NOTE: The DataSet that you define can have one or more pollutants and multiple years of data.
If your data come from a single source, for simplicity it probably makes sense to combine these
data into a single DataSet.
^ Manage Monitor DataSets
Available DataSets
Delete
Add
JflliSj
DataSet Contents (Number of Monitors by Pollutant by Year):
Edit
^ Monitor DataSet Definition
DataSet Name:
|MonitorDataSet 0
DataSet Contents (Number of Monitors by Pollutant by Year):
^jnjxj

Pollutant:
Year:
i 3 r
Database, Columns | Database, Rows ] Text File ]
Monitor Data File:
2000
Browse
Monitor Definition File:
Browse
Load Monitor Data

Cancel
OK

Now choose one of the three tabs (Database, Columns; Database, Rows; or Text File) that
matches the format of your data. Monitor data may be formatted in three different ways: (1) in
two database files/tables, with monitor definition information in rows in one file/table and
monitor values in a single column in the other file/table (Database, Columns format), (2) in a
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Chapter 4. Loading Data
database file, with monitor definition information and monitor values in a single line (Database,
Rows format), and (3) in a text file, with monitor definition information and monitor values in a
single line (Text File format). There is no preferred approach, and instead use the approach with
which you are most comfortable. (In the next sub-section we define these formats in detail.)
Click on the Browse button next to the Monitor Data File box. This will bring up a window
from which you can browse the BenMAP Data directory to find the desired data file. The Files
of type drop-down menu allows you to choose from a variety of file types.
Click Open. This will choose the file, and bring you back to the Monitor DataSet Definition
window. If you are using the Database, Columns format, you will need to repeat this procedure
with the Monitor Definition File. To add monitors from the selected file(s), click Load
Monitor Data.
Follow this same procedure to load all of your monitoring data. Select the pollutant from the
Pollutant drop-down menu, edit the Year box, choose the tab that matches the database format
that you are using, and select the appropriate file(s) by clicking the Browse button(s). When you
have clicked Load Monitor Data for your final dataset, click OK. This will bring you back to
the Manage Monitor DataSets window.
« Manage Monitor DataSets
Available DataSets
Philadelphia Ozone
Delete
Add
^Jnjxj
DataSet Contents (Number of Monitors by Pollutant by Year):
Pollutant
~ zone
~ zone
Ozone
5
a
Edit

Cancel
OK

To see the years of data and the number of monitors each year, use the scrollbars on the bottom
and on the right of the DataSet Contents box.
If desired you can add additional monitor datasets (click the Add button), delete existing datasets
(select the dataset in the Available DataSets list and click the Delete button), or edit existing
datasets (select the dataset in the Available DataSets list and click the Edit button).
When you have finished loading your monitor data, click OK in the Manage Monitor DataSets
window. This will take you back to the Manage Setups window, which will show the name of
the DataSet(s) that you just entered.
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Chapter 4. Loading Data
Format of Monitor DataSets
Monitor data may be formatted in three different ways. There is no preferred option. Simply use
whichever method seems most appropriate.
Database, Columns format. Two database files/tables, with monitor definition
information in rows in one file/table and monitor values in a single column in the other
file/table.
Database, Rows format. One database file, with monitor definition information and
monitor values in a single line.
Text File format. One text file, with monitor definition information and monitor values
in a single line.
Exhibits 4-4a through 4-4d present the variables in each of the datasets when using the columns
format and a sample of what the files might look like. Exhibits 4-5a and 4-5b present the
variables in the dataset when using the rows format and a sample of what a data file might look
like.
NOTE: The monitor data files do not specify the pollutant with which the data is associated - this
is specified by the user when loading the monitor data into BenMAP.
Exhibit 4-4a. Air Monitor Definition Variables - Column Format
Variable
Type
Required
Notes
Monitor Name
text
yes
Unique name for the monitor.
Description
text
no
Description of the Monitor.
Longitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the eastern hemisphere are
positive, and those in the western hemisphere are negative.
Latitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the northern hemisphere are
positive, and those in the southern hemisphere are negative.
Exhibit 4-4b. Sample Air Monitor Definition File - Column Format
Monitor Name
Description
Latitude
Longitude
10030010881011
Urban center near park
28.3501
77.1200
10270001881011
Urban center near highway
28.4502
77.3113
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Exhibit 4-4c. Air Monitoring Data File Variables - Column Format
Variable
Type
Required
Notes
Metric
text
no
This variable is either blank (signifying that the Values are Observations, rather than
Metric values), or must reference an already defined Metric for the appropriate
Pollutant.
Seasonal Metric
text
no
This variable is either blank (signifying that the Values are not Seasonal Metric values)
or must reference an already defined Seasonal Metric for the Metric.
Annual Metric
text
no
This variable is either blank (signifying that the values are not annual statistics) or must
be one of: None, Mean, Median, Max, Mn, Sum.
Monitor
Name(s)
numeric
(double)
yes
Each of these field names should match up with a value in the Monitor Name field in
the Definitions file. The values will be observations, metric, seasonal metric, or annual
metric values, as determined by the values of the other fields.
Exhibit 4-4d. Sample Air Monitoring Data File - Column Format
Metric
Seasonal Metric
Annual Metric
10030010SS1011
10270001SS1011
D24HourMean
QuarterlyMean

11.82
12.31
D24HourMean
QuarterlyMean

13.24
13.89
D24HourMean
QuarterlyMean

IS.79
18.27
D24HourMean
QuarterlyMean

14.25
21.19
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Exhibit 4-5a. Air Monitoring Data File Variables - Row Format
Variable
Type
Required
Notes
Monitor Name
text
yes
Unique name for each monitor in a particular location.
Description
text
no
Description of the Monitor.
Longitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the eastern hemisphere are
positive, and those in the western hemisphere are negative.
Latitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the northern hemisphere are
positive, and those in the southern hemisphere are negative.
Metric
text
no
This variable is either blank (signifying that the Values are Observations, rather than
Metric values), or must reference an already defined Metric (e.g., 1-hour daily
maximum) for the appropriate Pollutant.
Seasonal Metric
text
no
This variable is either blank (signifying that the Values are not Seasonal Metric
values) or must reference an already defined Seasonal Metric for the Metric (e.g.,
mean of the 1-hour maximum for the months of June through August).
Annual Metric
text
no
This variable is either blank (signifying that the values are not annual statistics) or
must be one of: None, Mean, Median, Max, Mn, Sum. (e.g., mean of the 1-hour
maximum for the year)
Values
comma
separated
values (text)
yes
If Metric is blank, either 365 or 8,760 comma separated observations (depending on
whether the Pollutant has daily or hourly observations). If Metric is defined, but
Seasonal Metric and Annual Metric are blank, 365 metric values. If Seasonal Metric
is defined, but Annual Metric is blank, n seasonal metric values. If Statistic is
defined, one annual statistic value (for either the Metric (if Seasonal Metric is blank)
or the Seasonal Metric. Mssing values are signified with a period ('.').
Exhibit 4-5b. Sample Air Monitoring Data File - Row Format
Monitor Name
Description
Latitude
Longitude
Metric
Seasonal Metric
Annual Metric
Values
10030010881011
Urban center near park
28.3501
77.1200

.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,5.90,.,.,11,.,.
10270001881011
Urban center near highway
28.4502
77.3113

19.80,.,.,3.5,.„, 14.70,.4.9C
10331002881011
Northern suburb
28.3524
77.6579

11.5, .,.,1.5, .,.,10.20,.,. ,2.5,.,. ,2. £
10491003881011
Eastern suburb near highway
28.5432
76.4544

12.60,.,.,4.60 7.30,.9.2[
10530002881011

28.3511
77.7762
D24HourMean

.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,5.90,.,.,11,.,.
10550010881011
Industrial area southwest of city
28.9054
77.9241
D24HourMean

19.80,.,.,3.5,.„, 14.70,.4.9C
10690002881011

28.1123
77.3425
D24HourMean

11.5, .,.,1.5, .,.,10.20,.,. ,2.5,.,. ,2. £
10730023881011

28.5834
78.0121
D24HourMean

12.60,.,.,4.60 7.30,.9.2[
10731005881011

28.2351
78.4237
D24HourMean
QuarterlyMean

11.82,13.24,18.79,14.25
10732003881011

28.1598
77.9453
D24HourMean
QuarterlyMean

12.31,13.89,18.27,21.19
10732006881011

28.4286
78.0042
D24HourMean
QuarterlyMean

12.33,15.68,18.49,15.96
10735002881011

28.3469
76.8953
D24HourMean
QuarterlyMean

13.52,13.69,19.04,22.43
10890014881011
Rural area west of city
28.3326
77.4683
D24HourMean

Mean
14.52
10970002881011
Rural area near large farms
28.9985
77.8531
D24HourMean

Mean
16.41
10972005881011
Rural area near national park
28.0014
78.9435
D24HourMean

Mean
15.62
11010007881011

28.1318
77.6289
D24HourMean

Mean
17.17
11130001881011

28.1689
77.4689
D24HourMean
QuarterlyMean
Mean
14.29
11170006881011

27.9845
77.8492
D24HourMean
QuarterlyMean
Mean
18.97
11190002881011

27.8934
77.1142
D24HourMean
QuarterlyMean
Mean
20.14
11210002881011

27.9923
76.8994
D24HourMean
QuarterlyMean
Mean
16.46
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Chapter 4. Loading Data
4.1.4 Manage Incidence/Prevalence DataSets
Most health impact functions, such as those
developed from log-linear or logistic health
impact functions, estimate the percent change
in a health effect associated with a pollutant
change. In order to estimate the absolute
change in incidence using these functions, the
baseline incidence rate (and in some cases the
prevalence rate) of the adverse health effect
are needed.
The incidence rate is the number of health
effects per person in the population per unit
of time, and the prevalence rate is the
percentage of people that suffer from a
particular chronic illness. For example, the
incidence rate for asthma attacks may be 25 cases per asthmatic individual per year, and the
prevalence rate (measuring the percentage of the population that is asthmatic) might be six
percent.
Available Setups:
| Philadelphia
Delete | Add
Grid Definitions

Pollutants

Monitor DataSets
County
Metropolitan Area
Tract

Ozone
PM10
PM2.5

Philadelphia Ozone
Philadelphia PM2.5
Edit
Edit
| Edit
Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets

Edit
Edit
Edit
Variable DataSets

Inflation DataSets

Valuation DataSets

Edit
Edit
Edit
Cancel OK
NOTE: For both incidence and prevalence rates, BenMAP allows the user to have rates that vary
by race, gender, and age group. BenMAP can support multiple sets of incidence and prevalence
rates, if the rates differ by year or by grid definition.
To start adding incidence and
prevalence data files, click on
the Edit button below the
Incidence/Prevalence DataSets
box. The Manage Incidence
DataSets window will appear.
In this window you may Add,
Edit, and Delete datasets. The
section on the left under
Available DataSets lists the
incidence/prevalence datasets
that are currently in the setup.
The section on the right under
the DataSet Incidence Rates
identifies the rates in the selected
dataset.
v Manage Incidence DataSets

Available DataSets
Delete
Add
DataSet Incidence Rates:
Endpoint Group Endpoint
Type

Edit

Cancel
OK

To add a dataset, click the Add button. This will bring up the Incidence DataSet Definition.
Give a name to the dataset that you are creating by typing a name in DataSet Name box.
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Chapter 4. Loading Data
£ Incidence DataSet Definition
DataSet Name:
IPhiladelphia County Incidence
DataSet Incidence Rates:
Endpoint Group Endpoint
Type

Load From Database
Delete
Grid Definition:
jnjxj
~~3
Cancel
OK
NOTE: If you have multiple incidence/prevalence datasets that van by, say, year and grid
definition, then use the name to provide a reference to the year and grid definition.
Click the Load From Database button. In
the Grid Definition drop-down list choose
the grid definition that matches the grid
definition used to develop the
incidence/prevalence dataset. Then click on
the Browse button, to browse for the dataset
file. (The format for the dataset is detailed in
the next sub-section.)
1 2 Load Variable Database
.jnlsll
| Grid Definition:
| County
d
| Database:
|ta\Philadelphia\Philadelphia County Incidence.csv)
Browse

Cancel
OK
1 1
After finding the file, click OK. The
Incidence DataSet Definition window will appear, displaying the rates in the data file that you
just loaded.
£ Incidence DataSet Definition
DataSet Name:
IPhiladelphia County Incidence
DataSet Incidence Rates:
Acute Myocardial Inf Acute Myocardial Inf Inc
Acute Myocardial Inf Acute Myocardial Inf Inc
Acute Myocardial Inf Acute Myocardial Inf Inc
Acute Myocardial Inf Acute Myocardial Inf Inc
Acute Myocardial Inf Acute Myocardial Inf Inc
Acute Myocardial Inf Acute Myocardial Inf IndV
~
-
Load From Database
Delete
Grid Definition:
II County
Cancel

"31

Column
Row
Value

42
17
0.043

42
29
0.043

42
45
0.043

42
91
0.043

*
42
101
0.043

d
OK
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Chapter 4. Loading Data
If the data look correct, click OK. This will return you to the Manage Incidence DataSets
window.
^ Manage Incidence DataSets
JflJx]
Available DataSets
Philadelphia County Incidence
DataSet Incidence Rates:
Acute Bronchitis Acute Bronchitis
Endpoint Group | Endpoint

In-
Acute Myocardial
Acute Myocardial
Acute Myocardial
Acute Myocardial
Acute Myocardial
Acute Myocardial
Inf Acute Myocardial Inf Inc
Inf Acute Myocardial Inf Inc
Inf Acute Myocardial Inf Inc
Inf Acute Myocardial Inf Inc
Inf Acute Myocardial Inf Inc
Inf Acute Myocardial Inf IndV
rif Inc T
Follow the same procedure for any additional incidence/prevalence datasets that you want to add
to the setup database. When you have finished adding data, click OK in the Manage Incidence
DataSets window. The Incidence/Prevalence DataSets box in the Manage Setups window will
show the datasets that you have entered.
Format of Monitor DataSets
Exhibit 4-6a presents the format that you should use for incidence and prevalence datasets, and
Exhibit 4-6b presents a sample datafile that follows this format.
Exhibit 4-6a. Health Incidence and Prevalence Data File Variables
Field Name:
Type:
Required:
Notes:
Endpoint Group
text
yes
If this doesn't reference an already defined Endpoint Group, one will be added.
Endpoint
text
yes
If this doesn't reference an already defined Endpoint for the Endpoint Group,
one will be added.
Race
text
no
Should either be blank (signifying All Races) or reference a defined Race
(from one or more Population Configurations).
Gender
text
no
Should either be blank (signifying All Genders) or reference a defined Gender
(from one or more Population Configurations).
Start Age
End Age
integer
integer
yes
yes
Specifies the low and high ages, inclusive. For example. Start Age of "0" and
End Age of "1" includes infants through the first 12 months of life and all one-
year old infants.
Column
Row
integer
integer
yes
yes
The column and the row link the incidence/prevalence data with cells from a
Grid Definition.
Value
numeric (double)
yes
The incidence/prevalence rate for the specified demographic group for this
location.
Type
text
no
If value is a prevalence rate, then "Prevalence" should be specified. Otherwise
BenMAP assumes that the value is an incidence rate.
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Exhibit 4-6b. Sample Health Incidence and Prevalence Data File
Endpoint Group

Endpoint
Race
Gender
Start Age End Age
Column
Row
Type
Value
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
1
1

8.7498E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
1
1

5.7498E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
1
3

8.7498E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
1
3

5.7498E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
1
5

8.7498E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
1
5

5.7498E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
2
15

8.1524E-D7
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
2
15

5.1279E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
2
17

8.1524E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
2
17

5.1279E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
2
19

8.1524E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
2
19

5.1279E-D7
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
2
21

8.1524E-D7
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
2
21

5.1279E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
5
1

9.1498E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
5
1

G.4128E-07
Hospital Admissions, Resp
ratory
HA, Asthma

0
17
5
3

9.1498E-07
Hospital Admissions, Resp
ratory
HA, Chronic Bronchitis

0
17
5
3

6.4128E-D7
4.1.5 Manage Population DataSets
The population data is used to estimate population exposure and in turn any adverse health effects
associated with a change in air pollution. BenMAP allows the user to specify race, gender, and
age of the population, as well as the year of the population estimate.
Population data loaded into BenMAP must be associated with a Population Configuration,
which defines the races, genders, and age ranges present in the data. Races and Genders are
unique text values representing population subgroups. Age ranges are defined by integer values
for starting age and ending age (inclusive), and a unique text value representing the name of the
age range. For example, "OTO1" might be used as a name for the age range defined by a start age
of zero and an end age of one, thus consisting of infants through the first twelve months of life
and all one-year old infants. The population
data provided to BenMAP should then contain
population values for all combinations of race,
gender, and age range. The population values
may be real numbers (e.g., non-integer values).
Population data must also be associated with a
Grid Definition which specifies the geographic
areas for which the data is available (see above
for more details on Grid Definitions). If
population data is available for multiple grid
definitions (cities and neighborhoods, for
example), users can have the option of using
different sets of population data for different
analyses. BenMAP can also estimate
populations for Grid Definitions for which no
.JSIJSI
Available Setups:
[Philadelphia jj]
Delete I Add
Grid Definitions

Pollutants

Monitor DataSets
County
Metropolitan Area
Tract

Ozone
PM10
PM2.5

Philadelphia Ozone
Philadelphia PM10
Philadelphia PM2.5
Edit
Edit
Edit

Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets
Philadelphia County Incidence
Philadelphia County Prevalence

|i Edit
Edit
Edit

Variable DataSets

Inflation DataSets

Valuation DataSets

Edit
Edit
Edit
Cancel OK
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Chapter 4. Loading Data
population data is available by calculating spatial overlap percentages with Grid Definitions for
which data is available.
To add population data to BenMAP, click on the Edit button below the Population DataSets box
in the Manage Setups window. The Manage Population DataSets window will appear.
Click on the Add button.
This brings up the Load Population DataSet
window, which has a number of important features.
The Population DataSet Name box allows you to
name the dataset.
The Population Configuration Definition window
allows you to define the variables that are in the
population data file to be loaded into BenMAP. Use
the drop-down list to choose an existing population
configuration and then view it by clicking the View
button, or you may click the Add button and define a
new population configuration.
The Grid Definition drop-down list provides the list of existing grid definitions, and you need to
choose the grid definition that matches that in the population dataset. The Browse button to the
right of the Database box allows to find the datafile that you want to load into BenMAP.

.jnjx]

Population DataSet Name:

[Philadelphia Tract
Population Configuration:
1 d

Add
View
Grid Definition:

d
Database:

Browse

Cancel
OK

Defining a Population Configuration
The Population Configuration defines the age range, race, and gender of the variables in your
population database. It is critical that the definitions in the population configuration match those
used in the development of the
database. If they do not match
exactly, BenMAP will fail to
load the population data.
To add a population
configuration, click the Add
button in the Load Population
DataSet window.
Under the Races box click on
New and type in the name for
any races present in your
population data. The names
appear in both the Races list box
and the Available Races list
box. (If you later create
alternative population
configurations, you can simply
Population Configuration Definition
Population Configuration Name:
JUS Census
Races
BLACK
WHITE
ASIAN
NATAMER
OTHER
Remove
Genders
MALE
FEMALE
Remove

New
Available Races
BLACK
WHITE
ASIAN
NATAMER
OTHER
New
Available Genders
MALE
FEMALE
Age Ranges

Low Age
(High Age

60to64

GO |
64

65to69

651
69

70to74

701
74

75to79

751
79

80to84

801
84

85up

851
99

~
Delete
Cancel
Add
OK
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Chapter 4. Loading Data
drag the relevant names from the Available Races list box into the Races list box.) Similarly,
under the Genders list box click on New and type in the name for any gender identifiers present
in your population data.
If you want to remove a category in the Races or Genders list boxes, then highlight the entry that
you want to remove and click the Remove button. (It is not possible to remove entries from
either the Available Races or Available Genders list boxes.)
The next step is to create the age groups that match the
age groups in your population file. To start click on the
Add button below the Age Ranges list box. The
AgeRange Definition window will appear. Type in the
name of the age variable in the Age Range ID box and
the upper bound of the age range in the High Age box.
(BenMAP automatically fills in the value for the Low
Age box.) For example, the age range names (with
corresponding low and high ages) might include the
following: OtoO, lto4, 5to9, 10tol4, 15tol9, 20to24,
25to29, 30to34, 35to39, 40to44, 45to49, 50to54, 55to59, 60to64, 65to69, 70to74, 75to79, 80to84,
and 85np. The choice of the names is up to you. However, you must be sure that the names
exactly match those in your population datafile.
Click OK when you have defined the age range. If you make a mistake and want to delete an age
definition after you have entered it, click on the Delete button. This will remove the last age
group that you have entered. (Click on it twice if you want to remove the last two age groups that
you entered.)
Click OK to return to the Load Population DataSet window. From the Grid Definition drop-
down list choose the grid definition (e.g., Tract). Then click on the Browse button to the right of
the Database box to choose the population data file to be loaded into BenMAP.
To finish loading the population datafile,
click OK. The Manage Population
DataSets window will appear displaying the
characteristics of the available population
datafile that you just loaded and any other
datasets that have already been loaded into
BenMAP.
Click OK. In the Population DataSets box
of the Manage Setups window you should
see an entry for the population dataset that
you just loaded.
Format of Population DataSets
£ AgeRange Definition
Age Flange ID:
^]nj^
15to19
Low Age:
High Age:

I
15 r
19
OK
E
Manage Population DataSets
[Available DataSets
Philadelphia T ract
Grid Definition:
[Tract
Population Configuration:
BRace by Gender by Age

Race
| Gender
AgeR

NatAmer
Female
OtoO

Asian
Female
OtoO

Black
Male
OtoO

White
Female
OtoO |—
rih-,n —
<
~1
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Chapter 4. Loading Data
Exhibit 4-7a presents the format that you should use for population datasets, and Exhibit 4-7b
presents a sample of what a datafile might look like. Note that the names used for the age ranges
do not need to follow this same pattern, and instead should be based on what seems most
appropriate for you.
Exhibit 4-7a. Population Data File Variables
Variable
Type
Required
Notes
Age Range
text
yes
References a defined age range in the associated Population Configuration
(see above).
Column
Row
integer
integer
yes
yes
The column and the row link the population data with cells in a Grid
Definition.
Year
integer
yes
The year of the data. Note that this may include historical population
estimates (such as from a census), as well as population forecasts.
Population
numeric (double)
yes
Population estimate. Note that the estimate is not restricted to integers.
Race
text
yes
References a defined race in the associated Population Configuration (see
above).
Gender
text
yes
References a defined gender in the associated Population Configuration (see
above).
Exhibit 4-7b. Sample Population Data File
Age Range
Column
Row
Year
Population
Race
Gender
~to 1
24
101
2000
2.2
ALL
FEMALE
2to9
24
101
2000
5.0
ALL
FEMALE
1 Oto19
24
101
2000
5.1
ALL
FEMALE
20to29
24
101
2000
2.5
ALL
FEMALE
30to39
24
101
2000
7.0
ALL
FEMALE
40to49
24
101
2000
9.0
ALL
FEMALE
50to59
24
101
2000
S.O
ALL
FEMALE
60to69
24
101
2000
2.0
ALL
FEMALE
70tc79
24
101
2000
6.0
ALL
FEMALE
SOup
24
101
2000
1.0
ALL
FEMALE
Otol
24
101
2010
6.0
ALL
FEMALE
2to9
24
101
2010
3.0
ALL
FEMALE
1Oto19
24
101
2010
3.0
ALL
FEMALE
20to29
24
101
2010
2.0
ALL
FEMALE
30to39
24
101
2010
6.0
ALL
FEMALE
40to49
24
101
2010
S.O
ALL
FEMALE
50to59
24
101
2010
3.0
ALL
FEMALE
60to69
24
101
2010
5.0
ALL
FEMALE
70to79
24
101
2010
2.0
ALL
FEMALE
SOup
24
101
2010
0
ALL
FEMALE
4.1.6 Manage Health Impact Function DataSets
Health impact functions calculate the change in adverse health effects associated with a change in
exposure to air population. A typical health impact function has inputs specifying the pollutant;
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Chapter 4. Loading Data
the metric (daily, seasonal, and/or annual); the age, race, and gender of the population affected;
and the incidence rate of the adverse health effect.
An impact function is typically derived from the estimated relationship between the concentration
of a pollutant and the adverse health effects suffered by a given population. This relationship is
called the health impact function. For example, the concentration of the pollutant may be
particulate matter (PM10), and the population response may be the number of premature deaths
per 100,000 persons per day. There are several different functional forms that have been used for
health impact functions. The one most commonly used is the log-linear form, in which the
natural logarithm of the health response is a linear function of the pollutant concentration.
However, for the purposes of estimating benefits, we are not interested in the health impact
function itself, but rather the relationship between the change in concentration of the pollutant,
and the corresponding change in the population health response. We want to know, for example,
if the concentration of PM10 is reduced by 10 micrograms per meter cubed, how many premature
deaths will be avoided? This relationship, called a health impact function, can be derived from
the health impact function.
In summary, estimating the relationship between air pollution and adverse health effects (i.e., the
impact function) can be thought of as consisting of three steps:
(1) choosing a functional form of the relationship between air pollution and adverse health (the
health impact function),
(2) estimating the values of the parameters of this relationship, and
(3) deriving the impact function from the estimated parameters of the functional form.
There are a number of issues that arise when deriving and choosing between health impact
functions that go well beyond this user manual. Hence, it is important to have a trained health
researcher assist in developing the impact function data file.
Health impact functions are subdivided by user-specified types of adverse health effects. The
broadest category is the Endpoint Group, which represents a broad class of adverse health
effects, such as premature mortality, cardiovascular-related hospital admissions, and respiratory-
related hospital admissions, among other categories. (BenMAP only allows pooling of adverse
health effects to occur within a given endpoint group, as it generally does not make sense to sum
or average together the number of cases of disparate health effects, such as premature mortality
and chronic bronchitis.) The Endpoint Group may then be subdivided by user-specified
Endpoints. For example, the respiratory-related hospital admission Endpoint Group, may have
separate Endpoints for Asthma-related hospital admissions and chronic bronchitis-related
hospital admissions.
There are a wide range of variables that can be included in a health impact function, to specify the
parameters of the function and to identify its source, such as the author, year, and location of the
study, as well as other pollutants used in the study. The bibliographic reference for the study may
be included, as well as any additional information needed to identify a particular impact function.
(The Reference and Qualifier variables are useful for this.) A number of health impact functions
have been developed based on epidemiological studies in the United States and Europe.
However, researchers have conducted an increasing number of epidemiological studies in Asia
and Latin America that can be used to develop more location-specific impact functions.
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Chapter 4. Loading Data
To add health impact functions to BenMAP,
click on the Edit button below the C-R
Functions DataSets box in the Manage
Setups window. The Manage C-R
Functions DataSets window will appear.
1
1 S Manage C-R Function DataSets
-=!OJ xll
Available DataSets

