&EPA
Sited states EPA/600/R-09/143F | September 2010 | www.epa.gov
Environ mental Protection
Agency
INTEGRATED CLIMATE AND LAND-USE SCENARIOS
(ICLUS) vl.3 USER'S MANUAL:
ARCGIS TOOLS AND DATASETS FOR MODELING US
HOUSING DENSITY GROWTH
ICLUS v 1,3
1) Spatially Explicit Regional Growth Model
.!if> Basic
$ Custom
2) Population and Housing Density Tools
.§ Batch Clip ICLUS Rasters
$ Create Population Projection Rasters
.!if> Reclassify Housing Density
3) Impervious Surface Tools
.^ Estimate Percent Impervious Surface
!jj} Summarise Impervious Surface by Housing Density Class
Global Change Research Program
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental
Protection Agency policy and approved for publication. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
Preferred Citation:
U.S. Environmental Protection Agency (EPA). 2010. ICLUS vl.3 User's Manual: ArcGIS
Tools and Datasets for Modeling US Housing Density Growth. Global Change Research
Program, National Center for Environmental Assessment, Washington, DC; EPA/600/R-
09/143F. Available from the National Technical Information Service, Springfield, VA, and
online at http://www.epa.gov/ncea/global.
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CONTENTS
Contents iii
Tables iii
Figures iv
Authors and Reviewers v
1. Purpose 1
1.1 Key assumptions and uses 1
2. Software, hardware, and experience requirements 2
2.1. Technical description of SERGoM v3 3
2.2. SRES-specific parameters for SERGoM 7
3. Tools 10
3.1. Setting Up The ICLUS Tools 10
3.2. SERGoM: The Spatially Explicit Regional Growth Model 11
3.2.1 Basic SERGoM 12
3.2.2 Customized SERGoM 13
3.3. Batch Clip ICLUS Rasters 13
3.4. Create Population Project!on Rasters 14
3.5. Reclassify Housing Density to Classes 15
3.6. Estimate Percent Impervious Surface 16
3.7. Summarize Impervious Surface By Housing Density Class 17
4. References 19
TABLES
Table 2-1. Description of the weights that are applied to NLCD which are used to allocate the
block housing units within each block 6
Table 2-2. Summary of Adjustments to SERGoM v3 for SRES scenarios 8
in
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FIGURES
Figure 2-1. Diagram of SERGoM data inputs and modeling process 4
Figure 3-1: Loading the ICLUS Toolbox into ArcGIS 10
Figure 3-2: The Expanded ICLUS Tools 11
Figure 3-3: Basic SERGoM User Interface 12
Figure 3-4: Custom SERGoM User Interface 13
Figure 3-5: Opening Screen for the Batch Clip ICLUS Rasters Tool 14
Figure 3-6: Opening Screen for Create Population Project on Rasters Tool 15
Figure 3-7: Opening Screen for the Reclassify Housing Density Tool 16
Figure 3 -8: Opening Screen for the Estimate Percent Impervious Surface Tool 17
Figure 3-9: Opening Screen for the Summarize Impervious Surface by Housing Density Class
Tool 18
IV
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AUTHORS AND REVIEWERS
The Global Change Research Program, within the National Center for
Environmental Assessment, Office of Research and Development, is responsible for
publishing this report. This document was prepared by ICF International under Contract
No. GS-10F-0234J, U.S. EPA Order No. 1101 and Interagency Agreement RW-14-
92237501-0 with the U.S. Geological Survey, and by Dr. David M. Theobald, Colorado
State University, through a sub-contract on these awards. Dr. Chris Pyke and Dr. Britta
Bierwagen served as successive Technical Project Officers. Drs. Pyke and Bierwagen
provided overall direction and technical assistance.
AUTHORS
Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO
David M. Theobald
U.S. EPA. Washington. D.C.
Philip Morefield
ICF International
Mark Bethoney, Chris Hogan, and Kevin Wright
REVIEWERS
U.S. EPA Reviewers
Jason Berner, Walt Foster, Gordon Hamilton
External Reviewers
Chansheng He (Western Michigan University), Victor Mesev (Florida State University),
Ningchuan Xiao (The Ohio State University)
ACKNOWLEDGEMENTS
We would like to thank reviewers for testing and evaluating these geoprocessing tools and
providing valuable feedback on usability and enhancements.
