Using a Cellular Automata
Urban Growth Model to
Estimate the Completeness of
an Aggregated Road Dataset
Tiernan Erickson
U.S. Census Bureau
Background
Address Canvassing:
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Census workers compare what they see on the ground
to what is shown on the Census Bureau's address list.
Based on their findings, the census workers will verify,
update, or delete addresses already on the list, and add
addresses that are missing from the list.
At the same time, they will also update maps so they
accurately reflect what is on the ground.
Housing unit addresses verified: 145 million
Census workers hired for address canvassing: 140,000
Source: U.S. Census Bureau. Address Canvassing Facts/Statistics. Retrieved June 16, 2012, from
http://2010.census.gov/ news/press-kits/one-year-out/address-canvasing/
address-canvassing-facts-statistics.html
Background
Geographic Support System (GSS) Initiative:
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Integrated program in support of the 2020 Census:
 Improved address coverage
 Continual spatial feature updates
 Enhanced quality assessment and measurement
A targeted, rather than full, address canvassing operation
during 2019 in preparation for the 2020 Census.
Collaboration with federal, state, local, and tribal
governments and other key stakeholders to establish an
acceptable address list for each geographic entity.
Source: U.S. Census Bureau. Geographic Support System (GSS) Initiative.
Retrieved June 16, 2012, from http://www.census.gov/geo/www/gss/index.html
Background
Geographic Support System (GSS) Initiative:
 Positional Accuracy
 Thematic Accuracy
 Temporal Accuracy
 Logical Consistency
Completeness?
Spatial Data Completeness
Spatial Data Completeness
Detroit, MI
Source: Google Maps
Spatial Data Completeness
South of Austin, TX
Source: Google Maps
Urban Growth Forecasting Models
Source: Project Gigalopolis
http://www.ncgia.ucsb.edu/projects/gig/v2/About/abImages/apps/wash-balt_1792-2100.htm
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Urban Growth Forecasting Models
Cellular Automata Urban Growth Models
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Generate realistic urban patterns
Integrate the modeling of the spatial and
temporal dimensions of urban processes.
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Image Source: Cutsinger (2006)
-Santé, et al. (2010)
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Urban Growth Forecasting Models
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SLEUTH Model
S - Slope
L - Landuse
E - Exclusion
U - Urban Extent
T - Transportation
H - Hillshade
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SLEUTH Model
S - Slope
L - Landuse
E - Exclusion
U - Urban Extent
T - Transportation
H - Hillshade
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SLEUTH Model
S - Slope
L - Landuse
E - Exclusion
U - Urban Extent
T - Transportation
H - Hillshade
13
SLEUTH Model
S - Slope
L - Landuse
E - Exclusion
U - Urban Extent
T - Transportation
H - Hillshade
14
SLEUTH Model
S - Slope
L - Landuse
E - Exclusion
U - Urban Extent
T - Transportation
H - Hillshade
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SLEUTH Model
S - Slope
L - Landuse
E - Exclusion
U - Urban Extent
T - Transportation
H - Hillshade
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SLEUTH Model
Model Parameters
(“Urban DNA”):
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Diffusion
Breed
Spread
Slope Resistance
Road Gravity
SLEUTH Model
Source: Clarke et al. (1997)
SLEUTH Model
Source: Clarke et al. (1997)
SLEUTH Model
Source: Project Gigalopolis
http://www.ncgia.ucsb.edu/projects/gig/v2/About/abImages/apps/wash-balt_1792-2100.htm
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SLEUTH-3r
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More efficient
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Jantz et al. (2009)
Used to model entire
Chesapeake Bay
drainage
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Uses different
measures of “fit” to
compare prediction with
actual for calibration and
validation
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SLEUTH and SLEUTH3r are free and run on
Unix (Cygwin)
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Requires 1.5G RAM
Methods
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Use SLEUTH-3r
NLCD available for 1992,
2001, and 2006.
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Calibration:
 1992 – 2001
Prediction:
 2006(est.)
Validation:
 2006(est.) vs. 2006 (actual)
Methods
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Expected Project Output
SLEUTH's Output:
 Series of rasters showing percent likelihood of new
development for each cell, for each year between 2001
and 2006
Research Product:
 Aggregate prediction values to the tract level.
 For each tract last updated more than a year previous
to 2006, the percent-likelihood of development will be
summed for each year since last updated.
 If the sum total of unaccounted-for growth is above a
threshold, then the tract is in need of updating.
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Significance
Estimate of completeness of aggregated
road dataset (TIGER)
Incomplete in areas where:
1) Road growth is occurring rapidly, and
2) Have not been updated recently
Complete (save resources) in areas where:
1) Little or no growth, or
2) Have been updated recently
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Limitations
SLEUTH does not get into the causes of urban growth
(as inputs).
Instead, focuses on measuring and predicting a region's
growth pattern ("Urban DNA") regardless of underlying
causes.
 Diffusion
Slope Resistance
 Breed
Road Gravity
 Spread
SLEUTH does not put constraints on growth, such as:
 Population Growth Projections
 Economic Growth Projections
Extrapolates from previous growth. Uses Self-Modification
Rules ("Boom" and "Bust") to produce realistic S-shaped
growth curve projections based on recent growth, but not
constrained to match other models' projections.
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Limitations
Are SLEUTH's predictions of urban growth a satisfactory
proxy for predictions of road network growth?
 This study will provide an answer to that question
 Compare actual 2006 road network growth using same
goodness-of-fit metrics that SLEUTH uses for Validation.
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Possibilities
Adapt model to constrain the outputs to match
population or economic growth projections
Adapt the model to make use of demographic inputs
New NLCD data (2011) scheduled for release in
December 2013

Updated projections for the rest of this decade
Imagery for specific areas could be processed to
create more frequent land cover datasets with which to
update predictions.
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Possibilities
Eventually it could be useful to model urban growth for
the entire country.
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SLEUTH's creator, Keith C. Clarke, has said that
he would like to see the model used for the entire
United States (Clarke, 2008 and 2011).
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Jantz et al. 2009 study of the entire Chesapeake
Bay watershed (208 counties) remains the largest
application of SLEUTH to date that I found in the
literature.
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An eventual nation-wide simulation could provide
estimates of completeness of coverage for TIGER
that could support the Census Bureau's stated
goals for targeted update operations.
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Next Steps
Download and install Cygwin (Unix environment for Windows)
Read Cygwin documentation
Download and install SLEUTH software
Read Cygwin documentation
Download remaining TIGER datasets (Tract, All LInes)
Convert TIGER/Line files to shapefiles
Merge DEMs for test county
Run SLEUTH on test county, probably my hometown for familiarity:
 Pima County, AZ (04019)
Estimate reasonable number of counties to process
Select counties for study
Streamline data download, setup, model run process
Download data for additional counties
Run SLEUTH model on counties
Validate output to 2006 land cover
Validate 2006 roads to 2006 land cover
Simulate TIGER update dates by Tract
Compare update dates to urban growth predictions by Tract
Create percent-likelihood-incomplete estimates by tract
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Questions? Comments?
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