C-R Functions In DataSet:

Tree
Data
E ndpoint G roup | E ndpoint
Pollutant | Metric

±U 2J

Delete | Add

Edit

Cancel | OK j
jnix]
Available Setups:
| Philadelphia
Delete j Add
Grid Definitions

Pollutants

Monitor DataSets
County
Metropolitan Area
Tract

Ozone
PM10
PM2.5

Philadelphia Ozone
Philadelphia PM2.5
Philedelphia PM10

Edit
Edit
Edit
Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets
Philadelphia County Incidence
Philadelphia County Prevalence

Philadelphia T ract

Edit
|j Edit
Edit
Variable DataSets

Inflation DataSets

Valuation DataSets

Edit

Edit
Cancel OK
In this window you may Add, Edit, and Delete datasets. The section on the left under Available
DataSets lists the health impact function datasets that are currently in the setup database. The
section on the right under the C-R Functions in DataSet lets you view the functions in a selected
dataset..
To add a new dataset, click the Add
button. The C-R Function DataSet
Definition window will appear.
Type the name that you want to use
for the dataset in the C-R Function
DataSet Name box.
You may then enter functions into
this dataset through an externally
created database by clicking the Load
From Database button.
Alternatively, you may Add, Delete,
and Edit individuals functions within
BenMAP.
To add a database click the Load
From Database button. In the Load a C-R Function Database window, click the Browse
button and then find and select the health imapct function database that you want to load into
your setup.
The C-R Function DataSet Definition window will reappear, and you can then view the health
impact functions that you have loaded into your dataset. Note that the central boxed area in this
window has two parts. On the left is the Tree and on the right is the Data.
The columns within each of the list boxes can be rearranged in order to provide the most useful
display. By clicking and holding the cursor on a column, you may move it withm either the Tree
C-R Function DataSet Definition
C-R Function DataSet Name:

Default C-R Functions)

Tree
Data
Endpoint Group (Endpoint
Pollutant
| Metric
| S easonal M etric | M etric S tatistic
li I
Load From Database
2J
Delete Add
Cancel OK
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Chapter 4. Loading Data
or Data boxes, or you may move columns between boxes. For example, by clicking and holding
on the Pollutant column and then dragging it to the far left of the tree, you can sort all of the
health impact functions by Pollutant. (Note rearranging the columns is only for display and has
no effect on the underlying health impact function database.)
Clicking OK brings you back to the Manage C-R Function DataSets window. Here you have
the opportunity to view the functions in your dataset. If you have more than dataset, you can
choose the dataset that you want to view in the Available DataSets in the left-hand section of the
window. In the C-R Functions in DataSet in the right-hand section of the window, you have the
same Tree and Data structure that you have in the C-R Function DataSet Definition window.
H Manage C-R Function DataSets
Jn|x|
Available DataSets
Default C-R Functions
Delete
Add
C-R Functions In DataSet:
Tree
Data
3
Pollutant Endpoint Group Endpoint
Metri
~
pj.
Ozone

B
\
Acute Respiratory S.

IT
Any of 19 Respirator

i
J
ml
Minor Restricted Act

Edit
Cancel
OK
You may edit this dataset by clicking the Edit button. This will bring up the C-R Function
DataSet Definition window. You may load an additional database to your dataset, or you may
Add, Edit, and Delete individual functions directly in BenMAP.
1
Ic-R Function Definition

DataSet Name:
Function:
|Default C-R Functions
)Beta-DELTAQ*POP

Edit
Beta:
Endpoint Group:

Endpoint:

Beta Distribution:

1 _ |
[Any of 19 Respiratory Sy /*- |

iJLl
| Normal
_~] 10.000137
Pollutant:

Metric:
Metric Statistic:
Beta Parameter 1:
Beta Parameter 2:

Seasonal Metric:

Constant Description:
Constant Value: |

I
3
A:f^
I5
Race:

Gender:
Start Age: End Age:
*l l°
1

I
3 |lB I2£| |S4 [«|
C:| |0
Author:

Year:
Location:
Incidence DataSet:
Prevalence DataSet: |
|Krupnick

11990
| Los Angeles, CA
I d I d
Qualifier:

Other Pollutants:

Variable DataSet:
|C°H
I m
1 Reference:

Cancel | OK | |
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Chapter 4. Loading Data
To edit an existing function, highlight a particular function in the C-R Function DataSet
Definition window, and then click the Edit button. The C-R Function Definition window
appears, and you may change any of the values in the boxes and the drop-down lists. When you
are finished click OK.
From the C-R Function DataSet Definition window you can also add health impact functions to
the ones that are already in your dataset. Click the Add button and a blank C-R Function
Definition window will appear, and you can then create new health impact functions. (We will
discuss this in more detail in subsequent examples.)
C-R Function Definition
DataSet Name:
|Default C-R Functions
"3
H
~3 r
Metric Statistic:
T| |None
3
Seasonal Metric:
"3
"3
Start Age: End Age:
i° [xi |o [xi
Year:
Other Pollutants:

Edit
Beta Distribution:
Beta:
(None
zi l°
Beta Parameter 1:
Beta Parameter 2:
l°
l°
Constant Description:
Constant Value:
I"

I"
Incidence DataSet: Prevalence DataSet:
I 31
"3
Variable DataSet:

After defining the new health impact function, click
OK. This will take you back to the C-R Function
DataSet Definition window. When you are finished
with any editing or adding of health impact functions
click OK.
The Manage Setups window will appear. Here in the
C-R Function DataSets box you should see an entry
for any health impact function datasets that you have
loaded.
Format of Health Impact Function DataSets
E3S3EB2M
Available Setups:
[Philadelphia
JSjx]
Grid Definitions

Pollutants

Monitor DataSets
County
Metropolitan Area
Tract

Ozone
PM10
PM2.5

Philadelphia Ozone
Philadelphia PM2.5
Philadelphia PM10
Edit
Edit
Edit
Incidence/Prevalence DataSets

Population DataSets

C-R Function DataSets
Default County Incidence
Philadelphia County Prevalence

Philadelphia T ract

Default C-R Functions
Edit
Edit
Edit
Variable DataSets

Inflation DataSets

Valuation DataSets

Edit
Edit
I Edit
Exhibit 4-8a presents the format that you should use for health impact function datasets, and
Exhibit 4-8b presents a sample of what a datafile might look like.
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Chapter 4. Loading Data
Exhibit 4-8a. Health Impact Function Data File Variables
Variable
Type

Required
Notes
Endpoint Group
text

yes
If this doesn't reference an already defined Endpoint Group, one will be
added.
Endpoint
text

yes
If this doesn't reference an already defined Endpoint for the Endpoint
Group, one will be added.
Start Age
End Age
integer
integer

yes
yes
Specifies the low and high ages, inclusive. For example, Start Age of "0"
and End Age of "1" includes infants through the first 12 months of life and
all one-year old infants.
Pollutant
text

yes
Should reference an already defined Pollutant.
Metric
text

yes
Should reference an already defined Metric for the Pollutant.
Seasonal Metric
text

no
Should either be blank (signifying no Seasonal Metric value) or reference
an already defined Seasonal Metric for the Metric.
Annual Metric
text

no
Should either be blank (signifying no Annual Metric value) or be one of:
None, Mean, Median, Min, Max, Sum.
Study Author
text

no
The author(s) of the study from which the function is derived.
Study Year
integer

no
The year of publication of the study.
Study Location
text

no
The location of the study.
Other Pollutants
text

no
Identifies other pollutants that were included simultaneously in the
estimation equation for the pollutant of interest.
Qualifier
text

no
Provides additional information to identify a particular health impact
function, such as when a particular study has multiple functions.
Reference
text

no
Bibliographic reference, included to identify the source in the health
literature.
Race
text

no
Should either be blank (signifying All Races) or reference a defined Race
(from one or more Population Configurations - see above).
Gender
text

no
Should either be blank (signifying All Genders) or reference a defined
Gender (from one or more Population Configurations - see above).
Beta
numeric
(double)
no
Mean value of the Beta distribution.
Distribution Beta
text

no
If the Beta has no distribution, any value is acceptable. Otherwise, should
be one of: Normal, Triangular, Poisson, Binomial, LogNormal, Uniform,
Exponential, Geometric, Weibull, Gamma, Logistic, Beta, Pareto, Cauchy,
Custom.
Parameter 1 Beta
numeric
(double)
no
Parameter 1 of the Beta distribution (meaning depends on the distribution -
for Normal distributions this represents the standard deviation).
Parameter 2 Beta
numeric
(double)
no
Parameter 2 of the Beta distribution (meaning depends on the distribution -
for Normal distributions this is not required).
Name A
text

no
Description of variable A.
A
numeric
(double)
no
A constant value which can be referenced by the Function (see below).
Name B
text

no
Description of variable B.
B
numeric
(double)
no
A constant value which can be referenced by the Function (see below).
Name C
text

no
Description of variable C.
C
numeric
(double)
no
A constant value which can be referenced by the Function.
Incidence DataSet
text

no
Specifies the dataset from which incidence data will be derived. The user
may choose from multiple datasets. (Initially this field may be left blank.)
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Chapter 4. Loading Data
Variable Type Required Notes
Prevalence DataSet text no Specifies the dataset from which prevalence data will be derived. The user
may choose from multiple datasets. (Initially this field may be left blank.)
Function text yes The functional form, interpreted (executed) by BenMAP when running an
analysis. Typically of the form "(EXP(Beta * DeltaQ)-l) * Incidence *
Pop". The BenMAP User Manual will contain more information on the
format of this field.
Abt Associates Inc.
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Chapter 4. Loading Data
4.1.7 Manage Variable DataSets
Health Impact functions and/or Valuation functions (see section 4.1.9 below) may sometimes
need to refer to variables other than those for which BenMAP automatically calculates values.
For example, some valuation functions may need to reference the median income within each
area of analysis. To facilitate this, BenMAP allows you to load datasets of variables, which may
either be global values or may vary geographically (meaning they are associated with a Grid
Definition).
To add variables to BenMAP (such as income
and other miscellaneous variables that might
be needed in the analysis), click on the Edit
button below the Variables DataSets box in
the Manage Setups window. The Manage
Setup Variables DataSets window will
appear.
In this window you may Add, Edit, and
Delete datasets. The section on the left under
Available DataSets lists the variables
datasets that are currently in the setup
database. The section on the right under the
DataSet Variables lets you view the
variables in a selected dataset.
To add variables click the Add button. This will take you to the Setup Variable DataSet
Definition window. In this window you may add externally created variables through the Load
From Data button for any of your predefined grid definitions. Alternatively, by clicking the
Add button you may create global variables to your dataset that are not tied to a specific
geographic region.
E
Manage Setup Variable DataSets
Available DataSets
Delete
Add
DataSet Variables

Edit
Cancel OK
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Chapter 4. Loading Data
^ Setup Variable DataSet Definition
DataSet Name:
[Philadelphia County Variables]
DataSet Variables
Load From Database
Delete

Add

Global Geographically Variable |
Grid Definition:
r
Values:
-!~! *1
3
Cancel
~K
To start, type the name that you want to use for the dataset in the DataSet Name box. (This is a
name that is internal to BenMAP and used just for identification.)
To add an externally created variable database,
click the Load From Database button. This will
bring up the Load Variable Database window
choose. Here you need to choose the grid
definition from the Grid Definition drop-down
list that matches the level of aggregation in your
variable database. Then you may use the Browse
button to find and select the desired data file.
Grid Definition:
1 County

A
Database:
jrial\Philadelphia\Philadelphia County Variables, mdtj
Browse

Cancel
OK

After choosing the database, click
OK. This takes you back to the Setup
Variable DataSet Definition
window. This allows you to see the
variables in the dataset and their
values.
^ Setup Variable DataSet Definition
DataSet Name:
| Philadelphia County Variables
DataSet Variables
-
median income

count_farm_employed

total_population

populationOtol 7

populationl 3to64

population65Up

dailyWageOutdoor

natl median income

~
Delete
Add
Load From Database
Global Geographically Variable |
Grid Definition:
fll County
Values:

"3
| | Column
(Row

Value

42^B
101
32447

42
91
32447

42
45
32447

42
29
32447
—

42
17
32447

d
Cancel OK
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Chapter 4. Loading Data
You may also add Global variables to the
dataset by clicking on the Add button in the
Setup Variable DataSet Definition window.
After clicking Add, type the name of the
variable where it says Setup Variable 0 in the
DataSet Variables list box, and then in
Value box type the value of the global
variable.
v Setup Variable DataSet Definition
DataSet Name:
| Philadelphia County Variables
JS]x]
DataSet Variables
-

count_farm_employed

total_population
—

populationOtol 7

populationl 8toG4

populationS5Up

dailyWageOutdoor

natl_median_income

income elasticity
~

Load From Database
Global | Geographically Variable |
Value:
When finished adding variables, click OK. This will take you to the Manage Setup Variable
DataSets window. In the Available DataSets list box there is an entry for the dataset that you
just added. And in the DataSet Variables list box are the variables in the highlighted dataset.
v Manage Setup Variable DataSets

Available DataSets
Philadelphia County Variables
Delete
Add
DataSet Variables

AvgHHSize

median income

count_farm_employed

total_population

populationOtol 7

populationl 8to64

population65Up

dailyWageOutdoor
1—
natl_median_income
-
Edit
Cancel
~ K
At this point you may click Add to load an additional dataset, click Edit to edit the selected
dataset, click Delete to delete the selected dataset, or complete this variable management step by
clicking OK.
Clicking OK returns you to the Manage Setups window, where in the Variable DataSets box,
you should see an entry for the variable dataset that you just created.
Format of Variable DataSets
Exhibit 4-9a presents the format that you should use for variable datasets, and Exhibit 4-9b
presents a sample of what a datafile might look like.
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Chapter 4. Loading Data
Exhibit 4-9a. Variable Data File Variables
Variable
Type
Required
Notes
Column
integer
yes
The column and the row link the population data with cells in a Grid
Row
integer
yes
Definition.
Exhibit 4-9b. Sample Health Impact Function Data File
COL
ROW
median income
pct_poverty
42
17
39871
0.11
42
29
43760
0.08
42
45
38187
0.13
42
91
42298
0.19
42
101
31261
0.21
4.1.8 Manage Inflation DataSets
It may be desirable for Valuation functions (see section 4.1.9 below) to account for inflation, and
generate economic benefits for various years. BenMAP supports this by allowing you to provide
inflation factors for three different types of values:
All Goods Index can be used to adjust the value of generic goods.
Medical Cost Index can be used to adjust the value of medical expenses.
Wage Index can be used to adjust the value of wages.
If a valuation function includes an estimate of wage income, for example, this value could be
multiplied by the variable Wctgelndex in order to adjust this value by the provided Wage Index
adjustment factor. Note that these variable values have default values of one.
To add inflation data to BenMAP, click on the Edit button below the Inflation DataSets box in
the Manage Setups window. The Inflation DataSet Manager window will appear.
In this window you may Add, Edit, and Delete datasets. The section on the left under Available
DataSets lists the inflation datasets that are currently in the setup database. The section on the
right presents the inflation factors in a selected dataset.
Click on the Add button. In the Load
Inflation DataSet window, type in the
name of the dataset in the Inflation
DataSetName box, and then click on the
Browse button to the right of the
Database box to choose the dataset that
you want to import.
1 Load Inflation DataSet

xj
Inflation DataSet Name:

[Default Inflation
Database:
Browse
1
|s lnc\BenMAP 2.0\Data\Philadelphia\lnflators.mdb

Cancel
OK

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Chapter 4. Loading Data
Click OK. The Inflation DataSet Manager
that you just loaded.
At this point you may add more data by
clicking Add, or you may complete this step
by clicking OK. Clicking OK takes you to
the Manage Setups window, where in the
Inflation DataSets box, you should see an
entry for the inflation dataset that you just
loaded.
will appear. Here you may view the data
1
£ Manage Setups

^jnjxj
Available Setups:

[Philadelphia
zi

Delete | Add

Grid Definitions

Pollutants

Monitor DataSets

County
Metropolitan Area
Tract

Ozone
PM10
PM2.5

Philadelphia Ozone
Philadelphia PM10
Philadelphia PM2.5
Edit
Edit
Edit

Incidence/Pre valence DataSets

Population DataSets

C-R Function DataSets

Philadelphia County Incidence
Philadelphia County Prevalence

Philadelphia T ract

Default C-R Functions

Edit
Edit
Edit

Variable DataSets

Inflation DataSets

Valuation DataSets

Philadelphia County Variables

Default Inflation

Edit
| Edlt
Edit

Cancel OK
Format of Inflation DataSets
Exhibit 4-10a presents the format that you should use for inflation datasets, and Exhibit 4-10b
presents a sample of what a datafile might look like.
Exhibit 4-10a. Inflation Data File Variables
Variable
Type
Required
Notes
Column
integer
yes
The column and the row link the population data with cells in a Grid
Row
integer
yes
Definition.
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Chapter 4. Loading Data
Exhibit 4-10b. Sample Inflation Data File
YEAR
AUGoods Index
MedicalCostlndex
Wagelndex
1994
0.86
0.8
0.81
1995
0.88
0.84
0.83
1996
0.91
0.87
0.86
1997
0.93
0.89
0.89
1998
0.94
0.92
0.92
1999
0.96
0.96
0.96
2000
1
1
1
2001
1.02
1.04
1.03
4.1.9 Manage Valuation DataSets
Reductions in ambient concentrations of air pollution generally lower the risk of future adverse
health affects. The value to society of avoiding these adverse health effect has three components:
(1) the cost of the illness to society, including the total value of the medical resources used, (2)
the value of the lost productivity (e.g., lost wage), and (3) the willingness to pay of the individual,
as well as that of others, to avoid the pain and suffering resulting from the illness. The first two
components might be termed the "market" component and the third as the "non-market"
component.
When an individual becomes ill, there is some amount of resources (medical goods and services)
that are used to address the illness. The value of those resources is the market component of the
value of avoiding the illness - i.e., the value of the resources that would not have to be used up if
the individual had not incurred the illness. This may be a small value - e.g., the cost of aspirin
used for a headache, or a very large value - e.g., the value of medical goods and services used to
treat someone who goes to the hospital with a life-threatening illness. In addition, when one
becomes ill, their productivity is reduced, and this can also have a market cost that might be
approximated by any lost wages associated with the illness.
The market (or cost-of-illness) approach attempts to estimate the total value of the medical
resources used up as well as the value of the individual's lost productivity as a result of the
illness. Because this method does not include the value of avoiding the pain and suffering
resulting from the illness, it is generally believed to underestimate the total value of avoiding the
illness, perhaps substantially.
The method the benefit analyst chooses when valuing a particular health endpoint will depend in
part on the estimates that are available. Benefit analysts typically do not do primary research to
generate valuation data for use in a benefit analysis - to do so is too expensive and time
consuming. Instead, the benefit analyst uses data or estimates that have been collected or
generated by researchers and can be readily obtained in publicly available databases or in the
open literature. When reviewing the economic literature to develop the valuation database, it is
important to have an economist assist.
BenMAP allows the valuation estimates to vary by Endpoint Group, Endpoint, and Age (note
that multiple estimates may be provided for a particular combination). BenMAP allows the
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Chapter 4. Loading Data
valuation function to be quite detailed, and allows an uncertain parameter (A) with a user-
specified distribution. The user can modify the valuation function with a number of constant
values (B, C, and D) that might represent an adjustment for inflation, income growth, income
elasticity, or, say, purchasing power parity. Finally, BenMAP has two fields to more clearly
identify the valuation function (i.e., Qualifier and Reference).
To add valuation fuctions to BenMAP, click on the Edit button below the Valuation DataSets
box in the Manage Setups window. The Manage Valuation Function DataSets window will
appear.
^ Manage Valuation Function DataSets
Available DataSets
Valuation Functions In DataSet:
^jajxj
Tree
Data
E ndpoint G roup | E ndpoint
Qualifier
|Referen

<1
id

Edit
Cancel I OK
In this window you may Add, Edit, and Delete
datasets. The section on the left under
Available DataSets lists the valuation datasets
that are currently in the setup database. The
section on the right under the Valuation
Functions in DataSet lets you view the
valuation factors in a selected dataset.
To add a dataset click on Add. In the
Valuation Function DataSet Name type the
name of the dataset that will be added.

Valuation Function DataSet Name:
[Default Valuatior)
Tree
Data
Endpoint Group | Endpoint
Qualifier | Reference | Start Age | End Age
Cancel | OK |
You may load valuation data with an
externally-created database, or you may add individual valuation functions from within BenMAP.
To load an externally-created valuation database, click on the Load From Database button. This
will bring you to the Load a Valuation Function Database window.
In the Load a Valuation Function Database window use the File of Type drop-down list to
choose the type of file (e.g., MS Access Database). Choose the valuation database and click
Open. This will bring you back to the Valuation Function DataSet Definition window. Here
you can view the valuation functions that you have in your database.
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Chapter 4. Loading Data
Note that the large box is divided in
two. On the left is the Tree and on
the right is the Data. The tree can be
expanded by clicking on the "+" sign,
or contracted by clicking on the
sign.
The columns within each of the list
boxes can be rearranged in order to
provide the most useful display. By
clicking and holding the cursor on a
column, you may move it within
either the Tree or Data boxes, or you
may move columns between boxes.
(Note that rearranging the columns is
only for display and has no effect on
the underlying Valuation function dataset.)
In the Valuation Function DataSet Definition window you can also edit the functions already
existing in your data set by highlighting a particular Valuation function and then clicking the Edit
button.
The Valuation Function Definition window allows you to change any of the values in the boxes
and the drop-down lists. When you are finished click OK.
1 Valuation Function Definition
DataSet Name:
Function:
[Default Valuation
A"AIIG oodsl ndex
Endpoint Group: Endpoint:
Edit
A Description: A:
[CT8BH3HSBBU5 ~ | |Acute Bronchitis
|V/TP in 2000$ 1355.049243104003

A Distribution:
Qualifier:
[Uniform
A Parameter 1: A Parameter 2:
|WTP: G day illness, CV studies
|l 05.059127S07G17 |606.639404296875
Reference:
Constant Description: Constant Value:
r
B: [adjustment for household inc 10.973810970783234

C:| |0
D: | |°
Cancel | OK | 1
From the Valuation Function DataSet Definition window you can also add Valuation
functions to the ones that are already in your dataset. Click the Add button and a blank
Valuation Function Definition window will appear, and you can then create a new
Valuation function.
After defining the new Valuation function, click OK. This will take you back to the
Valuation Function DataSet Definition window. When you are finished with any
editing or adding of valuation functions click OK.
Valuation Function DataSet Definition
Valuation Function DataSet Name:
_JS]x|
Default Valuation
Tree
Data

Z3
| Endpoint Group |
Endpoint | Qualifier
Reference
Start Age
End Age
_ I
|b
Ac
ute
Bronchitis

Acute Bronchitis

WTP: 1 day illness, (

jg-

WTP: 28 syrnptorn-d

WTP: 6 day illness, (

L.l

0
17
H
Acute Myocardic

\l
-
Acute Respirator

~
Asthma Exacerb.

p
-
Chronic Asthma

.
.

n-.

ir Rrnnrhifl

Ll
_

Load From Database
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Chapter 4. Loading Data
The Manage Setups window will appear. Here in the Valuation Function DataSets
box you should see an entry for the valuation function dataset that you just loaded.
Format of Valuation DataSets
Exhibit 4-1 la presents the format that you should use for valuation datasets, and Exhibit 4-1 lb
presents a sample of what a datafile might look like.
Exhibit 4-lla. Valuation Data File Variables
Field Name:
Type:

Required:
Notes:
Endpoint Group
text

yes
If this doesn't reference an already defined Endpoint Group, one will be
added.
Endpoint
text

yes
If this doesn't reference an already defined Endpoint for the Endpoint Group,
one will be added.
Qualifier
text

no
Provides additional information to identify a particular valuation function.
Reference
text

no
Bibliographic reference, included to identify the source in the economic
literature.
Start Age
End Age
integer
integer

no
no
Specifies the low and high ages, inclusive. For example, Start Age of "0"
and End Age of "1" includes infants through the first 12 months of life and
all one-year old infants.
Point Estimate
numeric
(double)
yes
Central estimate of the unit value.
Name A
text

no
Description of variable A.
A
numeric
(double)
no
Mean of the A distribution.
Distribution A
text

no
If A has no distribution, any value is acceptable. Otherwise, should be one
of: Normal, Triangular, Poisson, Binomial, LogNormal, Uniform,
Exponential, Geometric, Weibull, Gamma, Logistic, Beta, Pareto, Cauchy,
Custom.
Parameter 1 A
numeric
(double)
no
Parameter 1 of the A distribution (meaning depends on the distribution - for
Normal distributions this represents the standard deviation).
Parameter 2 A
numeric
(double)
no
Parameter 2 of the A distribution (meaning depends on the distribution - for
Normal distributions this is not required).
Name B
text

no
Description of variable B.
B
numeric
(double)
no
A constant value which can be referenced by the Function (see below).
Name C
text

no
Description of variable C.
C
numeric
(double)
no
A constant value which can be referenced by the Function (see below).
Name D
text

no
Description of variable D.
D
numeric
(double)
no
A constant value which can be referenced by the Function (see below).
Function
text

yes
The functional form, interpreted (executed) by BenMAP when running an
analysis. The BenMAP User Manual will contain more information on the
format of this field.
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Chapter 4. Loading Data
Exhibit 4-llb. Sample Valuation Data File