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1. PURPOSE
The purpose of this document is to describe the tools and models generated for the Integrated
Climate and Land Use Scenarios (ICLUS) project led by the US EPA's Global Change Research
Program in the Office of Research and Development's National Center for Environmental
Assessment (USEPA 2009). The tools and models described in this document provide users with
the ability to generate, visualize, and analyze spatial datasets about residential housing growth in
the coterminous US.
ICLUS consists of a demographic and a spatial allocation model. The tools described in this
document use output from the demographic model, but do not allow modifications to that model.
The tools do, however, provide users with the ability to modify some of the parameters of the
spatial allocation model.
The spatial allocation model used in this project is SERGoM, the Spatially Explicit Regional
Growth Model, which has been under development for nearly a decade, and a number of papers
describe it more fully (Theobald 2001, 2003, 2005). It has been used for a number of
applications to assess effects on other land cover classes or environmental variables, including
the Forests on the Edge project, which examines effects of housing density changes on forest
products and services (Stein et al. 2005, 2007). Unlike the majority of land use change models
focusing on urban growth, SERGoM uses and models a full continuum of housing density, from
urban to rural. This allows a more comprehensive examination of growth patterns, since
exurban/low-density development has become more prevalent in the past decades and is an
important aspect of possible future growth scenarios. SERGoM is based on a relatively high
resolution (1 ha) representation of land-use patterns, but is best applied at a regional to national
extent. This is because there are relatively few assumptions underlying the model - it is not a
data-rich approach (e.g., agent-based modeling). Semi-decadal population projections for each
county drive the production of new housing units, which are allocated in response to the spatial
pattern of growth during the previous time step, transportation infrastructure, and other basic
assumptions. An important technical advantage of this model is that it provides a consistent,
comprehensive, nationwide model at 1 ha resolution.
1.1 KEY ASSUMPTIONS AND USES
As with any model, there are a number of key assumptions that should be understood when
using the SERGoM model and results. In particular, these assumptions are:
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future growth will be similar to those patterns experienced during the previous times step
- this approach is applicable to understanding regional and broader-scale development
patterns, it is not suitable for finer-scale analysis;
projected population level of a county is the major driver of growth, particularly in the long
term;
- only residential housing density is estimated - other land uses typically considered to be part
of urbanization are industrial, commercial, or transportation land uses and are not modeled;
housing units are added - a loss of housing units due to conversion to commercial or
demolition is not modeled;
Although there are a large number of possible uses, the tools provided in the ICLUS vl.3
toolset are designed to assist with, at a minimum, the following common anticipated user
scenarios:
access the county-level ICLUS population projections;
- customize housing density patterns by altering household size and travel time assumptions;
- classify housing density into generalized categories;
estimate future impervious surface cover based on housing density; and
summarize levels of imperviousness by housing density classes.
Note that we originally intended to develop a tool to compute population density from housing
density. However, in developing such a tool, we discovered that there can be some numerical
instability issues because of the sensitivity of the assumption about people per housing unit
(household size) that is used as a multiplier to convert housing units to population.
2. SOFTWARE, HARDWARE, AND EXPERIENCE REQUIREMENTS
These tools were designed, created, and tested using ArcGIS 9.3.1. The geoprocessing-script
tools require the Spatial Analyst extension and Python 2.5.1, which is installed by default with
ArcGIS 9.3.1. Because this is a complex spatial model with numerous large (fine-grain, broad-
extent) spatial datasets, a high-end computing environment is highly recommended. At a
minimum, this means 4GB RAM with multiple CPU cores. It is also highly recommended that
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50-100 GB of disk space be available to accommodate large output files (-600 MB each) and
intermediate files created during processing. Hardware for high-speed input/output (e.g. RAID 0)
may improve processing times, particularly for the SERGoM model. The experience level of the
GIS analyst using these tools should be advanced (at least 3 years experience), with knowledge
of raster datasets and spatial modeling, and experience with very large datasets.
