Estimating small-area population growth using geographic-knowledge-guided cellular automata

Abstract

Estimation of small-area population counts in an intercensal year and in a future year is a challenging task. This paper presents preliminary results in the development of a geographic-knowledge-guided cellular automata (CA) for modelling growth in small geographic areas. Geographic knowledge contains rules dictating growth patterns that typically cannot be captured by a traditional CA model. Nighttime stable light images and census population counts in censal years are used to determine base-year population counts in each cell in the CA model, and these estimated base-year population counts are used to manually calibrate the model. We use census data in 1990 and 2000 in El Paso County of Texas as the base-year population data, develop a set of rules based on specific urban-growth situations in El Paso and use the model to estimate population counts in block groups in a future year in the study area. Preliminary results in El Paso County suggest that the model has the potential to produce reasonably accurate population counts in sub-county areas in a future year. Future work will include the development of computational procedures that can be used to automate the calibration of the CA model.