EPA/600/R-09/143A
November 2009
ICLUS vl.2 User's Manual:
ArcGIS tools and datasets for modeling US
HOUSING DENSITY GROWTH
- a ICLUS vl.2 Tools
0 §
} 1) Population and Housing Density Tools

S Batch Clip ICLUS Rasters

J> Create Population Projection Rasters

Reclassify Housing Density

B SERGoM v3
- S
>1 2) Impervious Surface Tools

Estimate Percent Impervious Surface

Summarize Impervious Surface by Housing Density Class
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT THIS INFORMATION IS
DISTRIBUTED SOLELY FOR THE PURPOSE OF PRE-DISSEMINATION PEER
REVIEW UNDER APPLICABLE INFORMA TION QUALITY GUIDELINES IT HAS NOT
BEEN FORMALLY DISSEMINATED BY THE U.S. ENVIRONMENTAL PROTECTION
AGENCY. IT DOES NOT REPRESENT AND SHOULD NOT BE CONSTRUED TO
REPRESENT ANY AGENCY DETERMINATION OR POLICY.
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 is an internal draft for review purposes only. This information is
distributed solely for the purpose of pre-dissemination peer review under applicable
information quality guidelines. It has not been formally disseminated by the U.S.
Environmental Protection Agency. It does not represent and should not be construed to
represent any agency determination or policy. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
Preferred Citation:
U.S. Environmental Protection Agency (EPA). 2009. ICLUS vl.2 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/XXX. Available from the National Technical Information Service, Springfield, VA,
and online at http://www.epa.gov/ncea.
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DRAFT - ICL US SERGoM v3 User's Guide - DRAFT
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	3
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.	Batch Clip ICLUS Rasters	12
3.3.	Create Population Projection Rasters	12
3.4.	Reclassify Housing Density to Classes	13
3.5.	SERGoM v3 Growth Model	14
3.6.	Impervious surface tools	15
3.7.	Estimate Percent Impervious Surface	16
3.8.	Summarize Impervious Surface By Housing Density Class	16
4.	References	18
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
Table 3-1. Housing density class definitions	17
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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: Opening Screen for the Batch Clip ICLUS Rasters Tool	12
Figure 3-5: Opening Screen for Create Population Projection Rasters Tool	13
Figure 3-6: Opening Screen for the Reclassify Housing Density Tool	14
Figure 3-7: Opening Screen for SERGoM v3 Growth Model Tool	15
Figure 3-9: Opening Screen for the Estimate Percent Impervious Surface Tool	16
Figure 3-10: Opening Screen for the Summarize Impervious Surface by Housing Density
Class Tool	17
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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 (Student Services Contractor)
ICF International
Mark Bethoney, Chris Hogan, and Kevin Wright
REVIEWERS
U.S. EPA Reviewers
Jason Berner, Walt Foster, Gordon Hamilton
ACKNOWLEDGEMENTS
We would like to thank reviewers for testing the geoprocessing tool 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 (EPA 2008). The tools and models described in this document provide users with the
ability to generate, visualize, and analyze spatial datasets about growth in the coterminous US.
SERGoM, the Spatially Explicit Regional Growth Model, has been under development for
nearly a decade, and a number of papers describe it more fully (Theobald 2001, 2003, 2005). It
has also 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).
Essentially, decadal population projections for each county drive the production of new housing
units, which are allocated in response to the spatial pattern of previous growth (e.g., 1990 to
2000), transportation infrastructure, and other basic assumption. 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:
future growth will be similar to those patterns experienced during the previous decade (e.g.,
1990-2000);
- this approach is applicable to understanding regional and broader-scale development
patterns, it is not suitable for finer-scale analysis;
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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 use;
housing units are added - a loss of housing units due to conversion to commercial or
demolition is not modeled;
-	the default resolution to run this model is 100 m (1 ha), but with appropriate datasets it could
be run at 30 m, or at a more coarse resolution up to 400 m; and
All input data should be in the same projected coordinate system (e.g., Albers, NAD1983).
Although there are a large possible number of uses, the tools provided in the ICLUS
SERGoM toolset are designed to assist with, at a minimum, the following common anticipated
user scenarios:
-	to replace the ICLUS projected population values to reflect different growth rate assumptions
for the nation;
-	to upscale maps of housing density to a coarser resolution;
-	to summarize patterns by region, watershed, county, or NLCD 2001 classes;
-	to reclassify housing density into classes different than those already provided; and
-	to generate a map of estimated impervious surface based on a housing density map.
Note that originally we 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. As a result,
we recommend computing this ratio at a coarser resolution data such as census tracts or 1 km2 to
reduce these artifacts, and then to carefully examine the results for outliers.
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2. SOFTWARE, HARDWARE, AND EXPERIENCE REQUIREMENTS
These tools were written as a series of tools in a Geoprocessing toolbox, using ArcGIS 9.3.1.
The ArcToolbox geoprocessing tools require the Spatial Analyst extension and Python 2.5.1,
which is installed by default with ArcGIS v9.3.1. If needed, a Python 2.5.1 installer has been
included in the "software" folder of the ICLUS vl zip file. Because this is a complex spatial
model, with numerous large (fine-grain, broad-extent) spatial datasets, a high-end computing
environment is required. Typically, this means >1GB RAM, 100-300 GB working space of
memory, and a RAID drive for fast disk I/O. The experience level of the GIS analyst using these
tools should be advanced (at least 3 years experience), knowledge of raster datasets and spatial
modeling, and experience with very large (>1 GB) 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
specified spatially-explicitly, so different regions (even census tracts or neighborhoods) have
different parameters (e.g., lower household size in amenity areas, etc.). The benefit of this
approach is that there are fewer (internal to coterminous United States) discrete differences
across artificial analytical boundaries imposed by "piecing" individual model runs into a
nationwide map, although the allocation of new housing units is restricted to counties.
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Nextdecade t+t
/ Protected
areas
Housing
density
:al growth
Rates
Allocation
weights
Census
water
lew housing //
densif^? T
Accessibility
to urban
Population
County-level
- population
forecasts
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 2000 and the geography or polygon boundary for each
census block (from the SF1 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 from 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 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 were 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. Also, 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 that then 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
mis-classification of land cover types, because all the known housing units will be allocated
to a given block, regardless of the 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 were computed by determining the
number of new housing units needed to meet the needs of the additional population,
assuming the same (in 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 of
NLCD 2001 (not open space developed), we identified locations (1 ha cells) that had >25%