2.1. TECHNICAL DESCRIPTION OF SERGOM V3
SERGoM, unlike the majority of land use change models, allocates a full continuum of
housing density, from urban to rural. This allows a more comprehensive examination of growth
patterns, since exurban/low-density development generally has a footprint 10 times as large as
urban areas and is growing at a faster rate than urban areas (Theobald 2005). In addition,
SERGoM forecasts housing development by establishing a relationship between neighboring
housing density, population growth rates, and transportation infrastructure (Theobald 2005). The
model is dynamic in that as new urban-core areas emerge, the model re-calculates travel time
from these areas. However, the expected changes in functional connectivity that would result
from such emerging urbanization were not fed back into the functional connectivity calculations
used to calculate domestic migration. SERGoM also incorporates a detailed layer of
developable/un-developable areas that incorporates public protected lands as well as private
protected (e.g., through conservation easements) lands. Finally, population forecasts are a
principal driver of SERGoM; in the model, population growth is converted to housing units,
which are spatially allocated in response to the spatial pattern of previous growth and
transportation infrastructure (Figure 2-1). Growth rates and other model parameters can be user-
specified and are spatially explicit, so different regions (even census tracts or neighborhoods) can
have different parameters (e.g., lower household size in amenity areas, etc.). The benefit of this
approach is that there are fewer discrete differences across artificial analytical boundaries
imposed by "piecing" individual model runs into a nationwide map, although the allocation of
new housing units takes place on a county-by-county basis.
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liocal growth
V Rates
I '
ew housing//
f f
/
Figure 2-1. Diagram of SERGoM data inputs and modeling process
Housing density (number of housing units per acres) was computed for each 1 ha cell (100 m
x 100 m raster; 2.47 acres). There are five main input spatial datasets used to estimate housing
density:
1. 2000 Census Bureau - Data were compiled from the 2000 census on the number of housing
units and population for each block in the year 2000 and the geography or polygon boundary
for each census block (from the Summary File 1 dataset). Block-groups, which are a coarser-
level aggregation of block polygons, and attributes of the number of housing units built by
decade were used to estimate the historical number of housing units in each block. An
operating assumption in estimating historical housing units is that they have not declined
over time, so that the number of housing units in any past decade (back to 1940) did not
exceed the number of units in any subsequent decade (up to 2000). Reservoirs, lakes, and
wide rivers that were identified as "water blocks" were also removed, so that no housing
units were placed in these undevelopable areas.
2. Undevelopable lands - Spatial data on land ownership were compiled from a variety of
sources to create the most current and comprehensive dataset - called the UPPT
(unprotected, private protected, public protected, and tribal/native lands). The UPPT dataset
was generated by starting with the Conservation Biology Institute's PAD-US database. We
updated the PAD-US dataset with more current data for 21 states. The operating assumption
is that housing units do not occur on publicly owned lands (e.g., national parks, forests, state
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wildlife areas, etc.) or on privately-owned, protected lands. Some state lands in the western
United States (the so-called "school lands" sections, but not "stewardship" lands) were kept
in the developable category because they are in practice sold to generate revenue for state
school systems. Also, tribal lands are often considered federal (public), but here we included
tribal lands as developable (except for known tribal parks). The portions of blocks that
overlapped with public (and other non-developable lands) were deleted to create a modified
or refined block. All housing units associated with each block are then assumed to be located
in the refined (developable) portion of the blocks. Housing units were apportioned within the
refined block using a dasymetric mapping approach described in Table 2-1 below.
3. Road and groundwater well density - The existence of major roads (interstates, state
highways, county roads) was used to better allocate the location of housing units within a
block. In a previous SERGoM model (vl, v2) housing units were spread evenly throughout
the refined blocks. Here, in v3 of SERGoM, housing units are disproportionately weighted to
areas of higher likelihood of being developed according to fine-grained land use/cover data
from NLCD 2001 (see Table 2-1). Because major road infrastructure is included in the
NLCD (actually burned in as values 21, 22, and some 23), road density per se was not
included. Also, in the western US, where the rural blocks are particularly large, groundwater-
well density was included to refine the allocation of units. The analytical hierarchy process
(AHP; Saaty 1980) was used to provide an estimate of logical consistency during the
development of the weights (the consistency index was 0.035, which is less than the 0.15
threshold, showing that these estimates were logically consistent). Note also that these
weights are applied in a relative, not absolute context. That is, the number of units that will
be distributed in a given area is specified by the census block and so units are allocated in
proportion to the weights found within a given block. This is robust in the face of potential
misclassification of land cover types, because all the known housing units will be allocated to
a given block, regardless of land cover (but note that the undevelopable - water and public
lands - portions of the blocks are excluded).