Start
End
Point
Endpoint Group
Endpoint
Qualifier
Reference
Age
Age
Estimate
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 3% DR, Eisenstein (2001)

0
24
49651
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 3% DR, Eisenstein (2001)

25
44
58683
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 3% DR, Eisenstein (2001)

45
54
62964
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 3% DR, Eisenstein (2001)

55
65
126602
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 3% DR, Eisenstein (2001)

66
99
49651
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 7% DR, Eisenstein (2001)

o
24
49651
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 7% DR, Eisenstein (2001)

25
44
57738
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 7% DR, Eisenstein (2001)

45
54
61570
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 7% DR, Eisenstein (2001)

55
65
118545
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
10 yrs med, 5 yrs wages, 7% DR, Eisenstein (2001)

66
99
49651
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
COI
5 yrs med, 5 yrs wages, 3% DR, Russell (1998)

0
24
22331
Name A
A
Distribution A
Parameterl A
Parameter2 A Name B
B
Name C
C
Name D
D
Function
Medical costs over 10 years in 2000$, 3% d.r.
4.2

opportunity cost over 5 yrs, 3% d.r.
0

A *M e die alC o stlndex+B * Wagelndex
Medical costs over 10 years in 2000$, 3% d.r.
4.2

opportunity cost over 5 yrs, 3% d.r.
9033

A*MedicalCo stlndex+B * Wagelndex
Medical costs over 10 years in 2000$, 3% d.r.
4.2

opportunity cost over 5 yrs, 3% d.r.
13313

A *M e die alC o stlndex+B * Wagelndex
Medical costs over 10 years in 2000$, 3% d.r.
4.2

opportunity cost over 5 yrs, 3% d.r.
76951

A *M e die alC o stlndex+B * Wagelndex
Medical costs over 10 years in 2000$, 3% d.r.
4.2

opportunity cost over 5 yrs, 3% d.r.
0

A *M e die alC o stlndex+B *Wagelndex
Medical costs over 10 years in 2000$, 7% d.r.
4.2

opportunity cost over 5 yrs, 7% d.r.
0

A *M e die alC o stlndex+B * Wagelndex
Medical costs over 10 years in 2000$, 7% d.r.
4.2

opportunity cost over 5 yrs, 7% d.r.
8087

A *M e die alC o stlndex+B *WageIndex
Medical costs over 10 years in 2000$, 7% d.r.
4.2

opportunity cost over 5 yrs, 7% d.r.
11919

A *M edic alC o stlndex+B * Wagelndex
Medical costs over 10 years in 2000$, 7% d.r.
4.2

opportunity cost over 5 yrs, 7% d.r.
68894

A *M e die alC o stlndex+B *WageIndex
Medical costs over 10 years in 2000$, 7% d.r.
4.2

opportunity cost over 5 yrs, 7% d.r.
0

A *M edic alC o stlndex+B * Wagelndex
Medical costs over 5 years in 2000$, 3% d.r.
4.2

opportunity cost over 5 yrs, 3% d.r.
0

A *M edic alC o stlndex+B * Wagelndex
4.2 Modify and Delete Setups
In the Manage Setups window you may add a new setup, or you may choose an existing setup in
the Available Setups drop-down list and either modify or delete it.
Manage Setups
Available Setups:
[cityOne

Grid Definitions
Pollutants
Monitor DataSets
CityOne Grid
Counties
Metropolitan Area
Ozone
PM2.5
CityOne Monitors
Edit
Incidence/Prevalence DataSets
Population DataSets
C-R Function DataSets
CityOne Incidence and Prevalent
CityOne T ract Population
CityOne Health Impact Functions
Variable DataSets
Inflation DataSets
Valuation DataSets
CityOne Variables
CityOne Inflators
CityOne Valuation Functions
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Chapter 4. Loading Data
4.3 Export and Import Setups
BenMAP allows users to export and import entire databases (all Setups), individual Setups, and
parts of individual Setups (e.g. all GridDefinitions, or individual health impact Function
DataSets). This functionality can be used to share data between users, as well as to allow users to
move databases between computers. In particular, all of the steps involved in creating a Setup
can be done just once, after which the data can be exported and then imported on other
computers.
4.3.1 Export Setups
To export part or all of an existing
setup, go to the Tools drop-down
menu, and choose the Database
Export option. This will bring up the
DatabaseExportForm window.
In the central panel under Select
Obects to Export, you can view and
select the data that you want to
export.
Tools Help
Database Export
Database Import
GIS / Mapping
Modify Setup
-lolxj
Environmental Benefits Mopping and Analysis Program
Create Air Quality Grids

Create and Run
Configuration

Aggregation, Pooling,
and Valuation

Create Reports
Active Setup:
[~tyOne
Initially, all of the setups are in a data tree,
which is initially collapsed into the dataset
Available Setups. To view the available
Setups, click on the "+" sign to the left of
Available Setups. This will expand the
data tree. To contract the tree simply click
on the sign.
Choose an dataset to export. You may
choose all available Setups, individual
Setups, the various collections within a
Setup (all GridDefinitions, all Pollutants,
all Population DataSets, etc.), and
individual datasets within the various
collections within a Setup. Click OK when you have selected the dataset to export.
^ DatabaseExportForm
- |q| *i
Select Object to Export:
Available Setups
& CityOne
Grid Definitions
j CityOne Grid
Counties
Metropolitan Area
0 Pollutants
EE Monitor DataSets
EE Incidence/Prevalence DataSets
lj Population DataSets
El C-R Function DataSets
EE Variable D ataS ets
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Chapter 4. Loading Data
This will bring up the Export Database Objects To File window. From here you may name the
dataset and choose the folder into which to export it. Type the dataset name the Filename box,
and in the Save in drop-down menu locate the folder that you want to use.
Export Database Object To File
Save jn: Final BenMAP input files for training
3 «- (S :i TT-
My Documents
Monitor Data
_| Shapefiles
r
File name:
Save as type: | BenMAP Database Files (*.bdb)
"3
3
JLlxJ
NOTE: Exported BenMAP database files have a bdb extension, and are a binary format not
suitable for viewing in external applications.
4.3.2 Import Setups
To import part or all of an existing setup, go to the Tools drop-down menu, and choose the
Database Import option. This will bring up the Import Object Into Database window.
The Database Object File box identifies the file that you want to import. Click on the Browse
button to locate the file to import. This will bring up the Import Database Object From File
window.
^ Import Object Into Database
Database Object File:
- njx.
Browse
T arget Setup:
3

Cancel
I OK |

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Chapter 4. Loading Data
Import Database Object From File
My Documents
Look .in: Final BenMAP input files for training
"3
e df n*
Monitor Data
I Shapef iles
1*1 New Grid Definitions.bdb
1*1 New 5etup.bdb
File name:
r
"3
Files of type: | BenMAP Database Files (x.bdb)
~3
jj*i
Open |
Cancel
V/,
Select the file that you want to import, and then click Open. This will bring you back to the
Import Object Into Database window. Click OK to finish the import process.
1
1 ^ Import Object Into Database
JnJxJ
Database Object File:
Browse
|\Final BenMAP input files for training\New Setup.bdb
T arget Setup:

Cancel
OK | ||
II : '1
If the file contains a sub-Setup item, such as a collection (e.g., a set of grid definitions) or an
individual item (e.g., a single grid definition from among many available), select the Setup into
which it should be imported. Select the Setup from the drop-down list below Target Setup.
Click OK to finish.
NOTE: Duplicates of datasets (typically
identified by their names - e.g. "Population
City One") will overwrite existing data in a
Setup. New datasets (i.e., non-duplicated)
will be added to the Setup.
1 £ Import Object Into Database
-ln|x|
Database Object File:
Browse
| H:\ENVR\BenMAP\T raining Material\Mexico Trairiin
Target Setup:

|HBH5E jj

mm
Cancel
°K
l l
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In this chapter...
Create air quality grids using
different methods.
Find details on the file structures
for data inputs.
Interpolate with Closest Monitor
orVoronoi Neighbor Averaging.
Scale monitor data with
modeling data.
Learn about the Monitor Rollback
feature.
Chapter Overview
5.1 Model Direct 5-2
5.2 Monitor Direct 5-3
5.2.1 Closest Monitor for Monitor Direct 5-4
5.2.2 Voronoi Neighbor Averaging (VNA) for Monitor
Direct 5-4
5.2.3 Other Monitor Direct Options 5-7
5.3 Monitor and Model Relative 5-10
5.3.1 Spatial Scaling 5-11
5.3.2 Temporal Scaling 5-11
5.3.3 Spatial and Temporal Scaling 5-11
5.3.4 Examples 5-12
5.4. Monitor Rollback 5-13
5.4.1 Example 5-17
5.5 Questions Regarding Creating Air Quality Grids 5-21
CHAPTER 5
Creating Air
Quality Grids
-------
5. Creating Air Quality Grids
BenMAP is not an air quality model, nor can it generate air quality data independently. Instead it
relies on the air quality inputs given to it. To estimate population exposure to air pollution,
BenMAP uses air quality grids that it generates from input air quality data (modeling and or
monitoring data).
BenMAP creates air quality grids to estimate the average
exposure to ambient air pollution of people living in some
regularly-shaped area, or domain, such as that delineated by air
quality models, as well as more irregular shapes, such as cities or
nations. However, BenMAP does not estimate personal
exposure. Instead, the air quality grids provide the average
population exposure for each grid cell that BenMAP can then use
in health impact functions.
To create air quality grids, BenMAP uses a number of inputs, including modeling data,
monitoring data, or both. You may enter your own modeling and monitoring data, provided that
the data are in a format recognized by BenMAP.
To start the grid creation process,
click on the Create Air Quality
Grids button. BenMAP will then
ask which of the following types
of air quality data you wish to use:
Model Direct. Choose this
option if you have air quality
modeling data that you wish to
use directly. Exhibit 5-1 below
describes the input format for
modeling data.
5*" Monitor Direct. Choose this
option if you wish to use
monitoring data, without
additional modeling data.
3*" Monitor and Model
Relative. Choose this option if
you wish to use a combination of
monitor and model data.
S*" Monitor Rollback. Choose this option if you want to reduce monitor levels by a specified
amount.
Select your option and then click Go!. BenMAP will direct you through the necessary' steps for
each option.
Air Quality Grids contain air
pollution data. BenMAP uses
one baseline and one control air
quality grid and estimates the
change in the number of adverse
health effects between the two.
£ BenMAP 2.0
Tools Help
jojxj

Environmen
^Choose Grid Creation Method—
(* iModei Direct!
C Monitor Direct
C Monitor and Model Relative
C Monitor Rollback
Cancel
Go!
is Program

Create Air Quality Grids
Create and Run
Configuration

Aggregation, Pooling,
and Valuation
Create Reports
Active Setup:
Philadelphia
d
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Chapter 5. Creating Air Quality Grids
5.1 Model Direct
After choosing the Model Direct option, you need to specify the location of your data file (Model
File), the grid definition to which the data file corresponds (Grid Type), and the pollutant that
the data is modeling (Pollutant)
v Model Direct Settings
^JnJj
-------
Chapter 5. Creating Air Quality Grids
Exhibit 5-2. Sample Air Modeling Data File
Column
Row
Metric
Seasonal Metric
Annual Metric
Values
24
101

5.90,.,., 11,.,.
24
102

19.80,.,.,3.5,.,.,14.70,.,.,.,.,.,4.9C
24
103

11.5,.,.,1.5,.,.,10.20,.,.,2.5,.,.,2.E
24
104

12.60,.,.,4.60,.,.,7.30 9.2C
24
105
Daily Average

.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,5.90,.,.,11,.,.
24
106
Daily Average

19.80,.,.,3.5,.,., 14.70,.,.,.,.,.,4.9C
24
107
Daily Average

11.5,.,., 1.5, .,.,10.20,.,., 2.5,.,. ,2.t
24
108
Daily Average

12.60,.,.,4.60,.,.,7.30,.,.,.,.,.,9.2[
24
109
Daily Average
Quarterly Average

11.82,13.24,18.79,14.25
24
110
Daily Average
Quarterly Average
12.31,13.89,18.27,21.19
25
101
Daily Average
Quarterly Average
12.33,15.68,18.49,15.96
25
102
Daily Average
Quarterly Average

13.52,13.69,19.04,22.43
25
103
Daily Average

Mean
14.52
25
104
Daily Average

Mean
16.41
25
105
Daily Average

Mean
15.62
25
106
Daily Average

Mean
17.17
25
107
Daily Average
Quarterly Average
Mean
14.29
25
108
Daily Average
Quarterly Average
Mean
18.97
25
109
Daily Average
Quarterly Average
Mean
20.14
25
110
Daily Average
Quarterly Average
Mean
16.46

TIP; Carefully name your air quality grids so you can easily recognize them. Include the
words base or control and a scenario identifier. For example, if you were analyzing a mobile source
emission reduction scenario for 2020 using the REMSAD air quality model for PM2.5, you might
name your grids "Base 2020 mobile REMSAD PM25" and "Control 2020 mobile REMSAD PM25"
or something similar.
5.2 Monitor Direct
Using the Monitor Direct grid creation option, you create an air quality grid directly from air
pollution monitoring data. At the top left of the Monitor Direct Settings window, you are asked
to select a previously defined grid definition from the Grid Type drop-down list, and a
previously-defined pollutant from the Pollutant drop-down list. And at the top right of the
Monitor Direct Settings window, you are asked to select an interpolation method. The
interpolation method is used to move from point-based monitor data to grid cell based air quality
data. That is, some grid cells will have many monitors in them, some will have just one, and
some will have none. BenMAP uses the interpolation methods to generate representative air
quality metric values for each grid cell from monitor data for all of these cases.
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Chapter 5. Creating Air Quality Grids
BenMAP includes two interpolation
methods. The Closest Monitor
method simply uses the monitor
closest to a grid cell's center as its
representative value. The Voronoi
Neighbor Averaging method takes an
inverse-distance weighted average of
a set of the monitors surrounding a
grid cell's center as its representative
value. Each method is described in
detail in sections 5.2.1 and 5.2.2. For
more detail, see Appendix C.
In the middle of the Monitor Direct
Settings window, you can choose the
rest of the options to create your air
quality grid. The tabs, and the other
options at the bottom of the window
are described below.
5.2.1 Closest Monitor for Monitor Direct
If you choose the Closest Monitor option, BenMAP identifies the monitor closest to each grid
cell's center, and then assigns that monitor's data to the grid cell.
Closest Monitor interpolation has one advanced interpolation option, which can be modified by
clicking on the Advanced button at the bottom of the window:
Maximum Neighbor Distance specifies the maximum distance that a monitor may be from a
grid cell (distances are calculated using the grid cell centroid). Cells without any monitors within
this distance will not be included in the resultant air quality grid. The default setting is infinite
(i.e. no limit to the distance between a monitor and a grid cell).
5.2.2 Voronoi Neighbor Averaging (VNA) for Monitor Direct
If you choose the VNA option, BenMAP first identifies the set of monitors that "surround" each
grid cell's center (these monitors are referred to as the grid cell's neighbors), and then BenMAP
calculates an inverse-distance weighted average of these neighboring monitors. In this section,
we provide some examples of the different ways that BenMAP calculates the average of the
neighbor monitors. See Appendix C for an expanded discussion of VNA, including how the
VNA algorithm actually chooses the neighbor monitors, as well as the different ways that it may
be used.
VNA interpolation has three advanced interpolation options, which can be modified by clicking
on the Advanced button at the bottom of the window:
S Monitor Direct Settings
_=lnl2sJ
Grid Type:
Pollutant:
3]
r
Interpolation Method
C Closest Monitor
(• Voronoi Neighborhood Averaging
Library | Database, Columns | Database, Rows | Text File)
Monitor DataSet:
I 3
Monitor Library Year:
I 3
Advanced
Map
Cancel
Go!
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Chapter 5. Creating Air Quality Grids
^ Maximum Neighbor Distance (in km) specifies the maximum distance that a monitor may
be from a grid cell, and still be included in the set of neighbor monitors used to calculate air
pollution exposure at a particular grid cell. The default setting is infinite (i.e., no limit to the
distance between a monitor and the grid cell).
^ Maximum Relative Neighbor Distance specifies the maximum ratio for the distance of each
monitor to the distance of the closest monitor. The default setting is infinite.
^ Weighting Approach specifies whether BenMAP should use inverse-distance weighting for
the monitors, or inverse-distance-squared weighting of the monitors. The default setting is
inverse-distance weighting.
The following examples illustrate how varying these options affects the final average
concentration estimate.
Example 1: Monitor Direct VNA method
Default options
Consider the following example at an hypothetical rural grid cell, where there are relatively few
monitors, and where the distance from a monitor to the grid cell can be fairly large. With VNA,
BenMAP first identifies the set of "neighbor" monitors for each grid cell. The number of
neighbors is usually in the range of about three to eight. In this case, assume that there are five
monitors at distances of 25, 50, 100, 200, and 400 miles from the grid cell, with annual PM2 5
levels of 8, 13, 12, 18, and 15 |ig/m \ respectively. BenMAP would calculate an inverse-distance
weighted average of the monitor values as follows:
Example 2: Monitor Direct VNA method
Maximum Neighbor Distance = 75
Using the same example that we used above, let us say you have specified a Maximum
Neighbor Distance of 75 miles, and left unchanged the default options (infinite value) for
Maximum Relative Neighbor Distance. BenMAP would only consider the first two monitors,
and would calculate an inverse-distance weighted average of the monitor values as follows:
PM25 average
= 10.68
11111
25 + 50 + 100 + 200 + 400
PM2 5 average = = 9.67
25 T 50
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Chapter 5. Creating Air Quality Grids
Example 3: Monitor Direct VNA method
Maximum Relative Distance = 10
Alternatively, if you have specified that the Maximum Neighbor Distance is infinite, but the
Maximum Relative Neighbor Distance should have a value of, say, 10, then BenMAP would
calculate the ratio of the distance for each monitor to distance of the closest monitor. In this case,
the ratios would be 1 (=25/25), 2 (=50/25), 4 (=100/25), 8 (=200/25), and 16 (=400/25), and
BenMAP would drop the monitor with a ratio of 16. BenMAP would then calculate an inverse-
distance weighted average of the monitor values as follows:
111 1
-•8+—-13+ 12+ 18
25 50 100 200
rM25 average = jjjj = 10.53
25 + 50 + TOO + 200
Example 4: Monitor Direct VNA method
Inverse-distance squared neighbor scaling
In addition, you can specify the an inverse-distance-squared weighting of the monitors. Let us
say that you have left unchanged the defaults (infinite values) for Maximum Neighbor Distance
and Maximum Relative Neighbor Distance, and specified that the Weighting Approach is
inverse-distance-squared. BenMAP would then calculate an inverse-distance-squared weighted
average of the monitor values as follows:
1111 1
-8+ 13+ 12+ 18+ 15
625 2,500 10,000 40,000 160,000
PM2 5 average j j j j j = 9.26
- +
625 2,500 10,000 40,000 160,000
Example 5: Monitor Direct VNA method
Maximum Neighbor Distance = 75
Maximum Relative Neighbor Distance = 10
Inverse-distance squared Weighting Approach
Finally, you could specify changes to all three options: a Maximum Neighbor Distance of 75
miles, a Maximum Relative Neighbor Distance of 10, and a Weighting Approach of inverse-
distance-squared weighting. BenMAP would then calculate the following average:
625 2,500
PM2 5 average = j j = 9.00
625 + 2,500
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Chapter 5. Creating Air Quality Grids
5.2.3 Other Monitor Direct options
You need to specify the Grid Type and the Pollutant. The choices for Grid Type include only
those that you have previously defined for the particular Setup in which you are working.
Similarly, the choices for Pollutant include only those that you have already defined in your
Setup. And you need to specify a source for your monitor data. In the Library tab you may
select data that you have already loaded into BenMAP.
You can also input your own monitor data
file rather than use monitor data already
existing in a dataset, so long as it is in the
format that BenMAP recognizes (defined
below in Exhibits 5-3 through 5-8). Click on
the tab that matches one of the three possible
formats for your data: (1) in a database file,
with monitor definition information and
monitor values in a single row (Database,
Rows), (2) in a text file, with monitor
definition information and monitor values in
a single line (Text File), and (3) in two
database files/tables, with monitor definition
information in rows in one file/table and
monitor values in a single column in the other
file/table (Database, Columns). There is no
preferred approach; you should use the
approach with which you are most
comfortable.
Exhibits 5-3 and 5-4 present the variables in
the data set when using the Database, Rows
format and a sample of what a data file might
look like. The Text File format is a text
(*.csv) version of the Database, Rows
format, with the exception that the Values
field must be the last field (the other fields
may be in any order) and there is no need for
double quotes at the start and the end of the
values. Exhibits 5-5 through 5-8 present the
variables in each of the data sets when using the Database, Columns format and sample of what
a data files might look like. Note that for all three formats there is not a field specifying the
pollutant with which the data is associated - instead this is specified when you load your monitor
data into BenMAP.
v Monitor Direct Settings
^jnjxj
| County
J
Interpolation Method
C Closest Monitor
Pollutant:
f* yoronoi Neighborhood Averaging
| Ozone
d
Library j Database, Columns j Database, Rows ] Text File]
Monitor DataSet:
(Philadelphia Ozone
d
Monitor Library Year:
12002
HI
Advanced
Cancel
Map
Go!
v Monitor Direct Settings
Jn]x|

Interpolation Method
I County
HI
C Closest Monitor
Pollutant:
(* yoronoi Neighborhood Averaging
1 Ozone
d
Library | Database, Columns Database, Rows | jext File ]
Monitor Data File:
Advanced
Cancel
Map
Go!
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Chapter 5. Creating Air Quality Grids
Exhibit 5-3. Air Monitoring Data File Variables - Row Format
Variable
Type
Required
Notes
Monitor Name
text
yes
Unique name for each monitor in a particular location.
Description
text
no
Description of the Monitor.
Longitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the eastern
hemisphere are positive, and those in the western hemisphere are
negative.
Latitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the northern
hemisphere are positive, and those in the southern hemisphere are
negative.
Metric
text
no
This variable is either blank (signifying that the Values are Observations,
rather than Metric values), or must reference an already defined Metric
(e.g., 1-hour daily maximum) for the appropriate Pollutant.
Seasonal Metric
text
no
This variable is either blank (signifying that the Values are not Seasonal
Metric values) or must reference an already defined Seasonal Metric for
the Metric (e.g., mean of the 1-hour maximum for the months of June
through August).
Annual Metric
text
no
This variable is either blank (signifying that the values are not annual
statistics) or must be one of: None, Mean, Median, Max, Min, Sum.
(e.g., mean of the 1-hour maximum for the year)
Values
comma
separated
values (text)
yes
If Metric is blank, either 365 or 8,760 comma separated observations
(depending on whether the Pollutant has daily or hourly observations). If
Metric is defined, but Seasonal Metric and Annual Metric are blank, 365
metric values. If Seasonal Metric is defined, but Annual Metric is blank,
n seasonal metric values. If Statistic is defined, one annual statistic value
(for either the Metric (if Seasonal Metric is blank) or the Seasonal Metric.
Mssing values are signified with a period ('.').
Exhibit 5-4. Sample Air Monitoring Data File - Row Format
Monitor Name
Description
Latitude
Longitude Metric
Seasonal Metric
Annual Metric
Values
10030010881011
Urban center near park
28.3501
77.1200

,.,.,.,.,5.90,.,.,11,.,.
10270001881011
Urban center near highway
28.4502
77.3113

19.80,.,.,3.5,.„, 14.70,.,.,.,.,., 4.9C
10331002881011
N orthern suburb
28.3524
77.6579

11.5,.,.,1.5,.,.,10.20,.,.,2.5,.,., 2. (:
10491003881011
Eastern suburb near highway
28.5432
76.4544

12.60 4.60 7.30 9.2C
10530002881011

28.351 1
77.7762
DailyAverage

5.90 11,.,.
10550010881011
Industrial area southwest of city
28.9054
77.9241
DailyAverage

19.80, .,.,3.5,. ,.,14.70,.4.9C
10690002881011

28.1123
77.3425
DailyAverage

11.5,.,., 1.5,.,., 10.20,.,., 2.5,.,., 2. c
10730023881011

28.5834
78.0121
DailyAverage

12.60,.,. ,4.60,., .,7.30,.,9.2C
10731005881011

28.2351
78.4237
DailyAverage
QuarterlyAverage

11.82,13.24,18.79,14.25
10732003881011

28.1598
77.9453
DailyAverage
QuarterlyAverage

12.31,13.89,18.27,21.19
10732006881011

28.4286
78.0042
DailyAverage
QuarterlyAverage

12.33,15.68,18.49,15.96
10735002881011

28.3469
76.8953
DailyAverage
QuarterlyAverage

13.52,13.69,19.04,22.43
10890014881011
Rural area west of city
28.3326
77.4683
DailyAverage

Mean
14.52
10970002881011
Rural area near large farms
28.9985
77.8531
DailyAverage

Mean
16.41
10972005881011
Rural area near national park
28.0014
78.9435
DailyAverage