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urban/built-up land cover but that had also had lower than suburban levels of housing density
(because high-density residential areas would otherwise be 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 smaller portions of the landscape and typically brown-field settings.
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
Description
Weight3
Open water & perennial ice/snow
(11,12)
No housing units on water or snowfields
0.0
Developed, open space (21)
This is typically either open space (urban
parks and greens space) or roads in rural
areas), so very low likelihood of housing
units
0.085
Developed, low, medium & high
intensity (22, 23, 24)
These cover types are where housing units
most likely are located
0.549
Transitional (31, 32, 33)
Includes barren, transitional areas such as
cleared or recently cleared areas, but also
mines, etc.
0.115
Wildland vegetation (41, 42, 43,
51, 52, 71, 72, 73, 74)
Not likely to have high-density (urban) in
these areas.
0.150
Agricultural (pasture/hay,
cultivated) (61, 81, 82)
Will have some housing density, but most
housing infrastructure is clustered and near
roads.
0.050
Wetlands (90, 91, 92, 93, 94)
Not likely to develop in wetlands (without
filling).
0.050
aNote 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 (but could be other analytical unit boundaries). The average
growth rate for each state-housing density class 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, we defined urban housing densities as less
than 0.1 ha per unit and suburban as 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. 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 scenario developed for the ICLUS project, the spatial location of growth was
modified using SERGoM in two ways, through 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). It can also be raised for exurban areas to reflect increased
"urban flight" of baby-boomer retirees and rural amenities. This weighting surface is re-
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computed at each time step. We modified the weights of travel times for the B1 and B2
storylines to model a "compact" growth scenario (see Table 2-2). Given the environmental
orientation of the B1 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.
Table 2-2. Summary of Adjustments to SERGoM v3 for SRES scenarios.
Scenario
SERGoM parameters