4. County population projections - Population projections for each county are used to drive the
future growth forecasts. Additional housing units needed in each county were computed by
determining the number of new housing units needed to meet the needs of the additional
population, assuming the same (as in the year 2000) population to housing-unit ratio in each
tract, using 2000 U.S. Census of Housing data.
5. Commercial/industrial land use - We also mapped locations with land uses that would
typically preclude residential development (increased housing density), especially
commercial, industrial, as well as transportation land uses. Using urban/built-up categories
from NLCD 2001 (not open space developed), we identified locations (1 ha cells) that had
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>25% urban/built-up land cover but very low levels of housing density (i.e. less than
suburban). High-density residential areas are otherwise included in the urban/built-up land
cover categories. Although some re-development of central business districts
("gentrification") is occurring, SERGoM works from the operating assumption that these are
relatively small portions of the landscape.
Table 2-1. Description of the weights that are applied to NLCD which are used to allocate
the block housing units within each block
NLCD Classes
Open water & perennial ice/snow
(11,12)
Developed, open space (21)
Developed, low, medium & high
intensity (22, 23, 24)
Transitional (3 1,32, 33)
Wildland vegetation (41, 42, 43,
51,52,71,72,73,74)
Agricultural (pasture/hay,
cultivated) (61, 81, 82)
Wetlands (90, 91, 92, 93, 94)
Description
No housing units on water or snowfields
This is typically either open space (urban
parks and greens space) or roads (in rural
areas), so very low likelihood of housing
units
These cover types are where housing units
most likely are located
Includes barren, transitional areas such as
cleared or recently cleared areas, but also
mines, etc.
Not likely to have high-density (urban)
development in these areas.
Will have some housing density, but most
housing infrastructure is clustered and near
roads.
Not likely to develop in wetlands (without
filling or permits).
Weight3
0.0
0.085
0.549
0.115
0.150
0.050
0.050
"Note that the weights are applied to each 30 m cell in the NLCD, then the weights are aggregated up to 100 m
resolution by averaging the 30 m weight values.
SERGoM is a demand/allocation/supply model, where the number of new housing units
needed for the next decade is computed to meet the demands of the projected population,
computed here for each county (although other analytical units are possible). The average growth
rate for each state-housing density class combination is computed from the previous to current
time step (e.g., 1990 to 2000). These average growth rates are computed using a moving
neighborhood for 12 development classes. These classes are formed by overlaying three housing
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density classes - urban/suburban, exurban, and rural - with four accessibility classes measured
as travel time (minutes one way) from the nearest urban/suburban core along (existing) major
roads: 0-10, 10-30, 30-60, and >60 minutes. The resulting combination creates a "surface" of
raster values that reflect historical patterns of growth - called allocation weights - and are used to
allocate the new housing units for a given time step.
Based on the Census definition of urban areas, urban housing densities are less than 0.1 ha
per unit and suburban are 0.1 - 0.68 ha per unit. We defined exurban density as 0.68 - 16.18 ha
per unit (to 40 acres) to capture residential land use beyond the urban/suburban fringe that is
composed of parcels or lots that are generally too small to be considered productive agricultural
land use (though some high-value crops such as orchards are a notable exception). Rural is
defined as greater than 16.18 ha per unit where the majority of housing units support agricultural
production.
2.2. SRES-SPECIFIC PARAMETERS FOR SERGOM
We used a number of population growth scenarios that are based on the Intergovernmental
Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social,
economic, and demographic storylines (Nakicenovic and Swart 2000). These scenarios are
rendered using a combination of models representing demography. The SRES provide storylines
along two major axes, economic vs. environmentally-driven development (A-B) and global vs.
regional development (1-2), which make up the four combinations of storylines, Al, A2, Bl, and
B2. In addition to changes in population that resulted from the various demographic assumptions
associated with each SRES-compatible storyline developed for the ICLUS project (USEPA
2009), the spatial location of growth was modified using SERGoM in two ways: household size
and travel time (Table 2-2). With SERGoM, household size is expected to reflect demographic
changes due to changes in fertility and socioeconomic changes that affect household formation.
