A conditionally parametric probit model of microdata land use in chicago

Daniel P McMillen, Maria Edisa Soppelsa

Research output: Contribution to journalArticlepeer-review

Abstract

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

Original languageEnglish (US)
Pages (from-to)391-415
Number of pages25
JournalJournal of Regional Science
Volume55
Issue number3
DOIs
StatePublished - Jun 1 2015

ASJC Scopus subject areas

  • Development
  • Environmental Science (miscellaneous)

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