Application of the dynamic spatial ordered probit model

Abstract The evolution of land development in urban area has been of great interest to policy‐makers and planners. Due to the complexity of the land development process, no existing studies are considered sophisticated enough. This research uses the dynamic spatial ordered probit (DSOP) model to analyse Austin's land use intensity patterns over a 4‐point panel. The observational units are 300 m × 300 m grid cells derived from satellite images. The sample contains 2,771 such grid cells, spread among 57 zip code regions. The marginal effects of control variables suggest that increases in travel times to central business district (CBD) substantially reduce land development intensity. More important, temporal and spatial autocorrelation effects are significantly positive, showing the superiority of the DSOP model. The derived parameters are used to predict future land development patterns, along with associated uncertainty in each grid cell's prediction. Resumen La evolucion del desarrollo del suelo en areas urbanas ha sido de gran interes para formuladores de politicas y urbanistas. Debido a la complejidad del proceso de desarrollo urbano, se considera que los estudios existentes no son lo suficientemente sofisticados. Este estudio utiliza el modelo probit ordenado espacial dinamico (DSOP, por sus siglas en ingles) para analizar los patrones de intensidad de uso del suelo sobre un panel de 4 puntos. Las unidades de estudio son celdas en una malla de 300m x 300 m a partir de imagenes de satelite. La muestra contiene 2,771 de estas celdas, distribuidas entre 57 regiones de codigos postales. Los efectos marginales de las variables de control sugieren que los incrementos en la duracion de los desplazamientos al distrito central de negocios (CBD, por sus siglas en ingles) reducen sustancialmente la intensidad del desarrollo urbano del suelo. Con mayor importancia, los efectos de autocorrelacion temporal y espacial son significativamente positivos, mostrando la superioridad del modelo DSOP. Los parametros derivados son utilizados para predecir patrones futuros de desarrollo urbano del suelo, junto con la incertidumbre asociada a la prediccion para cada celda de la malla.

[1]  D. Pfeffermann,et al.  Small area estimation , 2011 .

[2]  K. Kockelman,et al.  Baysian Inference for Ordered Response Data with a Dynamic Spatial-Ordered Probit Model , 2009 .

[3]  A. Case.  Neighborhood influence and technological change , 1992 .

[4]  C. Robert,et al.  Deviance information criteria for missing data models , 2006 .

[5]  K. Kockelman,et al.  THE DYNAMIC SPATIAL ORDERED PROBIT MODEL : METHODS FOR CAPTURING PATTERNS OF SPATIAL AND TEMPORAL AUTOCORRELATION IN ORDERED RESPONSE DATA , USING BAYESIAN ESTIMATION , 2009 .

[6]  Philippe Girard,et al.  Bayesian Analysis of Autocorrelated Ordered Categorical Data for Industrial Quality Monitoring , 2001, Technometrics.

[7]  Michael D. McKay,et al.  Evaluating Prediction Uncertainty , 1995 .

[8]  J. Keith Ord,et al.  Spatial Processes Models and Applications , 1981 .

[9]  Gerald Nelson,et al.  Do Roads Cause Deforestation? Using Satellite Images in Econometric Analysis of Land Use , 1997 .

[10]  David N. Wear,et al.  Land-Use Changes in Southern Appalachian Landscapes: Spatial Analysis and Forecast Evaluation , 1998, Ecosystems.

[11]  Jane Southworth,et al.  The Dynamics Of Land-Cover Change In Western Honduras: Spatial Autocorrelation And Temporal Variation , 2001 .

[12]  Kara M. Kockelman,et al.  Neighborhood impacts on land use change: a multinomial logit model of spatial relationships , 2008 .

[13]  Kara M. Kockelman,et al.  Use of heteroscedastic ordered logit model to study severity of occupant injury: Distinguishing effects of vehicle weight and type , 2005 .

[14]  G. Jasso.  Review of "International Encyclopedia of Statistical Sciences, edited by Samuel Kotz, Norman L. Johnson, and Campbell B. Read, New York, Wiley, 1982-1988" , 1989 .

[15]  Kara Kockelman,et al.  Occupant Injury Severity using a Heteroscedastic Ordered Logit Model : Distinguishing the Effects of Vehicle Weight and Type , 2005 .

[16]  D. Schrank,et al.  THE 2004 URBAN MOBILITY REPORT , 2002 .

[17]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[18]  J. Landis,et al.  Rail Transit Investments, Real Estate Values, and Land Use Change: A Comparative Analysis of Five California Rail Transit Systems , 1995 .

[19]  P. Moran.  Notes on continuous stochastic phenomena. , 1950, Biometrika.

[20]  James P. LeSage,et al.  A BAYESIAN PROBIT MODEL WITH SPATIAL DEPENDENCIES , 2004 .

[21]  D. McMillen.  PROBIT WITH SPATIAL AUTOCORRELATION , 1992 .

[22]  P. Waddell.  UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning , 2002 .

[23]  K. Kockelman,et al.  Credit-Based Congestion Pricing: Travel, Land Value, and Welfare Impacts , 2003 .

[24]  J. LeSage.  Spatial Econometrics , 1998 .

[25]  Kara M. Kockelman,et al.  Tracking Land Cover Change in Mixed Logit Model: Recognizing Temporal and Spatial Effects , 2006 .

[26]  Antonio Páez,et al.  Exploring Contextual Variations in Land Use and Transport Analysis Using a Probit Model with Geographical Weights , 2006 .