Travel time from urban "central city" locations is used to help express how the evolution of the
urban form might be affected by changing priorities and increases in the cost of transportation.
First, weighting values can be adjusted as a function of distance away (travel time) from
urban cores. Urban-area (<5 minutes) weights can be lowered by a given percentage to reflect a
carrying capacity or saturation of an area, specified by zoning perhaps; or raised to reflect
increased desire for urban living (lofts, gentrification, etc.). Exurban-area weights (-30-60
minutes) can be lowered to reflect assumptions of lower rates of development due to increased
fuel prices or can be used as a surrogate for lower land availability because of increased
conservation purchases (or easements). Weights can also be raised for exurban areas to reflect
increased "urban flight" of baby-boomer retirees and rural amenities. This weighting surface is
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re-computed at each time step. We modified the weights of travel times for the Bl and B2
storylines to model a "compact" growth scenario (see Table 2-2). Given the environmental
orientation of the Bl and B2 storylines, we assumed that growth patterns in these scenarios
would place a greater emphasis on promoting denser growth patterns closer to existing urban
centers. The baseline scenario uses the midline US Census Bureau projection of population
growth and assumes no other changes from business-as-usual development patterns.
Table 2-2. Summary of Adjustments to SERGoM v3 for SRES scenarios.
Scenario
Al
Bl
A2
B2
Baseline
SERGoM parameters
Household size
Smaller (-15%)
Smaller (-15%)
Larger (+15%)
No change
No change
Travel time (minutes)
<5; 10; 20; 30; 45; 45
75; 75; 85; 90; 95; 100
90; 95; 85; 90; 95; 100
75; 75; 85; 90; 95; 100
90; 95; 85; 90; 95; 100
75; 75; 85; 90; 95; 100
Travel time
consequences for urban
growth form
No change
Slightly compact
No change
Slightly compact
No change
A second type of modification is changing assumptions about households, particularly
household size (roughly family size), defined as the number of people living in a single housing
unit. Currently this ratio (population per housing unit) is static and is computed at the tract level
from the 2000 U.S. Census data. We modified this ratio to reflect assumptions in the SRES
scenarios to adjust for assumed changes in demographic characteristics. For example, SRES Al
and Bl assume smaller household sizes (a reduction by 15% throughout the future decades),
whereas scenarios B2 and baseline are not changed and A2 assumes a 15% increase in household
size (Jiang and O'Neill, 2007). The changes in household size correspond to changes in fertility
rates that are assumed under the different storylines. Under Al and Bl, where fertility is lowest,
smaller average household sizes are also expected. Conversely, A2 has the highest fertility rates,
so an increase in household sizes is expected. In B2, which uses the medium fertility rates,
household sizes are not changed. Household size remains unchanged in the baseline scenario.
Modifying urban form and household size requires several parameter changes in SERGoM.
To model the "compact" growth scenarios (Bl and B2), SERGoM was run with modifications to
the spatial allocation of new housing units as a function of travel time from urban cores. Urban
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cores were defined as a contiguous grouping of urban/suburban cells at least 128 ha in size. The
changes in household sizes for each scenario translate into the following parameter changes in
SERGoM: each census tract was modified by 0.85 for Al and Bl scenarios, and by 1.15 for A2;
B2 and baseline scenarios are unchanged.
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3. TOOLS
3.1. SETTING UP THE ICLUS TOOLS
This section describes the specific steps required to use the ICLUS toolset in ArcGIS.
Extract the ICLUS_vl.3.zip file to a location other than your desktop. Avoid path names
that include special characters (e.g. parentheses, quotation marks) or spaces. You will see
five folders titled: "base", "documentation", "population", "output", and "software". All
underlying datasets needed to run the ICLUS tools must be located in the ".. ./ICLUS_vl.3/base"
directory.
Start either ArcMap or ArcCatalog, right-click in the ArcToolbox panel, and select "Add
Toolbox". Navigate to the ".. ./ICLUS_vl.3/software" folder and add the red toolbox named
"ICLUS vl.3" (Figure 3-1).
