GEOSTATS: forecasting development from spatio-temporal data
forecasting development from spatio-temporal data
I have a block level census data map (California, USA) with attributes that give the density (per sq mi) of structures for the years 1939, 49, 59, 69, 79, 89 and 90. I want to use the time-series to project future development, i.e., the number of new structures in each block, say, by 2020. What is the right way to model this? I hypothesize that very low and very high density areas are special cases, in that some places are not imminently developable due to geophysical barriers, etc, while very tightly packed areas (i.e., very small blocks) are essentially "built out." There is spatial autocorrelation to deal with, over an obviously anisotropic landscape, and the nature of urban development has shifted over time from urban infilling, to suburban expansion, to exurban growth in the foothills.
Any suggestions will be heartily appreciated.
Jim Spero, Fire Economics Analyst
Fire and Resource Assessment Program
California Department of Forestry and Fire Protection
1920 20th Street
Sacramento, CA 95814