Spatio-temporal differences in model outputs and parameter

space as determined by calibration extent

7th International Conference on Geocomputation

Charles K. DietzelDepartment of Geography

University of California – Santa Barbara

Acknowledgements• Public Policy Institute of California• Dr. Keith C. Clarke, Advisor, UCSB• USGS Rocky Mountain Mapping Center

– Mark Feller (Geographic Analysis and Monitoring Program)

Presentation Outline• Introduction• Methods• Results• Conclusions• Broader impacts

Introduction• Increased computational power of computers• More geospatial data sources• Dependence on both of these to better urban

and land use models• Do outside areas influence the focal study

area?• To what degree?• SLEUTH urban growth model (Clarke et al,

1997)

Methods• SLEUTH (Slope, Land use, Exclusion, Urban,

Transportation, Hillshade)• Cellular Automata• Rigorously tested and widely used

– Santa Barbara (Candau and Goldstein, 2002)– San Francisco (Clarke et al, 1997)– New York (Esnard and Yang, 2002)– Washington-Baltimore (Clarke and Gaydos, 1998)– Lisbon and Porto (Silva and Clarke, 2002)

• ‘Brute force’ calibration of historical data

Methods• Five parameters of calibration

– Diffusion– Breed– Spread– Slope resistance– Road gravity

Methods• Calibrated San Joaquin Valley dataset at three extents

– San Joaquin County (local)– Bay Area Influenced (regional)– Entire San Joaquin Valley (global)

Results

Extent Diffusion Breed Spread Slope RoadSan Joaquin Co. 2 2 54 1 3Bay Area 2 4 47 30 3San Joaquin Valley 2 2 45 25 4

Coefficients

Results

Urbanization of San Joaquin County Over Time Using Local, Regional, and Global Parameters

35000

55000

75000

95000

115000

135000

155000

175000

2001 2006 2011 2016 2021 2026 2031 2036

Year

Tota

l Urb

an A

rea

(ha)

San Joaquin County

Bay Area

San Joaquin Valley

Results

New Urban Areas Per Year Using Local, Regional, and Global Parameters

1500

1700

1900

2100

2300

2500

2700

2900

3100

3300

2001 2006 2011 2016 2021 2026 2031 2036

Year

New

Urb

an A

rea

(ha)

San Joaquin County

Bay Area

San Joaquin Valley

Conclusions• Calibration at different extents shows that

local, regional, and global growth cannot be generally characterized

• Relative to regional and global growth, San Joaquin County is growing faster

• This method may be used to explore growth dynamics in any system

Broader Impacts• Areas outside the focal study area can change

the parameter space, and hence the forecasting, of models

• Inclusion of outside areas can characterize local growth relative to a larger scale

• Increased computing power warrants modelers including possibly influential areas in future efforts

• Modelers should spend more time considering the geographic extent of their models

ReferencesCandau, J., Goldstein, N.C. (2002). Multiple scenario urban forecasting for the California

South Coast region. Proceedings of the 40th Urban Regional Information Systems Association, Chicago, IL, October 26-30, 495-506.

Clarke, K.C., Gaydos, L. (1998). Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographic Information Science, 12 (7): 699-714.

Clarke, K.C., Hoppen, S., Gaydos, L. (1997). A self-modifying cellular automata model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24: 247-261.

Esnard, A.M., Yang, Y. (2002). Descriptive and Comparative Studies of 1990 Urban Extent Data for the New York Metropolitan Region. URISA Journal, 14 (1): 57-62.

Silva, E. A., Clarke, K.C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems 26: 525-552.