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As an essential part of integrated land use -transport models, prediction of land cover changes and illumination of the many factors behind such c hange are always of interest to planners, policy makers, developers and others. Using a mixed logit framework, this paper studies land cover evolution in the Austin, Texas region, recognizing distance -dependent correlations -- both observed and unobserved -- over space and time, in a sea of satellite image pixels. The paper describes the computational methods used for model estimation and application, including generalized Cholesky decomposition and likelihood simulation. Results indicate that neighborhood characteristics have strong effects on land cover evolution: Clustering is significant over time, but high residential densities can impede future development. Model application produces graphic predictions, allowing one to visually confirm these results a nd appreciate the variability in potential urban futures.

Tracking Land Cover Change in Mixed Logit Model: Recognizing Temporal and Spatial Effects