Household size
Travel time (minutes)
<5; 10; 20; 30;
45; >45
Travel time consequences
for urban growth form
A1
Smaller (-15%)
75; 75; 85; 90; 95;
100
No change
Bl
Smaller (-15%)
90; 95; 85; 90; 95;
100
Slight compact
A2
Larger (+15%)
75; 75; 85; 90; 95;
100
No change
B2
No change
90; 95; 85; 90; 95;
100
Slight compact
Baseline
No change
75; 75; 85; 90; 95;
100
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 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 A1 and B1
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 A1 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.
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SERGoM was parameterized to reflect the SRES scenarios in the following ways. First, the
A1 and B1 scenarios were modeled to reflect a 15 percent decline in average household size. A2
was modeled to show a 15 percent increase in average household size. B2 was modeled with no
change in household size. The household size for each census tract was modified by 0.85 for A1
and B1 scenarios, and 1.15 for A2. Second, to model the "compact" growth scenarios in SRES
for runs B1 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 cores were defined as a
contiguous grouping of urban/suburban cells at least 10 ha in size.

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3. TOOLS
3.1. SETTING UP THE ICLUS TOOLS
Extract the ICLUS_vl.2 zip file (ICLUS vl2.zip) to whatever location you desire other than your
desktop. Inside the folder you will see four folders titled: "base", "population", "output", and
"software".
Start either ArcMap or ArcCatalog, right-click in the ArcToolbox panel, and select "Add
Toolbox". Navigate to the "software" folder and add the red toolbox named "ICLUS vl.2".
Look in:
	i software
Add Toolbox
Toolbox
Name
Name:
Show of type: | Toolboxes	^
Figure 3-1: Loading the ICLUS Toolbox into ArcGIS
~ pen
Cancel
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- ^ ICLUS v 1.2 Tools
~ ^ 1) Population and Housing Density Tools
Batch Clip ICLU5 Rasters
Create Population Projection Rasters
Reclassify Housing Density
SERGoM v3
- 2) Impervious Surface Tools
S Estimate Percent Impervious Surface
ifS Summarize Impervious Surface by Housing Density Class
Figure 3-2: The Expanded ICLUS Tools
The tools are split into two general groupings. The two sections are Population and Housing
Density Tools (this includes the SERGoM model itself), and Impervious Surface Tools. Select
the particular tool you wish to run by double clicking. Some of the tools will already have
certain fields filled out. Those fields reference required files, and will automatically populate
with a network path relative to where you have unzipped your ICLUS folder. All fields with a
green dot next to them are required.
Please see below for a description of each tool screen.
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DRAFT - ICL US SERGoM v3 User's Guide - DRAFT
BATCH CLIP ICLUS RASTERS
S Batch Clip ICLUS Rasters
- nJiil


,_j Input Rasters
j Clip Mask
j Output Folder
OK
Cancel
+]
2SJ
~I
&|
d
Environments.,. << Hide Help
Batch Clip ICLUS
Rasters
Extracts the cells of a
raster that correspond to
the areas defined by a
mask. This is typically
used to cut out areas of a
raster that are not needed
or necessary for a
particular analysis.
Tool Help
d
Figure 3-3: Opening Screen for the Batch Clip ICLUS Rasters Tool
You can use this tool to create and work with smaller rasters, this is especially useful if you are
only interested in a certain part of the country rather than analyzing the whole. The clip mask can
be any polygonal boundary that you want to "cut out" and save from the main input rasters. You
select where you want the finished products to be stored in the Output Folder choice.
3.3.
CREATE POPULATION PROJECTION RASTERS
This tool creates population projection rasters from "cofips_upp" shapefile located in the "base"
folder. These rasters are used by the SERGoM model, but are also appropriate for analyses
requiring county level populations. The various ICLUS scenarios can be selected by clicking on
the pull down next to the field. Only valid choices are accepted.
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-	These rasters can also be created "on the fly" by simply running the SERGoM tool.
-	Population rasters will automatically be placed in the "population" folder.
| S Create Population Projection Rasters
-inlxil
A
Navigate ho "base" folder:
J
Create Population
Projection Rasters
Creates county level
1 &
j ICLUS Scenario:
zJ
, btart with year:
population projection
rasters based on the ICLUS
1 A
. End with year:
demographic model
1' ^
zl
M
OK Cancel | Environments... |
<< Hide Help 1
Tool Help
1
Figure 3-5: Opening Screen for Create Population Projection Rasters Tool
3.4. RECLASSIFY HOUSING DENSITY TO CLASSES
-	This tool reclassifies raw block housing density values into density classes.
-	Commercial land pixels, which are static in the ICLUS SERGoM model, may also be
incorporated into the output of this tool.
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g Reclassify Housing Density