Mean
15.62
11010007881011

28.1318
77.6289
DailyAverage

Mean
17.17
11130001881011

28.1689
77.4689
DailyAverage
QuarterlyAverage
Mean
14.29
11170006881011

27.9845
77.8492
DailyAverage
QuarterlyAverage
Mean
18.97
11190002881011

27.8934
77.1142
DailyAverage
QuarterlyAverage
Mean
20.14
11210002881011

27.9923
76.8994
DailyAverage
QuarterlyAverage
Mean
16.46
Abt Associates Inc.
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Chapter 5. Creating Air Quality Grids
Exhibit 5-5. Air Monitor Definition Variables - Column Format
Variable
Type
Required
Notes
Monitor Name
text
yes
Unique name for the monitor.
Description
text
no
Description of the Monitor.
Longitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the eastern
hemisphere are positive, and those in the western hemisphere are
negative.
Latitude
numeric
(double)
yes
Values should be in decimal degree format. Values in the northern
hemisphere are positive, and those in the southern hemisphere are
negative.
Exhibit 5-6. Sample Air Monitor Definition File - Column Format
Monitor Name
Description
Latitude
Longitude
10030010881011
Urban center near park
28.3501
77.1200
10270001881011
Urban center near highway
28.4502
77.3113
Exhibit 5-7. Air Monitoring Data File Variables - Column Format
Variable
Type
Required
Notes
Metric
text
no
This variable is either blank (signifying that the Values are Observations, rather than
Metric values), or must reference an already defined Metric for the appropriate
Pollutant.
Seasonal Metric
text
no
This variable is either blank (signifying that the Values are not Seasonal Metric
values) or must reference an already defined Seasonal Metric for the Metric.
Annual Metric
text
no
This variable is either blank (signifying that the values are not annual statistics) or
must be one of: None, Mean, Median, Max, Min, Sum.
Monitor
Name(s)
numeric
(double)
yes
Each of these field names should match up with a value in the Monitor Name field in
the Definitions file. The values will be observations, metric, seasonal metric, or
annual metric values, as determined by the values of the other fields.
Exhibit 5-8. Sample Air Monitoring Data File - Column Format
Metric
Seasonal Metric
Annual Metric
10030010SS1011
10270001SS1011
DailyAverage
QuarterlyAverage

11.82
12.31
DailyAverage
QuarterlyAverage

13.24
13.89
DailyAverage
QuarterlyAverage

18.79
18.27
DailyAverage
QuarterlyAverage

14.25
21.19
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Chapter 5. Creating Air Quality Grids
5.3 Monitor and Model Relative
The Monitor and Model Relative option lets you scale interpolated monitor values with model
data. As with the Monitor Direct option, you can choose Closest Monitor or Voronoi Neighbor
Averaging (VNA); see Section 5.2 above for a discussion of those options. In addition, you can
scale the monitor values with three different approaches: Spatial Only, Temporal Only, and
Spatial and Temporal. As discussed below, these approaches let you combine the advantage of
the actual monitor observations with the information provided by the models.
Monitor and Model Relative air quality grid creation is exactly the same as Monitor Direct air
quality grid creation (see section 5.2) with the exception of scaling. The concept of scaling is to
use modeling data to improve interpolation and/or forecast future air quality trends.
Monitor and Model Relative Settings
Grid Type:

1 County
d
Pollutant:
IOzone
d

Select Interpolation Method
0 Closest Monitor
(* Voronoi Neighborhood Averaging
Select Scaling Typer
C Spatial Only
C Temporal Only
(* jSpatial and Temporal
Library | Database, Columns ] Database, Rows | Text File ]
Monitor DataSet:
|Philadelphia Ozone
Monitor Library Year:
12002
"31
"3
Base Year Model File:
Browse
Future Year Model File:
Browse
Advanced
Map

Cancel
Go!
Identify and load your modeling data into BenMAP using the Base Year Model File box. If
temporal scaling is used, load your modeling forecasts using the Future Year Model File box.
The base year file should be created with modeling data which closely reflects the historical
conditions of the monitor data to be used. Typically multiple future year files will be used to
create multiple air quality grids for use in an analysis. That is, one future year file might
represent a future projection of current trends, while another might represent the results of
implementing a regulatory program.
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Chapter 5. Creating Air Quality Grids
5.3.1 Spatial Scaling
Spatial scaling involves only a Base Year Model File, and scales the concentrations of each
neighboring monitor by the ratio of the modeled concentration at the grid cell to the modeled
concentration at the grid cell containing the monitor. This approach takes into account what the
air quality modeling reveals about spatial heterogeneity in pollution levels. For example, if the
monitors are in relatively polluted urban areas, and the grid cell is in a relatively unpolluted rural
area, then the scaling will result in multiplying the monitor values with ratios less than one, and
thus produce lower values at the rural grid cell than would be estimated with interpolation of the
unsealed monitor data.
Spatial scaling, then, is useful because, while monitors provide invaluable information about
historical conditions, there are only a limited number of monitors. Many areas, particularly rural
areas, do not have close monitors. Model data can provide additional information that improves
the interpolated concentration estimates, and provides a more accurate picture of air quality.
5.3.2 Temporal Scaling
Temporal scaling involves both a Base Year Model File and a Future Year Model File, and scales
the concentrations of each neighboring monitor by the ratio of the modeled concentration at the
grid cell containing the monitor in the future year to the modeled concentration at the grid cell
containing the monitor in the base year. This approach takes into account what the air quality
modeling reveals about the changes in pollution levels over time at the monitor sites. For
example, if the modeling forecasts that in the future, pollution levels will decrease, then the
scaling will result in multiplying the monitor values with a ratio less than one, and thus produce
lower forecasts at the grid cell than would be obtained with the unsealed monitor data.
Temporal scaling, then, is useful because monitors cannot provide any information about future
conditions. Model data can provide this information, which can then be used to project future
monitor concentrations.
5.3.3 Spatial and Temporal Scaling
Using both spatial and temporal scaling involves both a Base Year Model File and a. Future Year
Model File, and is simply a combination of spatial scaling and temporal scaling, except that the
future year data is used for the spatial scaling. That is, it scales the concentrations of each
neighboring monitor first by the ratio of the modeled concentration at the grid cell in the future
year to the modeled concentration at the grid cell containing the monitor in the future year
(spatial scaling), and then by the ratio of the modeled concentration at the grid cell containing the
monitor in the future year to the modeled concentration at the grid cell containing the monitor in
the base year (temporal scaling). Notice, however, that the two future year concentrations at the
grid cell containing the monitor cancel out, allowing the ratio used to be simply the modeled
concentration at the grid cell in the future year to the modeled concentration at the grid cell
containing the monitor in the base year.
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Chapter 5. Creating Air Quality Grids
Using both spatial and temporal scaling gives the benefits of both approaches - it both improves
the interpolated estimates of air quality, and provides a future forecast of air quality.
5.3.4 Examples
The first example presented is a.Monitor Direct example, which will provide a foundation for the
following Monitor and Model Relative examples. Recall that Monitor and Model Relative air
quality grid creation is exactly the same as Monitor Direct grid creation with the exception of
scaling. For additional examples with more detail, see Appendix C.
Example 1: Monitor Direct VNA Method
Default options
Consider the example at an hypothetical rural grid cell, from Section 5.2.2, where there are five
monitors at a distance of 25, 50, 100, 200, and 400 miles from the grid cell. Further, let us say
that the monitors have annual PM25 levels of 8, 13, 12, 18, and 15 |ig/nr\ Without any model-
based scaling, BenMAP would calculate an inverse-distance weighted average of the monitor
values as follows:
Example 2: Monitor and Model Relative VNA Method
Default options
Spatial Only scaling
Assume the same monitors and monitor concentrations as above. Additionally, assume that the
grid cell model value in the base-year is 6 |ig/m3. and the model values at the monitors in the
base-year are: 10, 14, 11, 17, and 15 |ig/m3. (This modeling suggests that the grid cell is a less-
polluted area than the area around the monitors.) Incorporating Spatial Only scaling, BenMAP
would calculate an inverse-distance weighted average of the monitor values using the same
approach as before, with the difference being that the monitor values are scaled with the
modeling values:
PM25 average
= 10.68
11111
25 + 50 + TOO + 200 + 400
PM2 5 average =
= 5.36
25 + 50 + 100 + 200 + 400
Example 3: Monitor and Model Relative Closest Monitor Method
Default options
Spatial Only scaling
PM2 5 average = 8 — = 4.8
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Chapter 5. Creating Air Quality Grids
Assume the same monitors and monitor concentrations as above. Additionally, assume the same
model values as above. The Closest Monitor interpolation of these same values using Spatial
Only scaling would be calculated as follows:
Example 4: Monitor and Model Relative VNA Method
Default options
Temporal Only scaling
Again, assume the same monitors and monitor concentrations as above. Additionally, assume
that the model values at the monitors in the future-year are: 8, 11,9, 14, and 11 |ig/ml and that
base-year model values at the monitors are: 10, 14, 11, 17, and 15 |ig/nr\ (This modeling
suggests that the future-year model values are generally lower.) Incorporating the Temporal Only
scaling, BenMAP would calculate an inverse-distance weighted average of the monitor values as
follows:
— fa- —] + — -f i3- —) + —-(12- —) + —L fig- —] + —L.(15.ill
25 I 10) 50 I 14J 100 I IV 200 I 17J 400 I 15^
PM25 average = jjjjj = 8.52
25 + 50 + 100 + 200 + 400
Example 5: Monitor and Model Relative VNA Method
Default options
Spatial and Temporal scaling
Again, assume the same monitors and monitor concentrations as above. Additionally, assume
that the future-year model value at the grid cell is 4 |ig/nr\ and that base-year model values at the
monitors are: 10, 14, 11, 17, and 15 |ig/m3. (This modeling suggests that the future-year model
value at the grid cell is significantly lower than the base-year model values at the monitor.)
Incorporating the Spatial and Temporal scaling, BenMAP would calculate an inverse-distance
weighted average of the monitor values as follows:
— -(8-—) + — - f 13- —) + — ill- —) + — • f 18- —) + — • f 15- —
25 I lOJ 50 I 14/ 100 I \V 200 I 17^ 400 I 15,
PM25 average = jjjjj = 3.58
25 + 50 + 100 + 200 + 400
5.4 Monitor Rollback
The Monitor Rollback option allows you to reduce, or "roll back," monitor data using three
methods: percentage rollback, incremental rollback, or rollback to a standard. These approaches
let you quickly test what the benefits would be from reducing historical monitor levels.
Percentage rollback reduces all monitor observations by the same percentage. Incremental
rollback reduces all observations by the same increment. Rollback to a standard lets you choose a
standard, and then reduces monitor observations so that they just meet the standard. Note that
with each of these methods you can use the same two interpolation algorithms (closest monitor,
and VNA) as you can use with Monitor Direct and Monitor and Model Relative.
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Chapter 5. Creating Air Quality Grids
v Air Quality Grid Creation Method
JSjxJ
Choose Grid Creation Method—
C Model Direct
C Monitor Direct
C Monitor and Model Relative
(* Monitor Rollback
To apply a monitor rollback, first click the Create Air Quality Grids button. On the Air
Quality Grid Creation Method window, choose Monitor Rollback.
v Monitor Rollback Settings: (1) Select Monitors
^njxj
Pollutant:
Ozone

Library j Database, Columns ] Database, Rows | Tent File ]
Monitor DataSet:
|CityOne Monitors
Monitor Library Year:
12000
3
~E\
Rollback Grid Type:
I Counties
Cance
There are three steps to the Monitor Rollback method.
1. Monitor Rollback Settings: (1) Select Monitors. Choose the Pollutant for the monitor data.
If you use data from an existing dataset, then choose the Library tab, and from the drop-down
list choose the Monitor DataSet and the Monitor Library Year.
If you want to use your own data, then choose the format tab that matches the data that you want
to load: Database, Columns; Database, Rows; or Text File. The file should have the format
specified in Exhibits 5-3 through 5-8. Note, however, that to use Monitor Rollback grid creation
your data must have Observations - that is, it cannot simply be daily metric values (for an hourly
pollutant), seasonal metric values, or annual metric values.
Then choose the Rollback Grid Type from the drop-down list. This allows you to determine
how detailed the rollback scenario may be. If the whole region (e.g., city of Philadelphia) will
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have the same type of rollback then you may simply choose a grid outlining the area of interest.
If you are interested in different rollbacks withm a region, then you should choose a more finely
detailed grid definition (e.g., counties in Philadelphia).
When you have finished making your choices, click Next.
£ Monitor Rollback Settings: (2) Select Rollback Regions and Settings
-Inl x)
-Rollback Regions
•KKN
Add Region Delete Region |-- Region to Delete -- P Export After Rollback
Select All Deselect All
Back

2. Monitor Rollback Settings: Select Rollback Regions and Settings. In this section, you can
specify the type of the rollback method(s) that you would like to use, as well as the location of the
monitors that you want to rollback.
Choosing the Add Region button brings up the three rollback methods: Percentage Rollback,
Incremental Rollback, and Rollback to a Standard.
v Select Region Rollback Type
Rollback Type

f* Percentage Rollback]
C Incremental Rollback
C Rollback to a Standard
Cancel
OK
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After choosing the rollback type, you then need to specify the amount of the rollback and the
region to which you want to apply it. In this example, we specified a 10 percent reduction, a
background of 0 ppb, and applied it to all monitors in CityOne by clicking the Select All. So,
BenMAP will reduce all observations for all monitors in CityOne by 10 percent.
Monitor Rollback Settings: (2) Select Rollback Regions and Settings
Rollback Regions
# ©,
EH Region 1
-Rollback Parameters-
Percent: |l 0
Background: |0-00
J-- Region to Delete -¦ ~^] f" Export After Rollback
Add Region
Delete Region
Selt
Deselect All
Back
Note that just above the map of the counties in CityOne there are four buttons, typically seen in
mapping programs, that allow you to zoom in and zoom out, and to focus on the particular groups
of grid cells that interest you.
At any time you can change the grid cells that you have selected. This particular example is quite
simple, so we will use a more complicated example below. After choosing the counties where
you want to rollback monitors, click on the Next button. BenMAP will then perform the rollback
you specified on the monitors in the grid cells that you have chosen.
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v Monitor Rollback Settings: (3) Additional Grid Sett
JnJxJ
Select Interpolation Method-
f Closest Monitor
jVoronoi Neighborhood Averaging
"Select Scaling Method
<•" None
SpatialOnly
Grid Type: I 3
Adjustment Frlfe| |: Browse |
Make Baseline Grid (in addition to Control Grid).

Advanced
Map

Back
Go!
3. Monitor Rollback Settings: Additional Grid Settings.
The third stage is similar to the Monitor Direct grid creation method. As in Monitor Direct, you
need to specify the Interpolation Method (ClosestMonitor or VNA) and the Grid Type. You
may select a Scaling Method (None or Spatial Only). If you choose spatial scaling, it works
exactly as described in Section 5.3.1, where the modeling data is used to provide information in
those areas that are unmonitored.
By checking the Make Baseline Grid (in addition to Control Grid) you may create a baseline grid
at the same time as the control grid. The baseline grid uses the same parameters as the control
grid, with the exception of the rollback. That is, the baseline uses the same monitor data,
interpolation method, scaling (if any), and the same grid type. The two resultant grids can then
serve as both baseline and control scenarios in later stages of an analysis.
Note that there is an Advanced button that lets you select the Maximum Neighbor Distance,
Maximum Relative Neighbor Distance, and Weighting Approach. The specific availability of
advanced features depends on the interpolation method that you choose. You may also click the
Map button to view the inputs to the rollback grids that you are creating, as well as to view the
grids themselves. Chapter 8 discusses the mapping of grids in more detail.
5.4.1 Example: Combining Three Rollback Approaches in Different Regions
BenMAP allows you to have different rollback approaches in different regions. In this example,
we combine the three rollback types: Percentage Rollback, Incremental Rollback, and Rollback
to a Standard. As in previous example, start by clicking on the Create Air Quality Grids
button, and choosing Monitor Rollback. On the Monitor Rollback Settings: (1) Select
Monitors window, select your pollutant {Ozone) and year (2000), and click Next. On the next
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window, click the Add Region button and enter 10 for the Percent. In the previous example, we
used the Select All button to include all counties in the rollback region. In this example we want
to create three regions instead, so just click on the county in the lower left to add it to the region.
The county you have added to the region will turn yellow, as in the picture below.
55 Monitor Rollback Settings: (2) Select Rollback Regions and Settings
Rollback Regions
• ©,
I I Region 1
-Rollback Parameters-
Percent: |1C|
Background: |0-00
|- Region to Delete -¦ V Export After Rollback
Add Region
Delete Region
Select All
Deselect All
Back
To add counties with a second type of rollback, click on the
Add Region button, choose the rollback type, and then
click on the counties to include in this second region, which
BenMAP denotes as Region 2. In this example, we have
chosen an Incremental Rollback of 5 and a background of
20, and applied it to the upper right county.
The map now depicts two rollback regions. We can toggle
back and forth between each region by clicking on the
legend 011 the left side of the map. Any counties that have
not yet been included in a region may be added to an
existing region, or we may create one or more regions for
these remaining counties. Note that once counties have
been included in a rollback region, they cannot be included
in a different rollback region. In our example, the lower
left county is highlighted in dark gray.
Add a county to a region by
clicking on it so that it is
highlighted.
Remove a county from a
region by clicking on it again.
Add multiple regions using
the Add Region button.
Change the displayed region
by clicking on the region
name in the legend in the left-
hand panel.
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^ Monitor Rollback Settings: (2) Select Rollback Regions and Settings
^iojxj
Rollback Regions
EH Region 2
¦Rollback Parameters-
Increment: |5
Background: 20
Region 1
¦Rollback Parameters-
Percent: 10
Background: 0.00
•KKq
tj
Add Region Delete Region I- Region to Delete -- T~ Export After Rollback
Select All
Deselect All
Back
Next
To add a third rollback type covering the rest of the counties, click
again on the Add Region button, and then choose the rollback type.
However, instead of choosing individually the counties, simply click
the Select All button. This will select all of the counties that are not
yet included in a region, and these remaining counties will now
become Region 3.
Once you have added
counties to a region, the
Select All button will only
add the remaining counties
to the current region. If
you want to add all
counties, you must first
delete the previous region.
In this third region, we have chosen a Rollback to a Standard, which
involves two groups of parameters - those associated with the
Attainment Test, which determines whether a monitor is in
attainment (meets the standard), and those associated with the
Rollback Methods, which are used to bring out-of-attainment monitors into attainment.
The Attainment Test parameters are Metric, Seasonal Metric, Metric Statistic, Ordinality,
and Standard. A monitor is considered in attainment if the rih highest value of a daily metric
specified by Metric is at or below the value specified by Standard, where n is the value specified
by Ordinality. For example, if Metric is DSHonrMax, Ordinality is four, and Standard is
eighty-five, a monitor will be considered in attainment if the fourth highest value of the eight-
hour daily maximum is at or below eighty-five ppb. In this step BenMAP calculates the metric to
be used to determine whether a monitor's values must be rolled back and, if so, how much (e.g.,
if Metric is DSHonrMax, BenMAP calculates the 8-hour daily maximum for each day at each
monitor).
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The Attainment Test can also be used for seasonal metrics (by choosing previously defined
seasonal metrics from the drop-down list below Seasonal Metric), as well as for annual metrics
(by using the drop-down list below Metric Statistic). For example, if you want the annual mean
ozone level to go stay below 60 ppb, then you would choose the daily 24 hour mean
(1)241 lourMean) from the drop-down list below Metric, choose Mean from the drop-down list
below Metric Statistic, and set the Standard to 60. (Note that in this case Ordinality cannot be
chosen because there is only a single annual value.)
^ Monitor Rollback Settings: (2) Select Rollback Regions and Settings

Rollback Regions
| Region 3
¦Attainment T esl-
Metric:
]D8HourMax
Seasonal Metric:
"3
"3
Metric Statistic:
I-- None (use daily values) - ~^]
Ordinality: Standard:
85
4th Highest value of DSHourMax <=85
¦Rollback Methods-
Interday Rollback Method.. Background:
| Percentage |0.00
Intraday Rollback Method, Background:
| Percentage |0.00
J Region 2
Rollback Parametersr-
Incrernent: 5
Background: |2Q
Region 1

Add Region Delete Region J-- Region to Delete -- I- Export After Rollback
Select All Deselect All
Back
Next
The Rollback Method parameters are Interday Rollback Method, Interday Background
Level, Intraday Rollback Method, and Intraday Background Level. These four parameters
determine the rollback procedures used to simulate out-of-attainment monitors coming into
attainment. The Interday Rollback Method and Background Level are used to generate target
values for the metric specified by the Attainment Test. The Intraday Rollback Method and
Background Level are used to adjust hourly observations to meet the target metric values
generated in the previous step.
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BenMAP provides several types of Interday Rollback Methods (Percentage, Incremental,
Quadratic, and Peak Shaving) and several types of Intraday Rollback Methods (Percentage,
Incremental, and Quadratic). The methods involved for each can be somewhat complicated, so
we have included a section in Appendix A that goes through several examples.
5.5 Questions Regarding Creating Air Quality Grids
This section answers some common questions that may arise in the creation of air quality grids.
^How can I generate a map and export it?
This is explained in Chapter 8.
^For the Rollback to a Standard option, why are there Interday and Intraday rollback options?
This is explained in Appendix B.
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CHAPTER 6
In this chapter...
Use the Create and Run
Configuration button to specify
various options for calculating
incidence results.
Create and
Run
Configurations
Learn about baseline and
control scenarios.
Learn the difference between
Point Mode and the Latin
Hypercube option.
Select Concentration-
Response (C-R) functions.
>- Run and save a configuration.
Chapter Overview
6.1 Create New Configuration 6-1
6.1.1 Configuration Settings 6-2
6.1.2 Selecting C-R Functions for Configuration 5-4
6.1.3 Running the Health Effects Incidence
Configuration 6-8
6.2 Open Existing Configuration 6-8
6.3 Questions Regarding Configurations 6-9
-------
6. Create and Run Configurations
A configuration is a record of the choices you make in estimating the change in adverse health
effects between a baseline and control scenario. The choices include the following:
^ The air quality grids for the baseline and control scenarios;
^ The year for the analysis;
The threshold for the analysis;
^ Whether the analysis will focus on a single "point" estimate (Point Mode), or a range of
results that mirror the variability in the inputs to the health impact functions (Latin Hypercube
Points); and,
^ The concentration-response (C-R) functions to be used in
estimating adverse health effects.
Once these choices are made, they can be saved in a
configuration file for future reuse. BenMAP gives you flexibility
in the creation, editing, and saving of configuration files. You
can open an already existing configuration, and proceed directly
to the estimation of incidence. Or, you can create a new one,
and proceed with the incidence estimation. In addition, you may
save any edits made to existing or new configuration files.
BenMAP saves configuration files with a "".cfg" extension. After
calculating the change in adverse health effects, BenMAP saves
the results in a "configuration results" file with a "".cfgr"
extension.
A Health Impact Function
calculates the change in adverse
health effects associated with a
change in exposure to air
pollution. A typical function has
inputs specifying the air quality
metric and pollutant, population
characteristics, and the
incidence rate of the health
effect. The Incidence Rate gives
the average number of adverse
health effects per person per
year.
After clicking the Create and Run Configuration button, you will be asked if you want to create
a new configuration, or open an existing one, if you have already created one that you would like
to use again. Select the desired option and click Go!. Below, we discuss the subsequent steps for
each option.
v Configuration Creation Method
C Create New Configuration
C Open Existing Configuration

Go!
Cancel
6.1 Create New Configuration
If you choose to create a new configuration, there are two steps. First, fill out the Configuration
Settings form with the following options: the air quality grids for the Baseline File and the
Control File, the Population DataSet, Population Year, Threshold, and whether BenMAP will
run in Point Mode or use the Latin Hypercube Points option. In the second step, specify the
health impact functions that you want to use in your analysis.
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v Configuration Settings
"Select Air Quality Grids-
Baseline File:
^JnjxJ

Open
Create
Control File:

~ pen
Create
Map Grids
"Settings-
Latin Hypercube Points:
Population DataSet:
"3
"3
Population Year:

Run In Point Mode:
•if":
Threshold:
Cancel
Previous
Next
6.1.1 Configuration Settings
The Configuration Settings window opens after you select Create New Configuration and click
Go!. In this window, you first specify the air quality grids for the Baseline File and Control
File. The baseline file contains the air quality metrics for the scenario assumed to occur without
any change in policy. The control file specifies the air quality metrics assuming that some type of
policy or change has been implemented. The air quality grids should be of the same pollutant,
and should also be based on the same grid-type. If you choose a particular grid type (e.g.,
County) for the baseline file, then the same grid type must be used in the control file. Conversely,
it would not possible to use County grid-type in the baseline and a Tract grid type in the control
file.
You may choose existing air quality grids, by clicking the Open button, and selecting an air
quality grid, which is designated with an "".aqg" extension. You may also create a new air quality
grid by clicking the New button. This will take you to window for Air Quality Grid Creation
Method, where you follow the same steps outlined in Chapter 4 for air quality grid creation.
The Pollutant present in the air quality grids you choose determines the suite of health impact
functions available for the configuration. That is, only those health impact functions associated
with the pollutant in the air quality grids will be available for the configuration. Furthermore, if
only certain Metrics associated with the pollutant are present in one (or both) of the air quality
grids (this see the information on monitor and model data formats above for more information on
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how this can occur) only those health impact functions associated with those metrics will be
available.
In choosing the Population DataSet and Population Year, you specify the population data that
will be used in the health impact function. The values in the menu for the Population Year
depend on the range of values in your Population DataSet.
Configuration Settings
-Select Air Quality Grids-
Baseline File:

[ssociates lnc\BenMAP 2.(Mir Quality Grids\PM2.5 Philadelphia County Baseline VNA.aqg
~ pen
Create
Control File:

Is lnc\BenMAP 2.(Mr Quality Grids\PM2.5 Philadelphia County 10 Pet Rollback VNA.aqg
Open
Create
Map Grids
Settings-
Latin Hypercube Points:
I d
Population D ataS et: Population Year:
[Philadelphia Tract |2000 ~~
Run In Point Mode:
p- Warning: when running in point mode, some pooling options will be unavailable at later
stages of processing (e.g. Random / Fixed effects).
Threshold:
1°
Cancel
Previous:
Next
The Threshold indicates the minimum value that may be used in either the baseline or control air
quality metrics. That is, air quality metrics below the threshold will be replaced with the
threshold value. With a threshold of zero, there is no impact on the estimates generated by the
health impact functions. However, as the threshold increases, then it will have a progressively
larger impact on the incidence estimation. For most analyses, a threshold of zero is appropriate,
as there is little evidence suggesting the existence of a threshold. For particulate matter, a review
of the recent literature (Rossi et al., 1999; Daniels et al., 2000; Pope, 2000; Schwartz, 2000c)
found that PM-related health effects occurred down to the lowest measured levels. Nevertheless,
the Threshold option allows you to explore the impact of any given threshold on the incidence
estimation. This is also useful for scenarios where you might want to know the incidence
associated with changes in air quality occurring above a standard.
The Point Mode and Latin Hypercube Points options allow you to generate an average
incidence estimate, or a range of results that mirror the variability in the inputs to the health
impact functions. With the Point Mode option, BenMAP uses the mean values of the inputs to
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the health impact functions, and generates a single "point estimate" of the change in adverse
health effects.
With the Latin Hypercube Points option, you can generate a number of estimates that mirror the
variability in the inputs to the health impact functions. The Latin Hypercube Points option
allows you to generate specific percentiles along the estimated incidence distribution. For
example, if you specify 20 points, then BenMAP will generate estimates of the 2.5th percentile,
7.5th percentile, and so on, up through the 97.5th percentile. The number of points suggested in
the drop down menu varies between 10 and 100. In addition, you can simply type in the desired
number of points. The greater the number of chosen points, the greater the time needed by
BenMAP to process the results. The relationship between the number of points and time needed
is essentially linear, so a doubling of the number of points would double the processing time.
If Point Mode is chosen, the number of Latin Hypercube
Points cannot be modified and will be ignored (treated as
zero). However, with the Latin Hypercube Points option, a
point estimate will still be generated. As discussed in Chapter
6 on Aggregation, Pooling, and Valuation, by choosing the
Point Mode, you have fewer pooling options. You cannot
conduct fixed/random effects pooling, nor any other procedure
that depends on knowing the distribution, or the range of variability of the incidence estimates.
6.1.2 Selecting Health Impact Functions for Configuration
The second step in creating a configuration is to select the health impact functions. After filling in
both the Select Air Quality Grids and Settings of the Configuration Settings window, click
Next to select the health impact functions.
Pooling refers to combining of
different sets of data. BenMAP
has several pooling methods to
choose from.
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^ Configuration Settings
Available CR Functions:
a:
Tree
Data
-
DalaSet
Endpoint Group
Endpoint
Metric
Seasonal Metric
Metric Statistic

B
Philadelphia Def

Asthma Exacerbatio

—

Hospital Admissions

Lower Respiratory S

Mortality

-I
4
!

