【摘要】Spatial data sets pose challenges for discrete choice models because the data are unlikelyto be independently and identically distributed. A conditionally parametric spatial probit model isamenable to very large data sets while imposing far less structure on the data than conventionalparametric models. We illustrate the approach using data on 474,170 individual lots in the City ofChicago. The results suggest that simple functional forms are not appropriate for explaining the spatialvariation in residential land use across the entire city.

【文献来源】McMillen D;Soppelsa M E.Journal of Regional Science.2015（3）