Small-Area Population Forecasting: A Holistic Theory-Based Spatio-Temporal Approach

Guangqing Chi, University of Wisconsin at Madison
Paul R. Voss, University of Wisconsin at Madison

Existing demographic forecasting techniques for small areas generally are not guided by theoretical models or demographically-driven conceptual schemes. In this study we propose and test a theory-based spatio-temporal approach to small-area population forecasting. In particular, we develop five indices (demographics, accessibility, developability, desirability, and livability) and apply a spatio-temporal regression model to examine population change at the Minor Civil Division (MCD) level in Wisconsin since 1970. For each MCD, the population growth rate for 1980-1990 is regressed on its growth rate for 1970-1980, its independent index variables in 1980, and neighborhood characteristics in 1980 and growth rates for 1970-1980. The estimated coefficients and spatial parameters are then used for projecting population in 2000. The accuracy of forecasts is assessed against 2000 census counts by some statistical measures, graphic plots, and residual maps. This proposed study helps meet policy demands for better small-area population forecast techniques.