Cancel
Previous
Run
The Configuration Settings window is split into two display boxes. The upper box presents the
Available C-R Functions which you may select and the lower box the Selected C-R Functions
that you have already selected. Both boxes have a tree structure and the ability to change the
order of the fields for easy viewing of the functions.
To add studies to your configuration, simply highlight the health impact functions of interest and
drag them over to the lower portion of the window. You can do this for blocks of health impact
functions by dragging over an endpoint group or an endpoint, or drill down and drag individual
functions.
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Available CR Functions:
Tree
Data

DataSet
Endpoint Group
Endpoint
Metric
Seasonal Metric
Metric Statistic

Acute Bronchitis

D24HourMean
QuarterlyMean
Mean

D24HourMean
QuarterlyMean
Mean
—

D24HourMean
QuarterlyMean
Mean

D24HourMean
QuarterlyMean
Mean
-I
i
~

Selected CR Functions:
Function Identification
Function Parameters
D ataS et | E ndpoint G roup | E ndpoint
Race | Gen... | Start A... | End... | Incidence ... | Prevalence ... [Variable Dat...
Philadelphia Acute B ronchitis Acute B roncl
Black \ ]8 12 Philadelphia Irl
2J
Cancel Previous Run
If you want to delete some of the health impact functions that you added to your configuration,
just highlight the studies of interest and hit the Delete key on your keyboard.
TIP; Highlighting Blocks of Health Impact Functions
To highlight blocks of health impact functions that you have added to your configuration, you may
use two approaches: (1) point your cursor at one end of the block, hold down the Shift key on your
keyboard, and then point your cursor at the C-R function on the other end of the block; or (2) hold
down the Ctrl-key on your keyboard, and point your cursor at all of the studies that you want to
highlight - this latter approach allows you to highlight discontinuous blocks of functions.
When you drag over a study, BenMAP displays the study's information in two broad categories:
Function Identification and Function Parameters. The Function Identification includes
information such as the Endpoint Group, Endpoint, Metric, Location, and other variables.
This identification information is useful when distinguishing between multiple health impact
functions. The Function Parameters include those variables that you may directly edit: Race,
Gender, Start Age, End Age, Incidence DataSet, Prevalence DataSet, and Variable DataSet..
You can drag over the same study multiple times, and then make edits, in order to be able to
calculate the impact of changes in these variables. To edit Start Age and End Age, just highlight
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the appropriate cell and type in the desired age values. Keep in mind that these age represent
inclusive age bounds, so if you type in 10 and 12 this will include all children ages ten, eleven,
and twelve years old. If you want just a single age year, then type the same year in both the Start
Age and the End Age. Note that the accuracy of the populations calculated for these age ranges
will depend on the specificity of the population data present in your selected population dataset.
Configuration Settings
Available CR Functions:
.=lnJ2i]
Tree
Data

DataSet
Endpoint Group
Endpoint
Metric
Seasonal Metric
Metric Statistic

Acute Bronchitis

D24HourMean
QuarterlyMean
Mean

D24HourMean
QuarterlyMean
Mean
1

D24HourMean
QuarterlyMean
Mean

D24HourMean
QuarterlyMean
Mean
-1
i
I
I
Selected CR Functions:
[Function Identification
[Function Parameters
DataSet
Endpoint Group
Endpoint
Race
Gen... | Start A...
End... | Incidence...
Prevalence ...[Variable Dat...
Philadelphia
Acute Bronchitis
Acute Bronclj
Black
b
12 ] Philadelphia I r

Philadelphia Acute Bronchitis
Acute Broncl
Black

12 Philadelphia I r|
jJ
Cancel
Previous
Run
At the same time you can edit the Race and Gender variables, by clicking on the appropriate cell,
and then scrolling through the drop-down menu. Similarly, if you have multiple datasets to
choose from, you can change the source of the incidence and prevalence data that is used for each
function. Use the drop-down lists in the Incidence DataSet, Prevalence DataSet, and Variable
DataSet fields.
TIP; Editing Health Impact Functions
You can edit the Start Age, End Age, Race, Gender, Incidence DataSet, and Prevalence DataSet in
the Selected CR Functions box of the Configuration Settings window. Any changes you make to
the health impact functions will affect the current configuration ONLY. If you save the current
figuration, your edited version will appear next time you open it. However, the underlying health
impact function will not change. If you want to edit health impact functions, you can create and then
edit them using the Modify Setup option in the Tools menu.
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6.1.3 Running the Health Effects Incidence Configuration
To begin the calculation of incidence for the health impact functions in the configuration, you
click the Run button on the bottom right-hand corner of the Configuration Window that has the
list of chosen health impact functions. After clicking Run, BenMAP allows you to save the
configuration, or begin the calculation. If you wish to save the configuration for future use, click
Save and specify a file with a "".cfg" extension.
w Save Configuration
JnJxJ
Ready to run configuration. If you wish to save
this configuration, click the Save button. When
ready, click OK. If you are not ready to run this
configuration, click Cancel.
Save
Cancel
OK
When ready to generate incidence estimates, click on the OK button. BenMAP then requires that
you specify a file in which to save the results, with a ".cfgf' extension.
TIP; Saving a Configuration
When you click Run at the bottom of the Configuration Settings window, you will get a prompt
that says, "Ready to run configuration. If you wish to save this configuration, click the Save button.
When ready, click OK. If you are not ready to run this configuration, click Cancel/' If you click
Save, you will be saving the configuration, i.e., the options and health impact functions that you
have selected, so that you can open the configuration and re-run it in the future. Once you are ready
to generate incidence estimates, click the OK button, and you will be prompted to save another file.
This second file is for the results of the configuration run, which you will then use for aggregation,
pooling and valuation and to generate reports.
6.2 Open Existing Configuration
To use the same settings as a previous BenMAP run, you can choose to open an existing
configuration. After clicking the Create and Run Configuration button, you simply choose
Open Configuration and click the Go! button. Once an existing configuration is open,, you can
do all the things with it that you would do with a newly created configuration - modify settings,
add and delete health impact functions, save it to a configuration file, generate results, etc.
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Chapter 6. Create and Run Configurations
6.3 Questions Regarding Configurations
Below are answers to some of the questions that may arise regarding the creation and use of
configurations.
^Can I use air quality grids based on different Grid Types in the baseline and control scenarios?
No. In any given analysis, you need to use the same Grid Type in the baseline and control
scenarios.
^Can I use air quality grids of the same Grid Type but based on different Grid Creation
Methods?
Yes. In any given analysis, you may use air quality grids made with different methods. Air
quality grids made with Model Direct, Monitor Direct, and Monitor and Model Relative may be
used interchangeably, if desired. Similarly, air quality grids made with different interpolation
methods may be compared. However, it generally is not recommended to create grids with
different methods and use them in the same analysis.
^Can I do I an analysis with multiple pollutants?
No. Currently BenMAP analyzes one pollutant at a time.
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In this chapter...
Use the Aggregation, Pooling,
and Valuation button to create a
new aggregation, pooling, and
valuation (APV) configuration.
>- Sort and pool incidence
results.
>- Learn the differences between
the available pooling methods.
Assign economic values to
incidence results.
Aggregate incidence results
and valuations.
CHAPTER 7
Aggregation,
Pooling, and
Valuation
Save and re-open APV
configurations.
Chapter Overview
7.1 Creating a New Configuration 7-2
7.1.1 Pooling Incidence Results 7-2
7.1.2 Valuing Pooled Incidence Results 7-14
7.1.3 APV Configuration Advanced Settings 7-18
7.3 Running the APV Configuration 7-19
7.4 Opening an Existing APV Configuration File 7-20
-------
7. Aggregation, Pooling, and Valuation
Once you have created a configuration results file with incidence results based on your two air
quality grids (using the Create and Run Configuration button), you can use the Aggregation,
Pooling, and Valuation button to combine the incidence results and place an economic value on
the combined results. You have two options.
^Create a New Configuration for Aggregation, Pooling, and
Valuation. You can create a new type of configuration, termed
an Aggregation, Pooling, and Valuation (APV) Configuration.
This allows you to specify whether to aggregate incidence results
at the county, state or national level, or whether to leave them at
the grid cell level. In addition, you can specify how you might
want to combine or "poof the incidence results, using a variety
of pooling options. Given the aggregated and pooled incidence
results, you then can specify how you might want to value them -
typically there are multiple valuations. These valuation results can then be further aggregated,
and these aggregated valuation results can be pooled. Having made all of your selections, you
may save this APV Configuration file (".apv") for future use, and then proceed to calculating the
results, which are stored in an APV Results file ("apvr).
^Open Existing Configuration for Aggregation, Pooling, and Valuation. You can load an
existing APV Configuration file, edit the configuration, save it with the same or a different name,
and then proceed to calculating the results.
Aggregation refers to the
summing of grid cell level results
to the county, state or national
level. Pooling refers to
combining individual incidence
results or valuations into groups.

^ APV Configuration Creation Method
Er
-|Di X.1
C Create New Configuration for Aggregation, Pooling, and Valuation,
r Open Existing Configuration file for Aggregation, Pooling, and Valuation (".apv file).
Cancel
Go!
Create Ail Quality Grids

Create and Run
Configuration

Aggregation, Pooling,
and Valuation

Create Reports
Active Setup:

| Philadelphia
J
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Chapter 7. Aggregation, Pooling and Valuation
7.1 Creating a New Configuration for Aggregation, Pooling, and Valuation
To start you need to choose a configuration results file that contains incidence estimates at the
grid cell level (created by clicking the Create and Run Configuration button, see Chapter 6).
These incidence results are in files with a * .cfgr extension, and typically stored in the
Configuration Results folder.
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Once you open this file, you can begin creating your APV configuration. You will start with
selecting and pooling your incidence results, then move on to valuation. These processes are
described in detail below. Advanced functions, including aggregation, are described in Section
7.1.3.
7.1.1 Pooling Incidence Results
After opening the configuration results file, you will find a list of Available Incidence Results
on the left-hand side of the window. The results are represented by the health impact Functions
from which they were created, and are displayed in a tree-structure with three levels, similar to
the tree-structure found in the Configuration Settings Window (see section 5.1.2). Endpoint
Groups occupy the top-most level, followed by Endpoints, and then individual health impact
Function identifiers. You will also see a pooling window at the right, where you can select
pooling options.
There are several steps to pooling your incidence results:
Step 1. Select your default Advanced options
To select your default Advanced options, click on the Advanced button. There are two values
which you will want to set at this point - the Default Advanced Pooling Method and the Default
Monte Carlo Iterations. See Step 6, below, for a detailed discussion of these values. It is
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Chapter 7. Aggregation, Pooling and Valuation
important to set them first because these default values will be applied to all incidence results
added to the pooling window (see Step 2, below) after they are set. Once they are set, click OK.
If you decide not to change them, click Cancel.
Step 2. Add incidence results to the pooling window
In the Incidence and Pooling window, you will see all the incidence results generated from your
configuration (see Chapter 5) in the left hand column. You can drag individual incidence results,
or groups of results at any level of the tree and drag them over to the pooling window. Note that
once you drag a result or group of results into the pooling window it will still be displayed on the
left side. You do not have to drag all of your incidence results over into the pooling window, but
note that only those results showing in the pooling window will be included in the pooled
incidence or valuation results.
Incidence results are displayed in the pooling window in a tree structure determined by (1) the
order of the columns, and (2) the values of the identifying variables of the health impact
Functions from which the incidence results were generated (Endpoint Group, Endpoint, etc. -
for a complete list of variables and associated descriptions, see Exhibit 8.1).
Incidence Pooling and Aggregation
¦UaM
Available Incidence Results Select Pooling Methods
~ Hospital Admissions, Re
E) HA, Chronic Lung D
Moolgavkar, 20
Moolgavkar, 20
| " Moolgavkar, 20
Moolgavkar, 20
IS HA, Pneumonia
Q Hospital Admissions, Ca
(i) HA, Congestive He<
S) HA, Dysrhythmia
El HA, Ischemic Heart
< ««_
a
Pooling Window 1
Endpoint Group
Hospital Admissions, Respiratory
Endpoint
Author
Year Location
HA, Chronic Lung Disease
Moolgavkar
2000
Low Age
Los Angeles, CA
65
75
85
Pooling Method
None
_s
-- Window to Delete --
Delete
Add
Configuration Results Filename: C:\Program FilesSAbt Associates lnc\BenMAP\Configuration Results\HospitalAdrnissionsExam | Browse
Advanced
Cancel
Each line in the pooling window represents a node in the tree structure, with each node
representing either an individual incidence result or a collection of incidence results which have
common values for their leftmost identifying variables. The tree structure is generated by
comparing the leftmost values of the incidence result's identifying variables. High level nodes in
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Chapter 7. Aggregation, Pooling and Valuation
the tree are formed when results have common values for identifying variables, and branches in
the tree occur when the values differ.
Incidence Pooling and Aggregation l-JInlk^

B Acute Bronchitis
B Acute Myocardial Infarc
B Acute Respiratory Symp
B Asthma Exacerbation
(±1 Asthma Exacerbatic
B Asthma Exacerbatic
! Vedal et al , 19$
Vedal et al.,19J
|Dstro et al., 20C
: Ostro et al., 20C
El Asthma Exacerbatic
B Asthma Exacerbatic
B Asthma Exacerbatic
B Chronic Bronchitis
B Chronic Phlegm
B Emergency Room Visits
B Hospital Admissions, Ca
A
Pooling Window 1
Endpoint Group
Endpoint Author | Low Age | High Age | Qualifier
Pooling Method
iAsthma Exacerbation
Asthma Exacerbation, Cough

None

Vedal et al.

None

0
17
<18

6
13
6-13

Ostro et al.
8
13

None

8-13; Incidence

8-13; Prevalent

B Hospital Admissions, Re
B Household Soiling Dam-!—>
1+1 Mnrtalitu
< = 1 |T|
< I |i>
-Window to Delete •• [v [ Delete ] [ Add
Configuration R esults Filename: C: \Program Files\Abt Associates 1 nc\B enMAP\Configuration R esults\AIIPM 10S tudies. cfgr [ B rowse
| Advanced | [ Cancel ] [ Next
In the above example, four incidence results have been dragged into the pooling window. Each
of the four health impact Functions has Endpoint Group Asthma Exacerbation and Endpoint
Asthma Exacerbation, Cough. Thus, the top line, or root of the tree structure, represents all four
incidence results. A branch then occurs in the tree structure, because two studies have Author
Vedal et al. while the other two have Author Ostro et al. A further branch occurs within Vedal et
al. when the LowAge of the two incidence results differs. Similarly, a branch occurs within
Ostro et al. when the Qualifier of the two incidence results differs. Once a node has only a
single incidence result, no further branching can occur.
Step 3. Sort results
After dragging incidence results into the Pooling Window, you can rearrange the order of the
columns (variables), and thus change the tree structure. To do this, click on a column and hold
the button down as you drag it to its new location. Note that Endpoint Group is always the first
column, and Pooling Method is always the last column. All the other columns can be moved.
To see how the order of the columns in the pooling window affects the tree structure, consider the
following example:
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Chapter 7. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation [- lfnlfcjj
Available Incidence Results
Select Pooling Methods
E) Acute Bronchitis
(il Acute Myocardial Infarc
E Acute Respiratory Symp
El Asthma Exacerbation
S) Asthma E xacerbatic
~ Asthma Exacerbatic
!»•• Vedal et al., 19S
Vedal et al.,19J
|Ostro et al., 20C
Ostro et al., 20C
E) Asthma E xacerbatic
E) Asthma E xacerbatic
£) Asthma E xacerbatic
E Chronic Bronchitis
El Chronic Phlegm
[£| Emergency Room Visits
E) Hospital Admissions, Ca
-
Pooling Window 1
Endpoint Group
Qualifier Endpoint Author
Low Age
Hi.
Pooling Method
Asthma Exacerbation

None

<18
Asthma Exacerbation, Cough
Vedal et al.
0
17

6-13
Asthma Exacerbation, Cough
Vedal et al.
6
13

8-13; Incidence function
Asthma Exacerbation, Cough
Ostro et al.
8
13

8-13; Prevalence function
Asthma Exacerbation, Cough
Ostro et al.
8
13

El Hospital Admissions, Re
E) Household Soiling Darn.
ITl Mnrtalitu UU
< i I a

--Window to Delete-- v [ Delete ][ Add
Configuration Results Filename: C:\Program Files'\Ab\. Associates lnc\BenMAP\Configuration Results\AIIPM10Studies.cfgr [ Browse )
[ Advanced ] ( Cancel ] [ Next
This example uses exactly the same incidence results as the previous example, but with the
Qualifier column (variable) immediately after the Endpoint Group column. Because each
result has a unique value for the Qualifier variable, the first branch results in four children, which
each represent a single incidence result.

TIP; Sorting Incidence Results Prior to Pooling
Click and hold your cursor on a column (variable name) in the Pooling Window, then drag the
column either to the right or to the left. Release your cursor when you have moved the column over
the desired location - BenMAP will then rebuild thee tree structure using the newly specified
variable order. The Endpoint Group is always on the far left-hand side and Pooling Method is
always on the right, but all of the other variables can be freely moved. Note that whenever you
rearrange the tree structure any Pooling Method values you may have selected are reset to None. It
is thus recommended that you sort your results prior to selecting Pooling Method values.
Step 4. Select pooling methods
Once the tree structure is set up in the Pooling Window, you are ready to select your pooling
methods. Essentially each pooling method involves a different method of combining input
incidence results to generate new incidence results. Results can be pooled any time a branch
occurs in the tree structure - that is, any time two or more results share common values for their
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Chapter 7. Aggregation, Pooling and Valuation
leftmost variables. BenMAP helps you to identify these spots by inserting a value of None in the
Pooling Method column at each spot where pooling is possible.
Exhibit 7-1 summarizes the different types of pooling approaches, and Appendix I provides a
detailed discussion of the approaches.
Exhibit 7-1. Pooling Approaches for Incidence and Valuation Results
Pooling
Approach
Availability
Description of Pooling Approacha
Point Mode
Latin
Hypercube
None
Sum (Dependent)
Sum
(Independent)
Subtraction
(Dependent)
Subtraction
(Independent)
Subjective
Weights
Fixed Effects
Random / Fixed
Effects
No pooling performed.
Results are summed assuming they are perfectly correlated. In Point Mode, this is
just a simple sum. In Latin Hypercube mode, BenMAP chooses the first point
from each result in the pooling and does a simple sum to generate the first point in
the pooled result, and so on for all of the points in the distribution of results.
Results are summed assuming that they are independent. A Monte Carlo
simulation is used. At each iteration, a random point is chosen from the Latin
Hypercube of each result, and the sum of these values is put in a holding
container. After some number of iterations, the holding container is sorted low to
high and binned down to the appropriate number of Latin Hypercube points.
Results are subtracted assuming they are perfectly correlated. All subsequent
results are subtracted from the first result (the highest result in the display - to
reorder results, simply click and hold a result and then drag it to its new position).
In Point Mode, this is a simple subtraction. In Latin Hypercube mode, BenMAP
chooses the first point from each result in the pooling and does a simple
subtraction to generate the first point in the pooled result, and so on for all of the
points in the distribution of results.
Results are subtracted assuming that they are independent. A Monte Carlo
simulation is used. At each iteration, a random point is chosen from the Latin
Hypercube of the first result, and then random points are chosen from the Latin
Hypercube of each subsequent result and subtracted from the first. The result is
put into a holding container. After some number of iterations, the holding
container is sorted low to high and binned down to the appropriate number of
Latin Hypercube points.
Weights are specified by the user (see Step 5, below). In Point Mode, the new
result is generated by a simple weighted sum of the input results. In Latin
Hypercube mode, the results are combined using the user specified weights with
the "Round Weights to Two Digits" Advanced Pooling Method. See Step 6
below for details.
Pooling weights are generated automatically based on the inverse variance of
each input result, with the weights normalized to sum to one. Results with a
larger absolute variance get smaller weights. Results are then combined
according to the chosen Advanced Pooling Method.
BenMAP first tests if random weights should be used. If not, BenMAP uses fixed
effects weights. If yes, the weights take into account both the variance within
each set of results and the variance between sets of results. Results are then
combined according to the chosen Advanced Pooling Method.
~
~
~
~
1 Appendix I details the different pooling approaches.
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Chapter 7. Aggregation, Pooling and Valuation
Note that some pooling methods are only available in Latin
Hypercube mode. This is because these pooling methods
attempt to combine distributions of results into new
distributions, and no distributional information is available in
Point Mode. The Pooling Method column will thus have
different values in its drop down list depending on the mode
used to generate the incidence results being pooled.
Step 5. Create additional pooling windows if needed
Within a given pooling window, you can have only one
ordering of the columns (variables). As we have seen,
however, the ordering of the columns determines the
structure of the tree used to pool results. It may thus sometimes be necessary for analyses to have
multiple tree structures to handle the various pooling trees they require. To facilitate this,
BenMAP allows additional pooling windows to be added and deleted. To open a new pooling
window, simply click on the Add button. You may do this as many times as needed to
accommodate different sort orders. You can add the same incidence results to as many different
pooling windows as you like.
As needed you can also delete a pooling window by using the Window to Delete —" drop-
down menu to identify the pooling window, and then hitting the Delete button.
Latin Hypercube is a series of
points generated by using specified
percentiles in a given distribution,
such as that of a health impact
coefficient. It is a short-cut method
designed to represent a
distribution, while at the same time
saving on computation time. Using
the Point Mode means that
BenMAP will use the mean value of
the coefficient in the health impact
function.
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Chapter 7. Aggregation, Pooling and Valuation
¦0* Incidence Pooling and Aggregation 1- lln L^I

~ Hospital Admissions, Re
12 HA, Chronic Lung D
S) HA, Pneumonia
1=1 Hospital Admissions, Ca
B HA, Congestive He;
Lippmann et al.,
1 Lippmann et al.,
Lippmann et al.,
1=1 HA, Dysrhythmia
Burnett et al., 15
I*- Burnett et al., 1J
Lippmann et al.,
Lippmann et al.,
Lippmann et al.,
S HA, Ischemic Heart
< "» i [.>]
Pooling Window 1
Endpoint Group
Endpoint Author Year Location | Low Age
Pooling Method
Hospital Admissions, Cardiovascular

None

HA, Congestive Heart Failure
Lippmann et al.
2000
Detroit, Ml

None

HA, Dysrhythmia
Lippmann et al.
2000
Detroit, Ml

None

< I 1111 1 [>
Pooling Window 2
Endpoint Group
Low Age | High Age Endpoint Author |Ye<
Pooling Method
/V
Hospital Admissions, Cardiovascular

