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Spatio-temporal differences in model outputs and parameter space as determined by calibration extent. 7 th International Conference on Geocomputation Charles K. Dietzel Department of Geography University of California – Santa Barbara. Acknowledgements. Public Policy Institute of California - PowerPoint PPT Presentation
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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.