"1]
j Navigate to "base" folder:
j Input Raster(s)
, Number of density classes:
I	 	
p Include commercial/industrial lands in output (optional)
|^|
+1
*J
3
OK
Reclassify Housing
Density
Reclassify housing density
into generalized housing
density classifications
31
z]
Cancel | Environments... | << Hide Help	Tool Help
d
Figure 3-6: Opening Screen for the Reclassify Housing Density Tool
3.5.
SERGOM V3 GROWTH MODEL
Populate the required fields. Travel time weights can be adjusted manually to affect the density
of growth. The default weights for each scenario are listed in Table 2-2.
Block housing density rasters will automatically be placed in the "output" folder.
Clicking inside any blank field will bring up text in the Tool Help window.
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£ SERGoM v3


j Navigate to "ICLU5_vl" folder:
I	J2?
j Create housing density rasters beginning with year:	
I	3
j .. .and ending with year:	
r
, ICLU5 Scenario
I	
17 Use the default travel time weights for this scenario
Travel time <5 minutes: (optional)
3
~3
Travel time 5^10 minutes: (optional)
f
Travel time 10- 20 minutes: (optional)
r
Travel time 20-30 minutes: (optional)
r
Travel time 30-45 minutes: (optional)
Travel time > 45 minutes: (optional)
f
Use default household size assumption for this scenario
Household size adjustment: (optional)	
J
OK
Cancel
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SERGoM v3
SERGoM the Spatially
Explicit Regional Growth
Model has been under
development for nearly a
decade and a number of
papers describe it more
fully (Theobald 2001 2003.
2005). It has also 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
qvi irhan/lnw-Honcitv*
Tool Help

Figure 3-7: Opening Screen for SERGoM v3 Growth Model Tool
Only the fields with green dots are necessary for the tool to run. Selecting the various pulldowns
will select the various scenarios one can run through the tool.
3.6.
IMPERVIOUS SURFACE TOOLS
Impervious Surface Tools are described below. These tools are designed to be used with ICLUS
generated housing density data at resolutions < 1 kilometer.
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DRAFT - ICL US SERGoM v3 User's Guide - DRAFT
7. ESTIMATE PERCENT IMPERVIOUS SURFACE
This tool reclassifies block housing density values into estimated percent impervious categories.
A Categorical Regression Tree model was used to develop a regression equation that describes
the relationship between the Percent Urban Imperviousness (PUI) dataset produced by the
MRLC Consortium and housing density. This relationship was then converted into a set of
conditional statements which form the basis for this tool. For a detailed description of the
methods and sources used to derive the percent impervious surface values, see the full report.
S Estimate Percent Impervious Surface

^JnJxJ

j Input Raster(s)
&|
+i
*1
~I
±j

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d
Estimate Percent
Impervious Surface
Reclassifies projected
housing density into
estimated percent
impervious surface values.
Note that resolution of the
housing density raster
must be less than or equal
to 1 km.
zi
Tool Help
Figure 3-9: Opening Screen for the Estimate Percent Impervious Surface Tool
8. SUMMARIZE IMPERVIOUS SURFACE BY HOUSING DENSITY CLASS
- This tool uses housing density classes to summarize estimated percent impervious surface.

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DRAFT - ICLUS SERGoM v3 User's Guide - DRAFT
Table 3-1. Housing density class definitions
Class
Zone
Value
Density
Housing
Density
Raster Value
Rural
1
>16.18
ha/unit
<62
Exurban
2
0.68 -16.18
ha/unit
62- 1471
Suburban
3
0.1-0.68
ha/unit
1471 - 10000
Urban
4
<0.1 ha/unit
>10000
| S Summarize Impervious Surface by Housing Density Class
-ln| x|
j Impervious burface Raster


J
Summarize
r


Impervious Surface
j Housing Density Raster


by Housing Density
r


Class
j Output Folder




i

Tabulates estimated



percent impervious
statistics by four housing
density classes: rural,
exurban suburban, and
urban.


m
d
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Figure 3-10: Opening Screen for the Summarize Impervious Surface by Housing Density
Class Tool
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DRAFT - ICL US SERGoM v3 User's Guide - DRAFT
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.
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/voll0/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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