1. GIS-based Urban Growth Simulation Modeling
Jim Westervelt
FOSS4G Conference
September, 2006
Lausanne, Switzerland

2. Acknowledgements Funding:
Strategic Environmental Research and Development Program
Development partners:
University of Illinois - LEAM lab
Dr. Brian Deal
Jeff Terstriep

3. The Desire
Predict the impact of regional plans on the development of new residential areas
Regional Plan:
Location of highways
Land use zoning
Government land uses

4. The Constraints Very inexpensive Use standard U.S. government GIS data
No local data
Very quick
Results with a few days
Automatic calibration

5. Current Urban Patterns
Cause-Effect
Future Population
Future Urban Patterns
Regional Plans
This follows up the previous slide. Its about future training and the planning needed today to sustain/develop the training opportunities.
So, how do installations/communities move towards a desirable future? JLUS - next slide.
Current Urban Patterns

6. Major Steps Identify current residential attractiveness
Generate future residential development

7. Identify Current Residential Attractiveness
Ramps
Roads
State Roads
Forest
Neighbors
Intersections
Slope
Water
Cities

8. Sample Steps (Ramp) Identify ramps on limited access highways
r.mapcalc
Identify ramps on limited access highways
Generate shortest driving time maps
Relate driving time to probability of residential
Create residential probability map
r.cost
(modified)
r.stats
r.mapcalc
(modified)

9. Modification to r.mapcalc1 20 2 3 4 5 30 40 X Y
Ability to specify an
equation with a graph
r.mapcalc ‘new=graph(old, .5,20, 2.2,21, 3.8,26, 5,40)’

10. Modification to r.cost
Challenge: accommodate roads that cross, but do not intersect
r.cost.new parameters:
input Name of raster map containing grid cell cost information
output Name of raster map to contain results
m2 Name of second input raster map containing cost information
xover Name of optional raster map containing crossover cells
start_sites Starting points site file
Can help with other mixed transportation uses
e.g. roads and train

11. Main “script” is a “makefile”
# Create a travel cost surface for travel to ramps (road cat 6)
rampTimeRoad: cross intTravelTime30 othTravelTime30 otherroads interstates
@echo '#########################'; echo ; date
-g.remove
# Look for ramp (cat 6) cells that border limited access roads (cat 1)
g.region res=30
r.mapcalc && (\
interstates[-1,-1]==1 || interstates[-1,0]==1 || interstates[-1,1]==1 || \
interstates[ 0,-1]==1 || interstates[ 0,1]==1 || \
interstates[ 1,-1]==1 || interstates[ 1,0]==1 || interstates[ 1,1]==1),1,null())' > \
/dev/null 2>&1
r.cost.new in=othTravelTime30 m2=intTravelTime30 \
xover=cross percent=100 \
> /dev/null 2>&1
-g.remove

12. Major Steps Identify current residential attractiveness
Generate future residential development

13. Generate future residential development
Program name: gluc (general land use change model)
Developer: Jeff Terstriep, et al, LEAMlab under direction of Dr. Brian Deal University of Illinois, Dept of Urban and Regional Planning

14. GLUC Inputs Map Inputs: Landcover Residential attractiveness No-growth
Value Inputs
Growth per year
# of time steps

15. GLUC Outputs
Maps:
Change
Summary

16. Change/Summary Movie

17. Summary
Planning need: rapidly and inexpensively test alternative regional planning ideas
GRASS + UNIX allowed
Development of new/modified GRASS commands
Creation of shell and makefile scripts to automate
Result: New ability to convert a proposed regional plan to projection of:
Attractiveness of land to residential development
Future residential urban patterns