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Sheryl Luzzadder-Beach, Chair 305 E. 23rd Street • CLA 3.306 • A3100 • Austin, Tx 78712 • 512-232-1595

Colloquium: Dr. Chandra Bhat

Fri, February 14, 2014 • 4:00 PM - 5:00 PM • CLA 0.128

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Please join the Department of Geography and the Environment for our Spring 2014 colloquium series (rescheduled from last week’s snow day). We will be hosting a presentation by Dr. Chandra Bhat, Distinguished Teaching Professor, Adnan Abou-Ayyash Centennial Professor in Transportation Engineering and Director of the Center for Transportation Research at University of Texas at Austin-

A New Spatial Multiple Discrete-Continuous Modeling Approach to Land Use Change Analysis

Dr. Bhat discusses a multiple discrete-continuous probit (MDCP) land-use model within a spatially explicit economic structural framework for land-use change decisions. The spatial MDCP model is capable of predicting both the type and intensity of urban development patterns over large geographic areas, while also explicitly acknowledging geographic proximity-based spatial dependencies in these patterns. In an associated paper, it methodologically focuses on specifying and estimating a spatial MDCP model that allows the dependent variable to exist in multiple discrete states with an intensity associated with each discrete state. The formulation also accommodates spatial dependencies, as well as spatial heterogeneity and heteroscedasticity, in the dependent variable, and should be applicable in a wide variety of fields where social and spatial dependencies between decision agents (or observation units) lead to spillover effects in multiple discrete-continuous choices (or states). At an empirical level, the paper models land-use in multiple discrete states, along with the area invested in each land-use discrete state, within each spatial unit in an entire urban region. The model is estimated using a maximum approximate composite marginal likelihood (MACML) inference approach. A simulation exercise is undertaken to evaluate the ability of the propose approach to recover parameters from a cross-sectional spatial MDCP model. The results show that the MACML approach does well in recovering parameters. The empirical analysis is undertaken using the City of Austin parcel level land use data for the year 2010. The estimation results indicate that proximity to highways and other roadways, area under floodplain, distance from schools and hospitals, and average elevation of the area are important determinants of land use change. 

This event is free and open to the public. For more information, email Madeline Enos at madelineenos@austin.utexas.edu.


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