None
v

G5
74

None

HA, Congestive Heart Failure
Lippmann et al.
200

HA, Dysrhythmia
Lippmann et al.
200

75
84

None

HA, Congestive Heart Failure
Lippmann et al.
200

HA, Dysrhythmia
Lippmann et al.
200

85
Max

None

HA, Congestive Heart Failure
Lippmann et al.
200

< | | >

Pooling Window 2j v
Delete J [ Add
Configuration Results Filename: C:\Program FilesSAbt Associates lnc\BenMAP\Configuration ResultsM-
Pooling Window 2
ospitalAdmissionsE xample. cf
Browse |

Advanced ] [ Cancel ] [ Next
Step 6. Advanced Pooling Methods, Monte Carlo Iterations
Some pooling methods have advanced options which should be set at this point. To set them,
double-click the Pooling Method column on the row for which you wish to select advanced
options. The advanced options available depend on the particular pooling method.
None, Sum (Dependent), Subtraction (Dependent). These pooling methods have no advanced
options associated with them.
Sum (Independent), Subtraction (Independent). These pooling methods have one advanced
option associated with them, Monte Carlo Iterations. As discussed in Exhibit 7-1 above and in
Appendix I, these two pooling methods involve a Monte Carlo simulation. This advanced option
specifies the number of iterations this simulation should go through in generating results. Its
initial value is set by the Default Monte Carlo Iterations value from the APV Configuration
Advanced Settings window (see Step 1, above).
Fixed Effects, Random / Fixed Effects. These pooling methods have two advanced options
associated with them - Advanced Pooling Method, and Monte Carlo Iterations. Advanced
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Chapter 7. Aggregation, Pooling and Valuation
Pooling Method can take on three different values, which are discussed next. Its initial value is
set by the Default Advanced Pooling Method value from the APV Configuration Advanced
Settings window (see Step 1, above).
Round weights to two digits. BenMAP rounds each weight to two digits (e.g. 0.73), and
then multiplies these weights by 100 to get two digit integers. Each entire distribution
(set of Latin Hypercube points) is then put into a holding container an integral number of
times, according to its integral weight. This holding container is then sorted low to high
and binned down to the appropriate number of Latin Hypercube points.
Round weights to three digits BenMAP rounds each weight to three digits (e.g. 0.732),
and then multiplies these weights by 1000 to get three digit integers. Each entire
distribution (set of Latin Hypercube points) is then put into a holding container an
integral number of times, according to its integral weight. This holding container is then
sorted low to high and binned down to the appropriate number of Latin Hypercube
points.
Use exact weights for Monte Carlo. BenMAP uses exact weights and a Monte Carlo
simulation. On each iteration of the procedure, a particular result is chosen with a
probability equal to its weight. Once a result is chosen, one of its Latin Hypercube points
is chosen at random and put into a holding container. This is done some number of times
(see Monte Carlo Iterations, below), and the holding container is then sorted low to
high and binned down to the appropriate number of Latin Hypercube points.
Monte Carlo Iterations: This drop down list is only enabled when Use exact weights for
Monte Carlo is selected as the Advanced Pooling Method. It specifies the number of
iterations the Monte Carlo simulation should be run (see above). Its initial value is set by
the Default Monte Carlo Iterations value from the APV Advanced Settings window
(see Step 1, above).
Subjective Weights. Subjective weights pooling has no advanced options associated with it - the
advanced pooling method is always Round weights to two digits (see above). Double clicking,
however, brings up a dialog through which the weights for each result can be specified.
Alternatively, if you haven't set the weights before clicking the Next button the Select
Subjective Weights window will come up automatically.
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Chapter 7. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation 1- ll.P.ljBSj

(±3 Acute Myocardial Infarction
ffl Hospital Admissions, Cardiovascular
ED Hospital Admissions, Respiratory
£2 HA, Asthma
B HA, Chronic Lung Disease
Lippmann et al., 20001 65-74
Moolgavkar, 2000165-74; ~
Lippmann et al., 2000165+; (
Moolgavkar, 2000165+; CO
Lippmann et al., 20001 75-64
Moolgavkar, 2000175-64; C(
Lippmann et al., 2000165+; (
Moolgavkar, 2000185+; CO
IS HA, Chronic Lung Disease (less {
EE) HA, Pneumonia
GS Acute Respiratory 5ymptoms
S3 Mortality
B Work Loss Days
Pooling Window 1
Endpoint Group
Author Endpoint Low Age High Age
Pooling Method

Hospital Admissions, Cardiovascular

Random / Fixed Eff>
-

Moolgavkar
HA, All Cardiovascular

5um (Dependent)

65
74

75
84

85
Max

Lippmann et al.

5urn (Dependent)

HA, Congestive Heart Failure

5um (Dependent)

65
74

75
84

85
Max

<
>
Pooling Window 2
Endpoint Group
Endpoint i Author Low Age | High Age Year
Pooling Method
Hospital Admissions, Respiratory
HA, Chronic Lung Disease

Random / Fixed Effi

Lippmann et al.

5um (Dependent)

65
74
2000

75
84
2000

65
Max
2000

Moolgavkar

Sum (Dependent)

65
74
2000

75
84
2000

85
Max
2000

< « i i>

[ \ 1

< - i i>i
- Window to Delete - v
Delete j [ Add

Configuration Results Filename: C:\Program Files V\bt Associates lnc\BenMAP\Configuration Results\ClearSkiesResuJts.cfgr
| Browse
[ Advanced ] [ Cancel ] [ Next
Note that the weights you enter need not add up to one - BenMAP will normalize them internally.
Also note that BenMAP initializes all the weights to 1 In, where n is the number of results being
pooled.
Example: Simple sorting and pooling of incidence results
If you add a single incidence result to the right-hand window, you will see just one line, and
therefore no opportunities to pool. This shown in the example below.
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Chapter 7. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation
Bi§
Available Incidence Results
Select Pooling Methods
0 HA, All Cardiovascular
Moolgavkar, 2000118-24; CO; no ICD410
Moolgavkar, 2000118-G4; CO; no ICD410
Moolgavkar, 2000125-34; CO; no ICD410
Moolgavkar, 2000 I 35-44; CO; no ICD410
Moolgavkar, 2000145-54; CO; no ICD410
Moolgavkar, 2000155-64; CO; no ICD410
Moolgavkar, 20031G5-74; CO; no ICD410
Moolgavkar, 2003165+; no I CD 410
Moolgavkar, 2003175-84; CO; no ICD410
Moolgavkar, 2003185+; CO; no ICD410
0 HA, Congestive Heart Failure
ILippmann et al., 2000165-74; 03 j
-
Pooling Window 1
Lippmann et al., 2000165+; 03
Lippmann et al., 2000175-84; 03
Lippmann et al., 2000185+; 03
Endpoint Group
Hospital Admissions, Cardiovascular
Author
Endpoint
Low Age H igh Age Pooling M ethod
M oolgavkar HA, All Cardiovascular
165 74
<
H
--Window to Delete --
Configuration Results Filename: C:\Prograrn FilesSAbt Associates lnc\BenMAP\Configuration Results\ClearSkiesResults.cfgr
If you add a second incidence result to the window whose health impact Function has the same
Endpoint Group, but a different Author and Endpoint, you will then have a tree with two items
in it. The tree branches at the point where the two health impact Functions vary - at the Author
column.
Incidence Pooling and Aggregation
UlsHs
Available Incidence Results

1
|
3 H
H
A, All Cardiovascular
Moolgavkar, 2000118-24; CO; no ICD410
Moolgavkar, 2000118-64; CO; no ICD410
Moolgavkar, 20001 25-34; CO; no ICD410
Moolgavkar, 2000 I 35-44; CO; no ICD410
Moolgavkar, 2000 I 45-54; CO; no ICD410
Moolgavkar, 20001 55-64; CO; no ICD410
Moolgavkar, 2003165-74; CO; no ICD410
Moolgavkar, 20031 65+; no I CD 410
Moolgavkar, 20031 75-84; CO; no ICD410
Moolgavkar, 2003185+; CO; no ICD410
A, Congestive Heart Failure
=
Pooling Window 1
Endpoint Group
Author Endpoint Low Age High Age
Pooling Method
Hospital Admissions, Cardiovascular

None

Moolgavkar
HA, All Cardiovascular
65
74

Lippmann et al.
HA, Congestive Heart Failure
65
74

iLippmann et al., 2000 165-74; 03 j

Lippmann et al., 2000165+; 03
Lippmann et al., 2000 175-84; 03
Lippmann et al., 2000 I 85+; 03
-Window to Delete - v [ Delete ][ Add
Configuration Results Filename: C:\ProgramFilesVtotAssociateslnc\BenMAP\ConfigurationResults\ClearSkiesResults.cfgr | Browse ]
( Advanced ]
[ Cancel ] f Next
Note that a pooling method can now be selected for the two incidence results, since a branch has
appeared. If we desired to pool these two incidence results, we would end up with a pooled result
representing two Hospital Admissions (HA), Cardiovascular incidence results.
If you now add four more incidence results to the window whose health impact Functions have
the same Endpoint Group, you will see the following:
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Chapter 7. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation [- l[n]fej|

E) H
B H
A, All Cardiovascular
Moolgavkar, 2000 118-24; CO; no ICD410
Moolgavkar, 2000118-64; CO; no ICD410
Moolgavkar, 20001 25-34; CO; no ICD410
Moolgavkar, 20001 35-44; CO; no ICD410
Moolgavkar, 2000 I 45-54; CO; no ICD410
Moolgavkar, 20001 55-G4; CO; no ICD410
Moolgavkar, 20031 65-74; CO; no ICD410
Moolgavkar, 2003 I 65+; no I CD 410
Moolgavkar, 20031 75-84; CO; no ICD410
Moolgavkar, 20031 85+; CO; no I CD 410
A, Congestive Heart Failure
Lippmann et al„ 2000 165-74; 03
Lippmann et al., 2000 165+; 03
Lippmann et al., 2000 175-84; 03
iLippmann et al., 2000185+; 03:
-
0
Pooling Window 1
Endpoint Group
Author Endpoint Low Age High Age
Pooling Method
Hospital Admissions, Cardiovascular

None

Moolgavkar
HA, All Cardiovascular

None

65
74

75
84

85
Max

Lippmann et al.
HA, Congestive Heart Failure

None

65
74

75
84

85
Max

<
5
-Window to Delete -- |v [ Delete ] [ Add
Configuration Results Filename: C:\ProgramFiles\AbtAssociateslnc\BenMAP\ConfigurationResults\ClearSkiesResults.cfgr [ Browse ]
[ Advanced ] [ Cancel ] [ Next
Now you have many pooling options. Setting aside the issue of which pooling method to choose,
there are eight different pooling options at this point, since we have three places where we can
choose to pool or not to pool.
If you choose to pool at the two spots corresponding to Endpoints {HA, All Cardiovascular and
HA, Congestive Heart Failure) you would end up with two pooled results instead of six
individual incidence results.
If you choose to pool at the first place where the Pooling Method field says None, the spot
corresponding to the Endpoint Group (Hospital Admissions), you will end up with a single
result representing all six of the original incidence results. However, you can also pool at the
other spots as well, and thereby impact the final pooled result:
If you pool at all three spots:
^First, the three Moolgavkar results are pooled to a give a single HA, All
Cardiovascular result.
^Next, the three Lippmann results are pooled to give a single HA, Congestive Heart
Failure result.
^Finally, the two results generated in the previous steps are pooled to give a single
Hospital Admissions, Cardiovascular result.
If you pool at Hospital Admissions, Cardiovascular but not at the other two spots:
^k\\ six original results are pooled to give a single Hospital Admissions,
Cardiovascular result.
If you pool at Hospital Admissions, Cardiovascular and at HA, All Cardiovascular, but not at
HA, Congestive Heart Failure:
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Chapter 7. Aggregation, Pooling and Valuation
^First, the three Moolgavkar results are pooled to a give a single HA, All
Cardiovascular result.
^The result generated in the previous step is pooled with the three HA, Congestive
Heart Failure results to give a single Hospital Admissions, Cardiovascular result.
These same principles apply no matter how many incidence results are being pooled, and
regardless of which pooling methods are selected.
Example: Using multiple pooling windows
As shown in the example above, there are many different ways to pool your incidence results.
Sometimes you may want to look at the same results in different ways, or you may just have
many results that need to be sorted by different variables. In these cases, you can open up
multiple pooling windows by clicking on the Add button.
For example, you might want to pool all results of health impact Functions by a particular author,
rather than pooling all results of health impact functions of a particular endpoint. The examples
below show the same set of incidence results, first sorted by endpoint, then sorted by author. As
you can see, the pooling options are very different.

Incidence Pooling and Aggregation | - |[

I- Moolgavkar, 20001 55-64; CO; no ICD410
j-- Moolgavkar, 20031 65-74; CO; no ICD410
I- Moolgavkar, 20031 65+; no I CD 410
|- Moolgavkar, 20031 75-84; CO; no ICD410
Moolgavkar, 20031 85+; CO; no I CD 410
Q HA, Congestive Heart Failure
I- Lippmann et al„ 2000 165-74; 03
Lippmann et al., 2000 165+; 03
j- Lippmann et al., 2000 175-84; 03
Lippmann et al., 2000 185+; 03
Q HA, Dysrhythmia
j- Lippmann et aL 2000 165-74; 03
j- Lippmann et al„ 2000 165+; 03
j- Lippmann et al., 2000 I 75-84; 03
Lippmann et al., 2000 185+; 03
0 HA, Ischemic Heart Disease
j- Lippmann et al., 2000165-74; 03; no ICD411
Lippmann et al., 2000 I 65+; 03; no ICD410
Lippmann et al., 2000175-84; 03; no ICD411
Lippmann et al., 2000 I 85+; 03; no ICD410
E Hospital Admissions, Respiratory
El Acute Respiratory Symptoms
E Mortality
El Work Loss Days
< mi I [>
V
Pooling Window 1
Endpoint Group
Endpoint | Author | Low Age High Age
Pooling Method
Hospital Admissions, Cardiovascular

None

HA, All Cardiovascular
Moolgavkar

None

65
74

75
84

85
Max

HA, Congestive Heart Failure
Lippmann et al.

None

65
74

75
84

85
Max

HA, Dysrhythmia
Lippmann et al.

None

65
74

75
84

85
Max

HA, Ischemic Heart Disease
Lippmann et al.

None

65
74

75
84

85
Max

< UN | >
-Window to Delete -- |v [ Delete ] [ Add
Configuration Results Filename: C:\ProgramFiles\AbtAssociateslnc\BenMAP\ConfigurationResults\ClearSkiesResults.cfgr [ Browse ]
[ Advanced ] [ Cancel ] [j Next |
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Chapter 7. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation [- ][~

J™ Moolgavkar, 2000155-64; CO; no ICD410
j- Moolgavkar, 20031G5-74; CO; no I CD 410
!¦¦ Moolgavkar, 2003165+; no ICD410
j- Moolgavkar, 2003 I 75-84; CO; no ICD410
'¦¦¦¦ Moolgavkar, 2003185+; CO; no ICD410
Q HA, Congestive Heart Failure
!•¦¦ Lippmann et al., 20001 65-74; 03
j - Lippmann et al., 2000165+; 03
j- Lippmann et al., 20001 75-84; 03
Lippmann et al., 2000185+; 03
HA, Dysrhythmia
j- Lippmann et al., 2000165-74; 03
j- Lippmann et al., 20001 65+; 03
j- Lippmann et al., 2000175-84; 03
Lippmann et al., 2000185+; 03
~ HA, Ischemic Heart Disease
Lippmann et al., 2000165-74; 03; no ICD41
j- Lippmann et al., 2000165+; 03; no I CD 410
Lippmann et al., 20001 75-84; 03; no ICD41
j- Lippmann et al., 2000185+; 03; no I CD 410
(+)¦ Hospital Admissions, Respiratory
l+i Acute Respiratory Symptoms
l±) Mortality
E Work Loss Days
< „„ 1 r>
-
V
Pooling Window 1
Endpoint Group
Author | Endpoint Low Age High Age
Pooling Method
Hospital Admissions, Cardiovascular

None

Moolgavkar
HA, All Cardiovascular

None

65
74

75
84

85
Max

Lippmann et al.
None ~

HA, Congestive Heart Failure

None

65
74

75
84

85
Max

HA, Dysrhythmia

None

65
74

75
84

85
Max

HA, Ischemic Heart Disease

None

65
74

75
84

85
Max

|--Window to Delete-- |v [ Delete ][ Add
Configuration Results Filename: C:\ProgramFilesWjtAssociateslnc\BenMAP\ConfigurationResults\ClearSkiesResults.cfgr [ Browse ]
[ Advanced ] [ Cancel ] [ Next
If you use two different pooling windows, each sorted as shown above, you can create results
pooled by Author, and results pooled by Endpoint.
7.1.2 Valuing Pooled Incidence Results
After you have specified your incidence pooling options you can hit the Next button and select
valuations and valuation pooling options. The Select Valuation Methods, Pooling and
Aggregation window appears after you click Next on the Incidence Pooling and Aggregation
window. This window should look quite similar to the Incidence Pooling and Aggregation
window, with tree views on the left side representing the valuation databases available, and
various pooling windows on the right side representing the selected valuations and pooling
options.
There will be one pooling window in the Select Valuation Methods, Pooling, and Aggregation
screen for each pooling window Incidence Pooling and Aggregation screen. In each pooling
window, there will be one result present for each incidence result left over after all incidence
pooling has occurred. Each of these results will be represented by a Select —" value in the
Valuation Method column.
The columns present in the Select Valuation Methods, Pooling, and Aggregation window are
determined by the incidence results left after all incidence pooling has occurred. There will be
exactly enough columns in each pooling window to represent the "least" pooled incidence result.
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Chapter 7. Aggregation, Pooling and Valuation
That is, the columns will be in the same order they were in the Incidence Pooling and
Aggregation window, but the only columns present will be those up to the level of the pooled
incidence result with the most columns left over after all pooling has occurred. Here is an
example:
y s.
Select Valuation Methods, Pooling, and Aggregation [¦ l[n]fc3l

Valuation Methods
EPA Standard Valuations
Pooling Window 1
ffl Hospital Admissions, Cardiovascular
User Valuations
Endpoint Group
Endpoint
Valuation Method Pooling Method
Hospital Admissions, Cardiovascular

None

HA, All Cardiovascular
--Select-

HA, Congestive Heart Failure
--Select-

HA, Dysrhythmia
--S elect-

HA, Ischemic Heart Disease
-Select-

| Advanced |
Cancel Previous
1 Run 1

There are several steps to take in the Select Valuation Methods, Pooling, and Aggregation
window:
Step 1. Select your default Advanced options
To select your default Advanced options, click on the Advanced button. There are two values
which you will want to set at this point - the De fault Advanced Pooling Method and the De fault
Monte Carlo Iterations. See Step 6, above, for a detailed discussion of these values. It is
important to set them first because these default values will be applied to all valuation methods
added to the pooling window (see Step 2, below) after they are set. Once they are set, click OK.
If you decide not to change them, click Cancel. If you already set these values in Step 1 of
pooling incidence results, you do not need to reset them here.
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Chapter 7. Aggregation, Pooling and Valuation
Step 2. Select your valuation methods
Valuation methods are specific to endpoint groups, and sometimes to endpoints as well. The only
valuation methods which appear in the left side tree views are those which have the same
endpoint group values as the pooled incidence results which are available to be valued. To select
a valuation method, select it in the left side tree views and drag and drop it onto the appropriate
incidence result in the pooling window. Note that BenMAP will only allow you to drop valuation
methods onto incidence results which have the same endpoint group value. For example,
BenMAP will not allow you to drop a Mortality valuation on a Hospital Admissions incidence
result. Note also that you can only drag and drop individual valuation methods, not entire groups
of them. For explanations of the various valuations, see Appendix I.
If you have added any of your own User Valuations, using the Data menu (see Chapter 8), you
can drag and drop them in the same way as the EPA Standard valuations.
Select Valuation Methods, Pooling, and Aggregation [_ ][n]lS3l

Valuation Methods
EPA Standard Valuations
Pooling Window 1
0 Hospital Admissions, Cardiovascular
0 HA, All Cardiovascular
j— COI: rned costs + wage loss I
!•••• COI: rned costs + wage loss I
=•••• COI: med costs + wage loss I
0 HA, Congestive Heart Failure
COI: med costs + wage loss I
0 HA, Dysrhythmia
Endpoint Group
Endpoint
Valuation Method Pooling Method
Hospital Admissions, Cardiovascular

None

HA, All Cardiovascular

COI: med costs + wage loss I 65-Max

HA, Congestive Heart Failure

COI: med costs + wage loss I 65-Max

HA, Dysrhythmia

COI: med costs + wage loss I O-Max

[COI: med costs + wage loss j

HA, Ischemic Heart Disease

0 HA, Ischemic Heart Disease
COI: med costs + wage loss I
bc »'i i a
User Valuations

COI: med costs + wage loss 165-Max

[ Advanced ] [ Cancel ] [ Previous j [ Run
When BenMAP runs the APV Configuration, it will generate a valuation result for each
valuation method you select by running the valuation method's valuation functions on the
incidence results for which they were selected. You do not need to select valuation methods for
every incidence result - incidence results without any valuation methods will simply be ignored
when valuation results are generated, aggregated, and pooled.
Because valuation functions have uncertainty associated with them, generating valuation results is
fairly complicated. The procedure used depends on whether the incidence results being used
were generated in Point Mode or with Latin Hypercube Points (see Chapter 5, above).
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Chapter 7. Aggregation, Pooling and Valuation
In Point Mode, BenMAP simply runs the valuation functions once using the point estimate of the
incidence result and the mean of the valuation function (see section 9.2) as inputs.
With Latin Hypercube Points, on the other hand, BenMAP generates one hundred percentile
points (from the 0.5th percentile to the 99.5th percentile) to represent the distribution of the
inputs to the valuation function. It then runs the valuation function once for each combination of
values from the incidence result, Latin Hypercube, and the hundred valuation points, putting the
results into a holding container. Finally, the holding container is sorted low to high and binned
down to the appropriate number of Latin Hypercube points, yielding a single valuation result.
Step 3. Sort results
Depending on how your incidence results were pooled, the columns in the valuation pooling
windows can be resorted in the same way as the incidence pooling window columns. This
resorting will have the same sort of impact on the tree structure of valuation results that it had on
the tree structure of incidence results. See Step 3 of Section 7.1.1, above, for more information.
Step 4. Select pooling methods
The same pooling methods are available for valuation results which were available for incidence
results. See Step 4 of Section 7.1.1, above, and Exhibit 7-1, above, for more details. You should
note that when more than one valuation method is selected for a particular pooled incidence
result, it is possible to pool the generated valuation results.
Select Valuation Methods, Pooling, and Aggregation [- IfnljSSj

Valuation Methods
EPA Standard Valuations
Pooling Window 1
Q Hospital Admissions, Cardiovascular
~ HA, All Cardiovascular
!¦¦¦• CGI: med costs + wage loss
| CDI: med costs + wage loss
¦ CDI: med costs + wage loss
0 HA, Congestive Heart Failure
COI: med costs + wage loss
1=) HA, Dysrhythmia
¦•••• COI: med costs + wage loss
~ HA, Ischemic Heart Disease
! COI: med costs + wage loss
< <¦ i [>]
User Valuations
Endpoint Group
Author Endpoint
Valuation Method Pooling Method
Hospital Admissions, Cardiovascular

Random ! Fixed Effe

Moolgavkar

COI: med costs + wage loss I 65-Max

Lippmann et al.

None

HA, Congestive Heart Failure

None

COI: med costs + wage loss I 65-Max
Sum.JPeeendenU
Sum (Independent)
Subtraction (Depender
Subtraction (Independi
Subjective Weights
Random / Fixed Effect

HA, Dysrhythmia

COI: med costs + wage loss I 0-Max

HA, Ischemic Heart Disease

COI: med costs + wage loss I 65-Max

[ Advanced ] [ Cancel j f Previous ] [ Run
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Chapter 7. Aggregation, Pooling and Valuation
Step 5. Advanced Pooling Methods, Monte Carlo Iterations
The same advanced pooling methods are available for valuation results which were available for
incidence results. See Step 6 of Section 7.1.1, above, for more details.
7.1.3 APV Configuration Advanced Settings
At any point when specifying the incidence and valuation pooling options, you may click on the
Advanced button on the bottom-left of either the window for Incidence Pooling and
Aggregation or Select Valuation Methods, Pooling and Aggregation. This button will open
the APV Configuration Advanced Settings window.
The APV Configuration Advanced Settings
window lets you choose the level of
aggregation for the incidence and the
valuation results. Since the valuation depends
on the incidence, the level of aggregation for
the valuation must equal or exceed that of
incidence. For example, county-level
incidence aggregation may be combined with
metropolitan area-level valuation aggregation,
but not vice-versa.
As described above (see Step 6 for more
details), the APV Configuration Advanced
Settings window also allows the specification
of Default Advanced Pooling Method and Default Monte Carlo Iterations values. It is
recommended that these be set before any incidence results are added to the Incidence Pooling
and Aggregation pooling windows.
The APV Configuration Advanced Settings window also allows the specification of a Dollar
Year - all valuation dollar figures will be reported in this year's dollars. And it allows for the
specification of an Inflation DataSet, if alternative inflation rates are desired. See Exhibit 9-5
for descriptions of the variables used to adjust dollar figures to different years.
Finally, the APV Configuration Advanced Settings window allows the specification of a
Random Seed. As mentioned at the beginning of this chapter (see Open an APV Results File,
above) many of the pooling methods require the generation of sequences of random numbers (e.g.
choosing a random Latin Hypercube point during a Monte Carlo simulation). Providing a
specific Random Seed value allows the user to ensure that the same sequence of random
numbers is generated as in a previous analysis, thus allowing exact results to be reproduced.
If you do not set the Random Seed for a particular run, one will be generated automatically from
the system clock (the number generated will depend on the date and time, and should change
every minute). Normally, you should not set the Random Seed value. If you need to reproduce
a specific set of results, however, the random seed used to generate previous APV Configuration
1 v APV Configuration Advanced Settings
-ln|x|
1 ncidence Aggregation: | M etropolitan Area
zl
Valuation Aggregation: Metropolitan Area
zl
Default Advanced Pooling Method: Round weights to two digits.

