Abstract:
As an essential part of integrated land use–transport models, prediction of land cover changes and illumination of the many factors behind such change are always of interest to planners, policy makers, developers, and others. A mixed logit framework is used to study 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 computational methods used for model estimation and application are described, 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 confirm these results visually and appreciate the variability in potential urban futures.