,
Add Toolbox
Look in: ||_J software
?-?-! =^ nn
SIS: EEE nn
Name
I Type
|ICLLJ5vl.3
Toolbox
Name:
r
Open |
Show of type: |Too|boxes
Cancel
Figure 3-1: Loading the ICLUS Toolbox into ArcGIS
10
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ICLUSvl.3
1) Spatially Explicit Regional Growth Model
.1^ Basic
^ Custom
2) Population and Housing Density Tools
& Batch Clip ICLU5 Rasters
^ Create Population Projection Rasters
|£ Reclassify Housing Density
3) Impervious Surface Tools
^ Estimate Percent Impervious Surface
$ Summarize Impervious Surface by Housing Density Class
Figure 3-2: The Expanded ICLUS Tools
The tools are split into three general groupings. The first toolset contains two versions of the
Spatially Explicit Regional Growth Model (SERGoM) (Figure 3-2) The Basic version of
SERGoM will create housing density rasters for a user-specified time period under user-specified
ICLUS scenarios. Alternatively, a Customized SERGoM model run can be executed for a single
ICLUS scenario using user-specified parameters (i.e. travel-time weights, household size). The
Population and Housing Density and Impervious Surface toolsets provide other functions for
creating and managing ICLUS datasets. Some fields in the SERGoM-model tools will
automatically populate with default values. All fields with a green dot next to them are required.
The following sections provide a description of each tool screen.
3.2.
SERGOM: THE SPATIALLY EXPLICIT REGIONAL GROWTH MODEL
There are two version of the SERGoM-model user interface included in this toolset. Besides the
user interface, the specifics of the model executed by each tool are identical. The "Basic" version only
requires the selection of a time period, ICLUS population scenario(s), and output options (Figure 3-3).
This version of the model will run with the default SERGoM parameters found in Table 2-2. The
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"Custom" version allows users to specify travel time weights and a household size adjustment;
however, only one ICLUS population scenario may be selected at a time.
3.2.1
BASIC SERGOM
This is a simplified interface for SERGoM that will create nationwide housing density data (100
meter resolution) for a given set of ICLUS growth scenarios over a specified time period (Figure 3-3).
Note that running this tool for all years and all scenarios typically requires 100 hours of run time and
generates 50+ GB of output. Model output will be placed in the ".. ./ICLUS_vl.3/output" directory.
Create housing density starting with year:
...and ending with year:
Options: ICLUS Scenarios
l~ Create data for all scenarios
r*i
TA2
FBI
FB2
I Baseline
Options: Output
I Build pyramids
l~ Build raster attribute tables
F" Add output to ArcMap table of contents
This "Basic" version of
SERGoM will produce datasets
using the default travel time
and household size parameters
outlined in the User's Manual.
OK Cancel I Environments... | «Hide Help | Tool Help
Figure 3-3: Basic SERGoM User Interface
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3.2.2
CUSTOMIZED SERGOM
The "Custom" version of SERGoM will accept changes to the default travel time weighting
scheme and household size adjustments outlined on page 8 (Figure 3-4). Model output will be placed
in the ".. ./ICLUS_vl.3/output" directory.
%, Custom
j Create housing density rasters beginning with year; —
r ~n
j . . .and ending with year:
d
j ICLUS scenario;
r ~u
A Options: Customize SERGoM Parameters
17 Use the default travel time weights for this scenario
Travel time <5 minutes: (optional)
r
Travel time 5-10 minutes: (optional)
r
Travel time 10- 20 minutes: (optional)
r
Travel time 20-30 minutes: (optional)
r
Travel time 30-45 minutes: (optional)
r
Travel time > 45 minutes: (optional)
r
|7 Use default household size assumption for this scenario
Household size adjustment: (optional)
r
& Options: Output
l~~ Build pyramids
f~ Build raster attribute tables
1 Add output to ArcMap table of contents
r
OK Cancel Environments.., « Hide Help
^iDlxl
Custom
The "Custom" version of
SERGoM allows user-specified
travel time weights and
household size adjustment.
d
Tool Help
Figure 3-4: Custom SERGoM User Interface
3.3.