Default Monte Carlo Iterations: 15000
Jd
Random Seed: Random Integer

Inflation DataSet: |Default Inflation
zl
Currency Year: 12000
zl
Cancel
OK
i i
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Chapter 7. Aggregation, Pooling and Valuation
Results can be obtained from an APV Configuration Result file (*.apvr) Audit Trail Report (see
Section 7.3, below).
7.2 Running the APV Configuration
After having specified the various pooling and aggregation options, you have the opportunity to
save your configuration for future use. The file that you save has an ""apv" extension. The
configuration that you have specified is similar in idea to the configuration that you developed for
choosing health impact functions. (That configuration has a ""cfg" extension.) Both files allow
you to save choices that you have made, and re-run them at a later time.
You can save your APV configuration when you have finished making your valuation pooling
choices. Click the Run button, and then choose Save.
Save Aggregation. Pooling, and Valuation Con - nM
Ready to run Aggregation, Pooling, and Valuation
Configuration. If you wish to save this configuration,
click the Save button. When ready, click OK. If you
are not ready to run this configuration, click Cancel.
|i Save || | Cancel | [ OK
You then need to name your configuration (* .apv) file. We suggest that you save this in the
Configurations folder. When ready to generate APV Configuration results, click the OK button.
BenMAP then requires that you specify a file in which to save the results, with an "".apvr"
extension.

TIP; Naming APV Configuration and APV Configuration Results files
To keep track of your work, you may find it helpful to use the same name for your APV
Configuration (*.apv) and APV Configuration Results (*.apvr) files.
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Chapter 7. Aggregation, Pooling and Valuation
7.3 Opening an Existing Aggregation, Pooling, and Valuation (APV)
Configuration File
If you have an existing
configuration (*.apv) file, you can
open, and then edit it. If you have
only a few changes to make to an
existing configuration, it is
typically much quicker to open the
previous configuration, rather than
entering all of your choices again.
Note that the various parts of an
APV Configuration are quite
interdependent, so modifying part
of the configuration may cause other parts to be reset. For example, modifying the tree structure
for incidence pooling will cause the valuation method selection and valuation pooling tree
structure to be cleared and reset. Changing the Configuration Results Filename in the Incidence
Pooling and Aggregation window will not reset the incidence or valuation pooling trees as long
as the new file contains incidence results generated from the same health impact Functions as the
old file. This can be quite helpful for generating new APV Configuration Results from several
different Configuration Results files which were generated from different baseline / control
scenarios, but with the same set of health impact functions.
APV Configuration Creation Method
H[qM
O Create New Configuration for Aggregation, Pooling, and Valuation.
© Open Existing Configuration file for Aggregation, Pooling, and Valuation (*.apv file).
Cancel
Go!
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In this chapter...
>* Use the Create Reports button
to create incidence and valuation
reports.
Find the file formats, including
variable definitions and program
compatibility, for each report type.
>* Find out about using an Audit
Trail report to keep track of the
options and assumptions
underlying each analysis.
Chapter Overview
8.1 Incidence and Valuation Results 8-1
8.1.1 Incidence Results 8-2
8.1.2 Aggregated Incidence Results 8-4
8.1.3 Pooled Incidence Results 8-5
8.1.4 Valuation Results 8-6
8.1.5 Aggregated Valuation Results 8-8
8.1.6 Pooled Valuation Results 8-9
8.2 Raw Incidence Results 8-10
8.3 Audit Trail Reports 8-11
8.4 Questions Regarding Creating Reports 8-12
CHAPTER 8
Create
Reports
-------
8. Create Reports
There are three types of reports that you can access by clicking on the Create Reports button.
You will be asked which type of report you wish to create:
S** Incidence and Valuation Results: Raw, Aggregated, and Pooled use an Aggregation,
Pooling, and Valuation Results file (with the ".ap\ r" extension) to create report for incidence,
aggregated incidence, pooled incidence, valuation, aggregated valuation, or pooled valuation
results. These reports are comma separated values (CSV) files (*.csv) which can be read into
various spreadsheet and database programs, such as Microsoft Excel.
5*" Raw Incidence Results use a Configuration Results file (with the ".cfgr"' extension) to create
reports for incidence results. These reports are CSV files.
Audit Trail Reports provides a summary of the assumptions underlying each of five types of
files generated by BenMAP: Air Quality Grids (with the ",aqg" extension), Incidence
Configurations (with the ".efg" extension), Configuration Results (with the ".cfgr" extension),
Aggregation, Pooling, and Valuation Configurations (with the ".ap\extension), and
Aggregation, Pooling, and Valuation Results (with the " apvr' extension). These reports can
be viewed within BenMAP in an expandable tree structure, or can be exported to tab-delimited
text files.
^ Select Report Type UdM]
O Incidence and Valuation Results: Raw, Aggregated, and Pooled. (Created from x.apvr files)
O Raw Incidence Results. (Created from x.cfgr files)
O Audit Trail Reports (Created from x.aqg files, x.cfg files, x.cfgr files, x.apv files, or x.apvr files)
Cancel OK
8.1 Incidence and Valuation Results: Raw,
Aggregated, and Pooled

Cancel
OK
Using the results in the Aggregation, Pooling, and Valuation
Results file ("".apvr" extension), you can create six types of reports:
raw incidence, aggregated incidence, pooled incidence, valuation
(of the pooled incidence), aggregated valuation, and pooled
valuation. After you click on the Create Reports button and
specify your choice, you need to specify the APV Configuration
Results File that you want to use (see Chapter 6 for how to create an APV result file). You then
need to choose a Result Type. Exhibit 8-1 describes the results contained in each type of report.
Choose a Result Type
Result Type
O Incidence Results
O Aggregated Incidence Results
O Pooled Incidence Results
O Valuation Results
O Aggregated Valuation Results
O Pooled Valuation Results
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Chapter 8. Create Reports
Exhibit 8-1. Summary of the Reports Generated from APVR Files
Result Type
Description
Incidence Results
Incidence results for each health impact function at the grid-cell level or aggregated at the
county, state, or national level.
Aggregated Incidence Results
Incidence results for each health impact function aggregated to the level you specified in the
Aggregation, Pooling, and Valuation configuration file.
Pooled Incidence Results
Incidence results aggregated and pooled as you specified in the Aggregation, Pooling, and
Valuation configuration file.
Valuation Results
Valuation results for the pooled and aggregated incidence results.
Aggregated Valuation Results
Valuation results aggregated to the level you specified in the Aggregation, Pooling, and
Valuation configuration file.
Pooled Valuation Results
Valuation results aggregated and pooled as you specified in the Aggregation, Pooling, and
Valuation configuration file.
8.1.1 Incidence and Valuation Results: Incidence Results
The Incidence Results report gives you the opportunity to examine the results of each health
impact function at the grid-cell level, or aggregate them to the county, state, or national level.
Simply select the options that you desire from the four main sections of the Configuration
Results Report window: Column Selection (these include Grid Fields, C-R Function Fields,
and Result Fields), Grouping Options, Display Options, and Advanced Options. As you
modify your choices, the Preview section will be updated accordingly.
The Column Selection section allows you to choose the field names (and values) which will
appear in the report. The Grid Fields section allows the inclusion of Col and Row fields, which
can be helpful in identifying the grid-cell of a particular line in the report. These will not always
be necessary, however - for example, when results have been aggregated to the national level.
The C-R Function Fields section allows the inclusion of various fields which can be helpful in
identifying the health impact function of a particular line in the report. Almost all of the field
names have appeared previously in the preparation of a Aggregation, Pooling, and Valuation
Results file. Finally, the Result Fields section allows the inclusion of various types of results.
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Chapter 8. Create Reports
a*
Configuration Results Report
~©(S)
Column Selection
Grid Fields:
C-R Function Fields:
Result Fields:
0 Column
0 Row
D Endpoint Group
Metric
Version
0 Endpoint
Beta
Database
Pollutant
~ DistBeta
D CornpiledFunction
& Author
~ PI Beta
~ Incidence
Year
~ P2Beta
~ Incidence2
Qualifier
A
~ Prevalence
~ Location
NanneA

D Low Age
B

C High Age
~ NarneB

~ Race
C

Gender
NanneC

Other Pollutants
Function

~ Point Estimate
0 Population
Delta
0 Mean
~ Standard Deviation
~ Variance
~ Latin Hypercube Points
^Grouping Options
(~) Group by Gridcell, then by C-R function.
O Group by C-R function, then by Gridcell.
Preview
Display Options

Advanced Options—

Digits After Decimal Point:
1 @
Population Weighted Deltas: 1 1
Elements in Preview:
25 S
Aggregation Level:
Nation v

I Population
Column Row Endpoint
1
Author
Mean
Asthma Exacerbation, Asthma Attacks Whittemore and Korn 2795B1888.0
924090.2
Emergency Room Visits, Asthma
Weisel et al
V7H5M1!:!!:!!:!. U

HA.. All Respiratory
5 chwartz
18283208.0
^Liy.lJ
Lance
Exhibit 8-2 provides a summary of the variables available in this report format.
In the Grouping Options section, you can change the sorting of the results, by clicking the radio
buttons Group by Gridcell then by C-R function and Group by C-R function, then by Gridcell.
In the Display Options section, you may set the number of digits that appear after the decimal
point, and you can set the number of rows that appear in the preview window.
In the Advanced Options, you can set the level of aggregation at the grid cell (none), county,
state, and national levels. You can also choose to generate Population Weighted Deltas, which
BenMAP calculates at the national level for each health impact function by weighting the change
in the pollution metric at each grid cell with the population of the grid cell. For example, if there
are large changes in highly populated urban grid cells and relatively small changes in lightly
population rural grid cells, then the population-weighted change would reflect the large urban
changes and be relatively large.
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Exhibit 8-2. Selected Variables in the Reports Based on the APVR file
Variable
Variable Description
Col
The column of the grid cell of the result. For grid cell level results, this is the column of the grid cell. For

county and state level results, this is the state FIPS code. For national results, this is always 1.
Row
The row of the grid cell of the result. For grid cell level results, this is the row of the grid cell. For county

results, this is the county FIPS code. For state and national results, this is always 1.
Endpoint Group
The endpoint group of the result (from the health impact function and/or valuation method).
Endpoint
The endpoint of the result (from the health impact function and/or valuation method).
Author
Author of the study used to develop the health impact function associated with the result.
Year
Year of the study used to develop the health impact function associated with the result.
Location
Location of the study used to develop the health impact function associated with the result.
Qualifier
For incidence results, the qualifier is a description that uniquely identifies a health impact function when

combined with the endpoint group, endpoint, author, and year. For valuation results, the qualifier is a

description that uniquely identifies a valuation function when combined with the endpoint group, endpoint.

and age range.
Other Pollutants
Other pollutants that were simultaneously included in the original study used to develop the health impact

function associated with the result.
Metric
Air quality metric used in the health impact function associated with the result.
Function
The health impact function associated with the result.
Compiled Function
To run faster, BenMAP uses health impact functions that have already been compiled. There are

approximately 25 types of compiled in functions that are numbered starting with zero.
Version
The version of the health impact function associated with the result. Version indicates the number of times

that a particular health impact function has been included in a configuration. Typically the different

versions of a health impact function will have different population variables (Low Age, High Age, Race,

and/or Gender).
ValuationMethod
A combination of the qualifier and age range of the result (from the associated valuation method(s)).
Population
Population provides the number of persons used in the health impact function calculation.
Delta
The difference between the baseline and control scenarios for the metric used in the health impact function.

Calculated by subtracting the metric value in the control scenario from the metric value in the baseline

scenario.
Point Estimate
The point estimate for this result.
Mean
Mean of the points in the Latin Flypercube for this result.
Std Dev
Standard deviation calculated based on the points in the Latin Flypercube for this result.
Latin Flypercube
The number of percentiles depends on the number of points in the Latin Flypercube for this result.
Points

8.1.2 Incidence and Valuation Results: Aggregated
Incidence Results
The Aggregated Incidence Results Report presents the
incidence results at the aggregation level that you have
Aggregation refers to the
summing of grid cell level results
to the county, state or national
level. Pooling refers to
combining individual incidence
results or valuations into
composite results.
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Chapter 8. Create Reports
previously specified in the Aggregation, Pooling, and Valuation Configuration file. If you want
to change the level of aggregation, you need to revise your choices in the APV Configuration
Advanced Settings window (see Chapter 6).
The Column Selection section looks largely the same as in the Incidence Results Report,
except that the Population and Delta Result Fields are not available. The Grouping Options
section and Display Options section are exactly the same. The Advanced Options section no
longer exists, as the options in it do not apply to aggregated incidence results.
APV Configuration Results Report

Column Selection
Grid Fields:
C-Fl Function Fields:
Result Fields:
0 Column
0 Row
C Endpoint Group
Metric
Version
0 Endpoint
Beta
~ Database
C Pollutant
~ DistBeta
~ CompiledFunction
& Author
PI Beta
Incidence
~ Year
[ P2Beta
~ Incidence2
Qualifier
A
Prevalence
C Location
NanneA

~ Low Age
[ B

High Age
NarneB

~ Race
: c

~ Gender
NanneC

Other Pollutants
Function

Point Estimate
0 Mean
~ Standard Deviation
Variance
Latin Hypercube Points
Add Sums
Grouping Options
Display Options
® Group by Gridcell, then by C-R Function.
Digits After Decimal Point:
1
IXI
M
O Group by C-R Function, then by Gridcell.
Elements in Preview:
50
m
M
Preview
Endpoint
Column
Row
Author
Mean
Asthma Exacerbation, Asthma Attacks Whittemore and Korn 14194.0
Emergency Room Visits, Asthma
HA, All Respiratory
Hi ill Rfi'C-nirslnni
Weisel et al.
Schwartz

33.7
37.2
0
Cancel
OK
8.1.3 Incidence and Valuation Results: Pooled Incidence Results
The Pooled Incidence Results Report provides results aggregated and pooled to the level that
you previously specified in the Aggregation, Pooling, and Valuation Configuration file. This
report looks largely the same as the Aggregated Incidence Results Report, except that fewer
health impact Function Fields are available, and values for others will be blank. This is because
after pooling, only enough fields are retained to uniquely identify individual results.
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Chapter 8. Create Reports
APV Configuration Results Report
"DP]@
Column Selection
Grid Fields:
Pooled C-R Function Fields:
Result Fields:
0 Column
0 Row
W Endpoint Group
Function
J Endpoint
Version
~ Author

~ Year

Location

Low Age

~ High Age

Qualifier

Race

Gender

Other Pollutants

Metric

Point Estimate
0 Mean
Standard Deviation
Variance
0 Latin Hypercube Points
Add Sums |
Grouping Options
(~) Group by Gridcell, then by Pooled C-R Function.
O Group by Pooled C-R Function, then by Gridcell.
Preview
Display Options—
Digits After Decimal Point:
Elements in Preview:
1
1^.1
M
25
m
M
Column Row Endpoint Group
Endpoint Mean
Percentile 2.5
Percentile 7.5
Percentile 12 +>
Hospital Admissions, Respiratory
hmprnpnni Rnnm Vwik R p-?nir^fnri i
Lance
8.1.4 Incidence and Valuation Results: Valuation Results
The Valuation Results report gives you the opportunity to examine the valuation results for the
pooled and aggregated incidence results. In the example below, the incidence results were
aggregated to the state level, so in the Column field, which represents the state FIPS code, you
can see the value changing from 1 (Alabama) to 4 (Arizona).
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Chapter 8. Create Reports
APV Configuration Results Report
. | C' 1 i'
Grid Fields:
Valuation Method Fields:

Result Fields:

0! Column
0 Row

0 Endpoint Group ~ Function
~ Endpoint ~ Version
~ Author 0 ValuationMethod
Year
[J Location
Low Age
[ I High Age
Qualifier
~ Race
[J Gender
~ Other Pollutants
U Metric

Point Estimate
0 Mean
Standard Deviation
[ j Variance
E Latin Hypercube Points

Add Sums

Digits After Decimal Point:
Elements in Preview:
It!
55 S

l-Teview
Column
Row
Endpoint Group
ValuationMethod
Mean
Percentile 0.5
A
1
1
Mortality
VSL, based on range from $1 to $10 million, normal distribution.
164367804
-228305072

1
1
Mortality
VSL, based on range from $1 to $10 million. Uniform distributior
184548828
-246053728
-240078168
53286
1
1
Mortality
VSL, based on range from $1 to $10 million. Beta distribution, rr
164394128
1
1
Hospital Admissions, Resp
CO I: rned costs + wage loss I 65-Max
2678033
1
1
Emergency Room Visits, F
CO I: Smith et al. (1987) I 0-Max
10480
5850
1
1
School Loss Days
010-17
1391972
365261
1
1
Acute Respiratory Sympto
WTP: 1 day, CV studies 118-M ax
2895086
820284
1
1
Asthma Exacerbation
WTP: 1 symptom-day, Dickie and Ulery (2002) 118-Max
1044773
221077
4
1
Mortality
VSL, based on range from $1 to $10 million, normal distribution.
186764086
-234341664
4
1
Mortality
VSL, based on range from $1 to $10 million. Uniform distributior
186868160
-252558616
4
1 Mortality VSL, based on range from $1 to $10 million. Beta distribution, m 186812480
-246427072 |vj
<1 Stf 1

Cancel OK
Add Sums Button
To calculate the total value of different groups of effects, click on the Add Sums button in the
APV Configuration Results Report window. In the next window you can specify the effects
you want to sum and how you want to sum them. In the Include in Total field, simply check the
effects that you want to sum together, and then name the sum in the Valuation Sum Identifier
field. You also need to specify whether you are going to use the Dependent ox Independent sum
approach. (See Chapter 6 for a discussion of these two summing approaches.) In the
Summation Type use the drop-down menu, choose the approach that you want to use. If you
choose the Independent sum, then you also need to specify the number of Monte Carlo draws that
you want Hen MAP to use. The default is 5,000. You may repeat this procedure to generate as
many totals as you like.
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Note that the Add Sums button is only enabled for reports involving monetary valuations, not
those involving incidence estimates. Typically, incidence estimates should not be summed across
endpoint groups (for example, Mortality and Hospital Admissions, Respiratory). Within endpoint
groups, incidence estimates can be summed - you may do this in Aggregation, Pooling and
Valuation Configurations (see Chapter 6). Once results are in monetary values, however,
summing across endpoint groups can be useful in calculating aggregate benefits.
Add Valuation Sum

Pooling Window
Endpoint Group
ValuationMethod
Include in Total
1
Mortality
VSL, based on range from $1 to $10 million, r
a
1
Mortality
VSL, based on range from $1 to $10 million, L
~
1
Mortality
VSL, based on range from $1 to $10 million, E
~
1
Hospital Admissions, Respiratory
CO I: med costs + wage loss I 65-Max
0
1
Emergency Room Visits, Respiratory
CO I: Smith et al. (1997) 10-Max
0
1
School Loss Days
010-17
0
1
Acute Respiratory Symptoms
WTP: 1 day, CV studies 118-M
ax
0
1
Asthma Exacerbation
WTP: 1 symptom-day, Dickie and Ulery (200^
0

llll

i>i

Valuation Sum Identifier:

Summation Type:

Total With Mortality

Dependent v 5000
Cancel
OK

8.1.5 Incidence and Valuation Results: Aggregated Valuation Results
The Aggregated Valuation Results Report presents valuation results aggregated to the level you
specified in the Aggregation, Pooling, and Valuation configuration file. In the example below,
the valuation results are aggregated at the national level, so all of the results have the same value
(7) in the Column and Row fields. As with the Valuation Results Report, you can use the Add
Sums button to create totals with various valuation results.
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APV Configuration Results Report
uisjy
f~- i r- [ t"
Grid Fields:
Valuation Method Fields:
Result Fields:

0 Column
0 Row

@ Endpoint Group ~ Function
~ Endpoint ~ Version
~ Author 0 ValuationMelhod
~ Year
Location
L: Low Age
~ High Age
L Qualifier
~ Race
Gender
~ Other Pollutants
G Metric

~ Point Estimate
3 Mean
~ Standard Deviation
~ Variance
2 Latin Hypercube Points

Add Sums

(*) Group by Gridcell, then by Valuation Method.
O Group by Valuation Method, then by Gridcell.
Digits After Decimal Point:
Elements in Preview:
25 0

Preview
Column Row |Endpoint Group ValuationMelhod
| Mean | Percentile 0.5
1 1 Mortality VSL, based on range from $1 to $10 million, normal distributor 9765866496 -12825786363
1
1
Mortality
VSL, based on range from $1 to $10 million. Uniform distribut
t 3774171136
-13822873800
1
1
Mortality
VSL, based on range from $1 to $10 million. Beta distribution
9763701352
-13487233024
1
1
Hospital Admissions, Resp
CO 1: med costs + wage loss I 65-Max
173828443
3456038
1
1
Emergency Room Visits, R
COI: Smith et al. (1997)1 0-Max
653580
370741
1
1
School Loss Days
010-17
93317712
24487106
1
1
Acute Respiratory Symptor
WTP: 1 day, CV studies 118-M ax
203364112
57523343
1
1
Asthma Exacerbation
WTP: 1 symptom-day, Dickie and Ulery (2002) 118-Max
68019328
14337180

< UH |
m

Cancel
OK

8.1.6 Incidence and Valuation Results: Pooled Valuation Results
The Pooled Valuation Results Report presents valuation results aggregated and pooled to the
level you specified in the Aggregation, Pooling, and Valuation configuration file. (Recall that
aggregation refers to the geographic level that you have combined your results, and pooling refers
to how you have combined the results of different health impact functions / valuations.) As with
the Pooled Incidence Results Report, fewer Pooled Valuation Method Fields are available,
because only enough fields are retained to uniquely identify individual results.
In the example below, several different valuation approaches have been combined when valuing
mortality, so the Valuation Method field is blank. As with the other valuation reports, you can
use the Add Sums button to create totals with various valuation results.
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Chapter 8. Create Reports
APV Configuration Results Report
Q\nM
Column Selection
Grid Fields:
Pooled Valuation Method Fields:
Result Fields:
0 Column
0 Row
V Endpoint Group
/ ValuationMethod
Point Estimate
0 Mean
~ Standard Deviation
~ Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
(*) Group by Gridcell then by Pooled Valuation Method.
O Group by Pooled Valuation Method, then by Gridcell.
Preview
Column | R ow | E ndpoint G roup
Display Options
Digits After Decimal Point:
Elements in Preview: 25
0
1^.1
25
10
ValuationMethod
Mean
Percentile 0.5
Percentile 1.5 Percentile I
Mortality
9769579520 -13378630656 -10G4573849S
Hospital Admissions, Respiratory
173828448
3458038
345G038
3458033
Emergency Room Visits, Respiratory
653580
370741
397688
415920
School Loss Days
93317712
24487106
24487106
2448710G
Acute Respiratory Symptoms
209984112
57523848
70813816
78359616
Asthma Exacerbation
68019328
14397180
15479634
Cancel
16276288
a
OK
8.2 Raw Incidence Results
The Raw Incidence Results report gives you the opportunity to examine the results of each health
impact function at the grid-cell level, county, state, or national level. It is based on the
Configuration Results file (with the *.cfgr extension), and is otherwise identical to the Incidence
Results Report generated from the Aggregation, Pooling, and Valuation results file (with the
*.apvr extension). BenMAP includes both versions to increase the reporting flexibility for users.
(See Section 8.1.1 for a description of the options available for this report type.)
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Chapter 8. Create Reports
8.3 Audit Trail Reports
The Audit Trail Reports provide a summary of the assumptions underlying the various parts of
the analysis. You may generate an audit trail with any of the file types used in BenMAP: Air
Quality Grids (with the "".aqg" extension), Incidence Configurations (with the "".cfg" extension),
Configuration Results (with the "".cfgr" extension), Aggregation, Pooling, and Valuation
Configurations (with the "".apv" extension), and Aggregation, Pooling, and Valuation Results
(with the "".apvr" extension). The report itself has a tree structure that lets you easily find the
information that you are seeking. Below is an example of an Audit Trail Report for a
Aggregation, Pooling, and Valuation Results file.
Note that each successive step in an analysis contains a summary of its assumptions, and those of
each previous step in the analysis. For example, in the below report the assumptions of the
Configuration Results file used to generate the APV Results are present in the Configuration
Results node. Similarly, the assumptions of both the baseline and control air quality grids are
present under the Configuration Results node.
Note that you can export Audit Trail as a text file . Each branch in the tree structure will be
converted to a tab in the exported file, allowing for easy viewing in Excel, WordPad, and a
variety of other programs. Simply click on the Export button, name the file, and click Save.
Audit Trail Report UfiJB
Aggregation, Pooling, and Valuation Configuration Result: C:\Program Files^Abt Associates lnc\BenMAP\Configuration Results\chap7_o3_e
B Configuration Results: CAProgram Files^Abt Associates lnc\BenMAP_new\Configuration Results\ozone_test.cfgr
£)¦ Baseline Air Quality Grid: C:\Progrann Files Wbt Associates lnc\BenMAP_newWir Quality Grids\o3_2002_county_closest_baseline.at
E Control Air Quality Grid: C:\Prograrn FilesV\bt Associates lnc\BenMAP_newWir Quality Grids\o3_2002_county_closest_1 Opct.aqg
Latin Hypercube Points: 20
Pollutant: 03
Year: 2000
Threshold: 0.000000
+ Selected Studies
+l Advanced
(j Incidence Pooling Windows
- Pooling Window 1
-I Mortality, Mortality, Short-Term, Non-Accidental [Pooling Method: Random / Fined Effects] [Advanced Pooling Method: Round V.
H [Weight: 0.15, Mean: 3,528.29, StdDev: 1,355.66] Ito and Thurston, 1996, Chicago, IL [Pooling Method: Sum (Dependent)]
El [Weight: 0.21, Mean: 0.79, StdDev: 1,152.79] Kinney et al., 1995, Los Angeles, CA [Pooling Method: Sum (Dependent)]
El [Weight: 0.63, Mean: 1,953.77, StdDev: 670.24] Moolgavkar et al., 1995, Philadelphia, PA [Pooling Method: 9um [Depende
E) Hospital Admissions, Respiratory, HA, All Respiratory, Schwartz, 1995 [Pooling Method: Random Fined Effects] [Advanced Poc
Emergency Room Visits, Respiratory, Emergency Room Visits, Asthma, Weisel et al., 1995, New Jersey (Northern and Central), 0
School Loss Days, School Loss Days, All Cause, Chen et al., 2000, Washoe Co, NV, 6,11, 6-11, All, All, CO,PM10, OneHourDa
Acute Respiratory Symptoms, Minor Restricted Activity Days, Ostro and Rothschild, 1989, nationwide, 18, 64,, All, All, PM2.5, Ti
Asthma Exacerbation, Asthma Exacerbation, Asthma Attacks, Whittemore and Korn, 1980, Los Angeles, CA, 0, Max, All ages, Al
E Valuation Pooling Windows
< I >
Export OK
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Chapter 8. Create Reports
8.4 Questions Regarding Creating Reports
Below are answers to some of the common questions that may arise in the creation of reports.
>^When creating reports from *.cfgr and *.apvr files, why do some of the variables that I have
checked appear as blanks?
When results are pooled together, some of the identifying information for individual health
impact functions gets lost. For example, when pooling together endpoints within the same
endpoint group, such as "HA, Pneumonia" and "HA, Chronic Lung Disease" (both within
"Hospital Admissions, Respiratory"), there is no longer a unique endpoint name for the pooled
result. So, BenMAP would leave the endpoint name blank.
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In this chapter...
Learn about BenMAP's
mapping functions.
Map configuration inputs like
air quality grids.
>- Map incidence and valuation
results.
Map different variables and
modify the map display.
CHAPTER 9
Mapping
Chapter Overview
9.1 Overview of Mapping Features 9-1
9.1.1 Display Options 9-3
9.1.2 Taskbar Buttons 9-4
9.1.3 Mapping Different File Types with the Tools Menu 9-6
9.2 Viewing Maps in a BenMAP Analysis 9-11
9.3 Questions Regarding Mapping 9-17
-------
9. Mapping
BenMAP features integrated mapping capabilities which you can access at several points in the
model. The main Mapping GIS tool, available via the Tools drop-down menu in the main
window, allows you to map all types of files and data associated with an analysis, including air
quality grid files, monitor data, population data, and incidence and valuation results. In addition,
at several points from within the program, you can view data being used in an analysis. You can
view maps when filtering monitor data, creating air quality grids, and when creating incidence
configuration files (with the *.cfg extension).
9.1 Overview of Mapping Features
To access the main mapping capabilities
within BenMAP, go to the Tools drop-down
menu, and choose the Mapping GIS option.
A blank window will appear, with buttons at
the top for managing files and navigating
the map. To see the name of each button,
simply hold the cursor over it. Use the
Open a file button in the top-left corner to
choose the file (or other type of data) that
you want to view. You can view all of the
files and data underlying an analysis. Each
map that you select and load into the GIS
viewer will appear on the left-hand side of
the window as a layer. Exhibit 9-1
describes each type of map you can view.
BenMAP GIS
LllriBI