BATCH CLIP ICLUS RASTERS
The SERGoM model will only create nationwide datasets; these datasets are fairly large in
terms of file size. The User can apply the Batch Clip tool to extract data for an area of interest
(Figure 3-5). This is especially useful when only certain parts of the country are of interest,
rather than the whole coterminous US. The clip mask can be any polygonal boundary that the
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User wants to "cut out" and save from the main input rasters. Users select where the finished
products will be stored in the Output Folder choice. Those data may then be used as input for
other ICLUS tools, significantly reducing processing times. Projection for 'Clip Mask'
polygon must be: USA Contiguous Albers Equal Area Conic USGS (projection file located
in "base" folder).
3.4.
(~ Build pyramids
F Build raster attribute tables
F Add output to ArcMap table of contents
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rasters can also be created "on the fly" by simply running the SERGoM tool. Population rasters
will automatically be placed in the "population" folder.
3.5.
1 f> Create Population Projection Rasters 1
„ ICLUS Scenario: — '
r ~3
j Start with vear;
r ~~3
_, End with year;
r ~3
V Add Output to Arch/lap Table of Contents
r
OK Cancel Environments... « Hide Help
Create Population
Projection Rasters
Creates county level population
projection rasters based on the
ICLUS demographic model.
Tool Help
Figure 3-6: Opening Screen for Create Population Projection Rasters Tool
RECLASSIFY HOUSING DENSITY TO CLASSES
This tool reclassifies raw block housing density values into density classes (Figure 3-7).
Commercial land pixels, which are static in the ICLUS vl.3 model, may also be incorporated
into the output of this tool There are two classification schemes available; please check the
within-tool help screens for details.
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3.6.
Reclassify Housing
Density
Reclassify housing density into
generalized housing density
classifications.
, Number of density classes:
V Include commercial/industrial pixels in output
f~ Build raster attribute tables
F~ Add output to ArcMap table of contents
il
OK | Cancel I Environments... | «Hide Help | Tool Help |
Figure 3-7: Opening Screen for the Reclassify Housing Density Tool
ESTIMATE PERCENT IMPERVIOUS SURFACE
This tool reclassifies block housing density values into estimated percent impervious
categories (Figure 3-8). A Categorical Regression Tree model was used to develop a regression
equation that describes the relationship between the Percent Urban Imperviousness (PUT) dataset
produced by the MRLC Consortium and housing density (see Appendix C in USEPA 2009 for
more details). This relationship was then converted into a set of conditional statements which
form the basis for this tool.
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1 S Estimate Percent Impervious Surface
I]
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, Number of housing density classes:
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Summarize Impervious
Surface by Housing
Density Class
Tabulates estimated percent
impervious statistics by
housing density classes.
~~| Cancel I Environments... | «Hide Help I Tool Help |
Figure 3-9: Opening Screen for the Summarize Impervious Surface by Housing Density
Class Tool
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4. REFERENCES
Jiang, L; O'Neill, BC. (2007) Impacts of demographic trends on US household size and structure. Population and
Development Review 33(3): 567-591.
Nakicenovic, N; Swart, R, eds. (2000) Special report on emissions scenarios Cambridge, UK: Cambridge University
press.
Saaty, A. (1980) The Analytic Hierarchy Process: planning, priority setting, resource allocation, McGraw-Hill.
Stein, SM; McRoberts, RE; Alig, R; et al. (2005) Forests on the Edge: housing development on America's private
forests. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station PNW-GTR-636. May
2005. URL: http://www.fs.fed.us/projects/fote/reports/fote-6-9-05.pdf
Stein, SM; Alig, R; White, EM; et al. (2007) National Forests on the Edge: development pressures on America's
National Forest System. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, PNW-
GTR-728. August 2007.
Theobald, DM. (2001) Land use dynamics beyond the American urban fringe. Geographical Review 91(3):544-564.
Theobald, DM. (2003) Targeting conservation action through assessment of protection and exurban threats. Conservation
Biology 17(6): 1624-1637.
Theobald, DM. (2005) Landscape patterns of exurban growth in the USA from 1980 to 2020. Ecology and Society 10(1):
32. [online] URL: http://www.ecologyandsociety.org/vollO/issl/art32/.
U.S. EPA (Environmental Protection Agency). (2009) Land-Use Scenarios: National-Scale Housing-Density Scenarios
Consistent with Climate Change Storylines (Final Report). U.S. Environmental Protection Agency, Washington,
DC; EPA/600/R-08/076F. Available from the National Technical Information Service, Springfield, VA, and online
at http://www.epa.gov/ncea.
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