Monitors

Population

County Data

Adjustment Grid

APV Results ~

CFG Results

| Close |
Exhibit 9-1. Description of Mapping File Types
File Type
Variables Available
Source File Used
Notes
Air Quality
Annual summary values (average.
Air quality grids

Grid
median or other metric where
available) within each grid cell.
created by BenMAP
(*aqg)

Monitors
Annual summary values (average.
Select monitors
Customize the monitor filtering options or

median or other metric where
from the BenMAP
use the default settings.

available) for each monitor.
library or use your
own by loading a
file.

Population
Total population, and cross-
Internal BenMAP
Variable names for the cross-tabulations

tabulations of age and gender with
population files and
are abbreviated: for example.

race/hispanic origin within each grid
projections, based
a m 35to39 is Asian males 35 to 39

cell (for the Year and GridType you
on Census block-
years of age. Total population and single

specify).
level data.
variables are at the bottom of the list.
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Chapter 9. Mapping

File Type
Variables Available
Source File Used
Notes
County Data
Incidence rates by age group; some
household, wage, income and
employment data; total population.
At the county level.
Internal BenMAP
incidence data.
Variables are listed in alphabetical order;
like variables are not grouped together.
Adjustment
Grid
PM grids: each variable represents a
seasonal adjustment factor. Daily
average concentrations for each
season are sorted and placed in 5 bins.
The average of each bin is used as the
adjustment factor: si bl is the
average for season l's first bin.
Ozone grids: each variable represents
one of 10 decile adjustment factors.
BenMAP
adjustment files
(*.adj)
Adjustment files are created using the
Adjustment Factor Creator under the
Tools menu, or during the Air Quality
Grid creation process using the Model
and Monitor Relative option.
APV Results:
Incidence
For each incidence result, the ResultX
variable is the mean increase in cases
between the control and baseline
scenarios; the DELTA X variable is
the difference between the baseline
and control scenarios for the metric
used in the health impact function;
and the POP X variable is the
number of persons used in the health
impact function.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
Variables are as calculated between the
baseline and control scenarios before
pooling and aggregation. Each incidence
result is given a number (e.g. ResultO,
Resultl). Result variables can be
renamed in the Edit GIS Field Names
window.
APV Result:
Aggregated
Incidence
For each aggregated incidence result,
the ResultX variable is the mean
increase in cases between the control
and baseline.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
Results are aggregated to the level
specified in the APVR file. If results
were aggregated to the national level, the
US map will only show one color.
Variables can be renamed in the Edit GIS
Field Names window.
APV Results:
Pooled
Incidence
For each pooled incidence result, the
ResultX variable is the mean
increase in cases between the control
and baseline.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
Results are pooled and aggregated as
specified in the APVR file. Variables can
be renamed in the Edit GIS Field Names
window.
APV Results:
Valuation
For each valuation result, the ResultX
variable is the mean economic value
placed on the increase in cases
between the control and baseline.
You can also map any new sums
created with the Add Sums button.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
Only incidence results assigned a value in
the APVR file will appear in the variable
list. You can create new groups of results
and add their valuations together using
the Add Sums buttons. Variables can be
renamed in the Edit GIS Field Names
window.
APV Results:
Aggregated
Valuation
For each aggregated valuation result,
the ResultX variable is the mean
economic value placed on the increase
in cases between the control and
baseline. You can also map any new
sums created with the Add Sums
button.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
Values are aggregated to the level
specified in the APVR file. Variables can
be renamed in the Edit GIS Field Names
window.
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Chapter 9. Mapping
File Type
Variables Available
Source File Used
Notes
APV Results:
Pooled
Valuation
For each pooled valuation result, the
ResultX variable is the mean
economic value placed on the increase
in cases between the control and
baseline. You can also map any new
sums created with the Add Sums
button.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
Values are pooled and aggregated as
specified in the APVR file. Variables can
be renamed in the Edit GIS Field Names
window.
APV Results:
All
Variables as described above in each
report type.
Aggregation,
Pooling and
Valuation Results
files (*.apvr)
BenMAP opens each file type as a
separate layer. You can widen the left-
hand panel to see the full layer names.
CFG Results
Variables are the same as APV
Results: Incidence.
Configuration
Results files (*.cfgr)
This produces the same map and variables
as the Incidence option under APV
results. Variables can be renamed in the
Edit GIS Field Names window.
9.1.1 Display Options
After choosing the file or data type that you want to map, you must select the variable you want
to map, and how you want it displayed. Double-click on the layer name on the left-hand side of
the window. A small box will appear with the Display Options for the layer. This box allows
you to set the following options:
Variable. The drop-down menu lists all of the variables contained in the layer you chose. Select
the variable that you want to view. If you want to view multiple variables in the same map at the
same time, you must load multiple versions of the same layer. Each layer can only show one
variable at a time.
Start Color and End Color. These are the colors that represent the gradations in the selected
variable. BenMAP uses 10 equal-sized increments for the variable, with a gradual transition
between the Start Color and the End Color. To change either color, click on the colored square
and select a new color.
Default Color. This is the color is used for values that fall outside of the range of the Min Value
and the Max Value.
Min Value and the Max Value. These options define the range of
the selected variable and are automatically set to the minimum and
maximum of the variable. However, you may wish to enter other
minimum and maximum values, such as in the case where there is
one outlier that is dominating the color scale. For example, some
urban grid-cells such as those in Los Angeles have extremely large
values. Since BenMAP creates 10 equal-sized increments between
the minimum and the maximum, it is not uncommon to have most
of the grid values in just a few of the increments. If you change
^ Display Options
~@0

Variable: Total

Id
Start Color: | |
Min Value:
1627.64
End Color: H~~|
Max Value:
125779.40]
Default Color:

Grid Outline: 0
Decimal Digits:

r
Cancel
OK

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Chapter 9. Mapping
the minimum and/or maximum values to exclude outliers from the color scale, the outliers will
then appear in the Default color.
Decimal Digits. This specifies the number of digits used in displaying the results. The default is
two decimal points. However, for variables with values much smaller than one, such as some of
the incidence rate data, you will want to increase the number of digits that you display.
Grid Outline. This appears as a fine white line around the border of each grid cell. For some
grid types, this outline can make the map too complicated, and you may want to uncheck this
option. An alternative is to use the Reference Layer drop-down window, which lets you add a
blue outline for the nation, states, or counties.
Note that if you have specified point data, such as monitors, the Display Options window
includes the ability to set the size of the points on the map. The Start Size, End Size, and
Default Size have a default value of 100, which you then edit. For example, if you want smaller
values to be represented by smaller points, you might leave the End Size unchanged, and reduce
the value for the Start Size.

t Display Options
~00
Variable:
AnnualAvgj

Start Size:
100
1X1
It)
Start Color:
u
Min Value:
5.67
End Size:
100
m
hd
End Color:
¦
Max Value:
139.39
Default Size:
100
m
M
Default Color:
¦
Decimal Digits:
1 [~]
Cancel
OK
9.1.2 Taskbar Buttons
There are a number of standard buttons used in most map viewing programs which you can use to
navigate and customize the map view.
&
Open a file. Use this to open maps for viewing.
a
Save active layer to file. Creates a shape file for use in other map-viewing programs. The
active layer is the topmost visible layer.

Zoom to full extent. Allows you to view the whole map that you are viewing.
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O
f?
Chapter 9. Mapping
Q
Increase zoom and Decrease zoom. Allows you to zoom in and out.
Select a region for zooming. Allows you to select a region to view.
Drag mode. Allows you to manually move the map by clicking and dragging.
Click to display info for the cell under the mouse. Allows you to display information (all
the variable values) for individual cells or points by clicking on them.
Build Query. Allows you to view grid-cells that satisfy certain criteria. Hitting the Execute
button will produce a map of the cells that meet the criteria that you have specified. Below is
an example of how you might use this function.
Build Query

Fields:
Other
0
Hispanic
Male

Female

Total
a
!< 1 M
1 :>!
Sample Values:
<
¦ ii >
<=
ii
a.
V
Not
And Or
in
772.00
A
730.00
r~l
160.00

1322.00

979.00

22497.99

83784.95
0
T otal >= 50000
E xecute
Cancel
OK
£
Layer Statistics. Provides information about the active layer. In the Fields section simply
choose the variable of interest, and BenMAP will display statistics and sample values for that
variable.
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Chapter 9. Mapping
4*
* Layer Statistics
\nM
Fields:
Statistics:
Black
0
NatAmer
Asian

Other

Hispanic

Male

Female

T otal

V
< ^_| [>}
Mean:
Min:
Max:
StdDev:
Sum:
54109.40
0.00
7208146.00
2089G4.G0
2.795833E08
Count: 5187
Sample Values:
772.00
A
730.00
I—I
ISO. 00

1322.00

979.00

22497.99

83784.95

2888.00

810.00

25394.98
1OAOn QQ
0
OK
The drop-down menu provides alternative projections for displaying the data, with the default set
to Albers Equal Area Conic.
9.1.3 Mapping Different File Types with the Tools Menu
The Open a file button gives the option to open a variety of file and data types. All of the
options are straightforward, though some require a few more steps than others. See Exhibit 9-1
above for information about each type. Below we give some step-by-step examples for some of
the types.
Example 1: Mapping Monitor Data
You can map monitor locations and concentration levels using
the Monitors option. Click the Open a file button and select
Monitors from the drop-down menu. A small window will
appear in which you can select a Monitor Source. You can
map data that you have already stored in the Library, or you
may map your own monitor data, so long as it follows the
format in Exhibits 5-3 through 5-8.
If you map data from the Ben MAP library, you need to specify
the Pollutant, the Monitor DataSet, and the Monitor
Library Year. Click OK, and BenMAP will generate a
monitor layer that you can view.
35
Choose Monitor... [V] ~
Monitor Source
(_.) Library
O File
Pollutant:
Monitor Yean
Cancel
OK
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BenMAP GIS
UlnlB
(El § ®S ®\ Q 0 fl 0 ^ Albers Equal Area Conic v
Layers—
States
E
PM2.5, 2002
Close
In this map, each red square is a monitor location. To see the monitor values displayed with
colors varying by the level of the monitor, double-click on the layer on the left side of the map,
and follow the steps outlined above for setting the display options. Values shown using the
Monitors mapping option are annual average values (i-ig/m3 for PM and ppb for ozone).
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Chapter 9. Mapping
€? BenMAP GIS
States]
& y » ©s ©v q
Layers
Close
Albers Equal Area Conic v
PM2.5, 2002
3.96-6.31
G.31-8.EG
8.66-11.01
11.01-13.36
13.36-15.70
15.70-18.05
18.05-20.40
20.40-22.75
22.75-25.10
25.10-27.45

Example 2: Mapping County Data
You can also map county incidence rates and demographic characteristics.
Included are incidence rates by age and race in some cases, total
population and population by age, and some housing and income variables.
Select Mappingf (US from the Tools menu on the main BenMAP window.
Click the Open a file button and select County Data from the drop-down
menu. A map of the continental United States will appear, with all of the
counties filled with gray. In the Layers field on the left side of the
window, double-click on CountylncidenceData label. In the next
window, you can choose the variable of interest and your display options.
See Appendix E for more information on incidence data, including
sources.
Variables for County
Incidence Data are
shown in alphabetical
order by variable name,
not by type; for
instance, total
population is not
grouped with other
population variables.
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Example 3: Mapping Aggregation, Pooling, and Valuation Results
You can map each of the six types of results stored in AI'V Results files (*.apvr): (1) Incidence,
(2) Aggregated Incidence, (3) Pooled Incidence, (4) Valuation, (5) Aggregated Valuation, and (6)
Pooled Valuation. Finally, you can select (7) AIL if you wish to map all of the types of results
simultaneously. All of the options use the information contained in the Aggregation, Pooling
and Valuation Results files (*.apvr), which you can create using the Aggregation, Pooling, and
Valuation button on the main window.
-f Ben MAP GIS
. ~ X
Albers Equal Area Conic v
States
Air Quality Grid
Monitors
Population
County Data
Adjustment Grid
APV Results
CFG Results
18.05-20.40
20.40-22.75
22.75-25.10
25.10-27.45
i
---I
Incidence
Aggregated Incidence
Pooled Incidence
Valuation
Aggregated Valuation
Pooled Valuation
All

Close
Click the Open a file button and select APV Results and one then one of the six results types from
the drop-down menu. A window will appear with identifiers for each of the variables. On the
right-hand side is the GIS Field Name. Mapping programs typically have a default length of 10
characters for variable names, so it is necessary to specify your own names or to use the default
name given in the GIS Field Name column.
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Edit GIS Field Names

Endpoint Group
Endpoint
Pollutant
Author
Year
Qualifier
Gis Field Name
/V
0
Asthma Exacerbatio
Asthma Exacerbatio
03
Whittemore and Kor
1980
All ages
ResultO
Emergency Room V
Emergency Room V
03
Weisel et al.
1995
All ages
Resultl
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1995
65-74; New F
Result2
Hospital Admissions.
HA, All Respiratory
03
Schwartz
1995
85-74; T aeon
Result3
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1995
75-84; New F
Result4
Hospital Admissions.
HA, All Respiratory
03
Schwartz
1995
75-84; T aeon
Results
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1995
85+; New Ha
Results
Hospital Admissions.
HA, All Respiratory
03
Schwartz
1995
85+; T acoma
Result7
Acute Respiratory S
Minor Restricted Act
03
Dstro and Rothschik
1989

Result8
Mortality
Mortality, Short-Tem-
03
Ito and Thurston
199S
<18; PM10
Result9
Mortality
Mortality, Short-Terrr
03
Kinney et al.
1995
<18; PM10
ResultIO
Mortality
Mortality, Short-Terrr
03
Moolgavkar et al.
1995
<18
Result11
<
1

E
OK

For each of the three valuation options (Valuation,
Aggregated Valuation, and Pooled Valuation), a second
window appears that allows you to specify variables that you
want to add together (if you have selected Incidence, Pooled
Incidence, ox Aggregated Incidence, BenMAP will go directly
to loading the new layer).
Click on the Add Sum button. Then in the far-right column,
Include in Total, check the variables that you want to include
in the total. In the lower-left corner, give a name (no longer
than 10 characters) for the total in the Valuation Sum
Identifier, and then choose whether to use a Dependent or
Independent sum. If you choose the latter, then you also need
to choose the number of Monte Carlo draws. To finish, click OK. You may repeat this step as
many times as desired.
Valuation Slims Layer
Sum Identifier | Gis Field Name |
I Add Sum |
[ Cancel ] [ OK
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^ Add Valuation Sum - n]|S3i
Pooling Window
Endpoint Group
Endpoint
Author
Year
Location
Include in Total
1
Mortality

~
1
Mortality

~
1
Hospital Admissions, Respiratory

~
1
Emergency Room Visits, Respiratory

~
1
School Loss Days

~
1
Acute Respiratory Symptoms

~
1
Asthma Exacerbation

A
0
Valuation Sum Identifier: Summation Type: Monte i arlo Iteration?:

Total 0 Dependent v 15000 Cancel
OK

Example 4: Mapping Multiple Layers of Data
It is possible to map multiple layers simultaneously. The layer that you have opened most
recently appears on the top of the list, and in the map its values lie on top of the other layers. By
right-clicking on any given layer, you can move its position within the list (select Move Up or
Move Down). By checking the box to the left of the layer name, you can turn a layer's visibility
off and on. For instance, if you want to see the second layer in the list, simply un-check the box
next to the first layer in the list. The second layer will then be on top and be visible.
9.2 Viewing Maps in a Ben M AP Analysis
In addition to the Tools menu, BenMAP provides
several places where you can map the input data
that you are using for a particular analysis. We
provide examples of these mapping options below.
Monitor Direct Settings
~(D@

O Closest Monitor Q Kriging
i Kriging Settings |
O Voronoi Neighbor Averaging (VNA)

0 Use Library Monitor Data

Grid Type:
3
Pollutant:
3
Library M onitor Y ear: | v

Monitor Fife;
Browse j

Advanced J [ Map

Cancel J [ Go!
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Example 5: Mapping Monitor Direct Air Quality Grids
When generating air quality grids there is an option to map the grid and the data used to create it.
For example, when generating a Monitor Direct grid, click on the Map button on the Monitor
Direct Settings page.
BenMAP will then generate the grid, generate a map with both the monitors and the monitor data
interpolated to the grid cells. You can then use the display options to generate the map you
desire.
¦-
™ BenMAP GIS 1- llnltej
im ® Q, © ^ £ Albers Equal Area Conic v Reference Layer ¦¦ fv
¦- Layers
3 Monitors
3 Air quality grid
ivBUwl i>*2 14 P7jfcL_:
¦ umm M

Close
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Example 6: Mapping Air Quality Monitors from the Advanced Monitor Filter
You can access mapping through the advanced monitor filter when generating air quality grids.
Click on the Create Air Quality Grids button and choose either Monitor Direct, Monitor and
Model Relative, or Monitor Rollback. On the Settings page, click on the Advanced button,
which brings up the Advanced Options page. Choose your filtering options and click Go! Then
click the Map button, which becomes active after filtering. An initial map appears with each
monitor location identified by a red square. To immediately provide some context, you may want
to choose, say, the States reference layer.
** BenMAP GIS

Br y I ^ Ci Olf) O 1 Albers Equal Area Conic v [States
Layers
3 PM2.5,2002
J
' ¦ ft v—«—£
- if. »l
i. V"
j r .
¦ ¦»
Close
Double-click on the layer to the left of the map. Choose the display options that you would like,
and then click OK.
A map with your display options will then appear. You may then use the various mapping
options available, such as zooming in to an area of interest, getting information on particular
monitors, querying the map to display monitors with certain characteristics, and saving the map
as a shapefile and viewing it in another map viewer.
^ Display Options
Variable: AnnualAvgl j vj
Start Size: jToO gj
End Size: pfoo ^
Default Size: -| qq
Cancel | | OK
Start Color:
End Color:
Default Color:
~
Min Value:
5.67
Max Value: 139.39
Decimal Digits: 2
(XI
M
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0 Q O | O O1 S | Albers Equal Area Conic [ v States]
BenMAP GTS
Ugjsa
Layers
3
PM2.5, 2002

3.96-6.31

6.31-8.66

8.66-11.01

11.01-13.36
¦
13.36-15.70
¦
15.70-18.05

18.05-20.40
¦
20.40-22.75
¦
22.75-25.10
¦
25.10-27.45
Example 7: Mapping Monitor Rollback Inputs and Outputs
When using the Monitor Rollback option, you can generate two types of maps. First, you can
map the inputs to the rollback - the monitor data and any spatial adjustment file that you may
have used. Second, you can map the inputs and at the same time map the grid based on these
inputs. To start, click the Map button.
You will then have the option to map the inputs, or map
the inputs and the resulting grids.
In this example, BenMAP will produce a map with both
the inputs, as well as the baseline and control grids.
(The baseline grid gets created because we checked the
box for Make Baseline Grid (in addition to Control
Grid.) As before, you may use the various display
options to generate the map that you desire. Recall that
the topmost layer lies on top of the other mapped layers.
To change the ordering of the layers, simply right-click on the layer that you want to move (up or
down).
Mapping Options
Mapping Options
Map Grid Inputs (Adjustment files. Monitors).
© Map Grid Inputs and Create and Map Grid.
OK
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f Mo nito i Ro 11 bac k Setti rigs: <3) Additional Giid Settl ngs ~D@
Select Interpolation Method
0 Closest Monitor
O Voronoi Neighbor Averaging (VNA)
O Kriging
Select Scaling Method
® None
O Spatial Only
Li rid Type:
Adjustment File:
County
Browse
0 Make Baseline Grid (in addition to Control Grid).
Create
Advanced

Map

Cancel

Go!
% BenMAP GIS

9 © Q Q Q .fj1 Q "Z Albers E qual Area Conic | v | - Reference Layer -
Layers -
3 Rolled back Grid
3 Baseline Grid
3 Rolled back Monitor
3 Baseline Monitors

s
w^
14
Close
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Example 8: Mapping Air Quality Deltas
When developing a Configuration file (with the * .cfg extension), you can map the baseline and
control air quality grids, as well as the difference between the two. In the Configuration Settings
window, after choosing the grids that you want to use, click on the Map Grids button.
¦0" Configuration Settings

Select Air Quality Grids
Control File:
C:\Program FilesWrt Associates lnc\BenMAP^Air Quality Grids\Ba
~pen
Create

C:\Program FilesWrt Associates lnc\BenMAPW Quality Grids\Co
~pen
Create
Map Grids
Settings
Pollutant: |PM1Q
Population Year: 2020
Latin Hypercube Points: 110
Run In Point Mode: | |
Threshold:
0.0
Cancel
Previous
Next
This will generate layers with the delta (baseline minus control), baseline, and control values for
each grid cell.
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BenMAP GIS
mm

Albers Equal Area Conic |v -- Reference Layer -- v
Layers
^ s.
T-i ¦*
3 Delta
3 Control Grid
3 Baseline Grid

9.3 Questions Regarding Mapping
^Why is the Open a File menu button disabled?
This happened because you did not use the Tools button and choose Mapping GIS. When
viewing maps while generating air quality grids, filtering monitor data, and other activities within
BenMAP you do not have access to other types of maps. This is to avoid too many competing
activities.
^All of the mapped values have the same color. How do I avoid this?
This can happen when the values are extremely small and you have not specified a sufficient
number of decimal points. Go to the Display Options window, and change the Decimal Points.
This can also happen when one of the grid cells is an outlier, with either a very low or very small
value. You can go to the Display Options window, and change either the Min Value or the Max
Value. Finally, this can happen if you have mapped national data. In this instance, you should
expect all areas to have the same color, since there is only a single national number for display,
such as when mapping national results, or mapping incidence rates that do not vary by region
(e.g., MRAD incidence rate).
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>^Can I map air quality for individual days?
No. BenMAP only maps annual averages. In the case of hourly metrics, such as the one-hour
daily maximum for ozone, BenMAP will map the average of the metric for the available days.
^Can I print maps in BenMAP?
No. BenMAP does not currently allow printing directly from the program. However, you can
export shapefiles, and then read these shapefiles into a program that does support printing, such
as ArcView.
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