Xiaokun Wang

Bucknell University, Lewisburg, Pennsylvania, United States

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Publications (9)5.09 Total impact

  • Source
    Xiaokun Wang, Kara M. Kockelman
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    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.ResumenLa evolución del desarrollo del suelo en áreas urbanas ha sido de gran interés para formuladores de políticas 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 dinámico (DSOP, por sus siglas en inglés) 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 imágenes de satélite. La muestra contiene 2,771 de estas celdas, distribuidas entre 57 regiones de códigos postales. Los efectos marginales de las variables de control sugieren que los incrementos en la duración de los desplazamientos al distrito central de negocios (CBD, por sus siglas en inglés) reducen sustancialmente la intensidad del desarrollo urbano del suelo. Con mayor importancia, los efectos de autocorrelación temporal y espacial son significativamente positivos, mostrando la superioridad del modelo DSOP. Los parámetros derivados son utilizados para predecir patrones futuros de desarrollo urbano del suelo, junto con la incertidumbre asociada a la predicción para cada celda de la malla.
    Papers in Regional Science 05/2009; 88(2):345 - 365. · 1.43 Impact Factor
  • Source
    Xiaokun Wang, Kara M. Kockelman
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    ABSTRACT: Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial-ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for interregional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time-series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, nonspatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two-dimensional autocorrelation. Copyright (c) 2009, Wiley Periodicals, Inc.
    Journal of Regional Science 01/2009; 49(5):877-913. · 2.00 Impact Factor
  • Source
    Xiaokun Wang, Kara M. Kockelman
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    ABSTRACT: Annual average daily traffic (AADT) values have long played an important role in transportation design, operations, planning, and policy making. However, AADT values are almost always rough estimates based on the closest short-period traffic counts, factored up using permanent automatic traffic recorder data. This study deve lops Kriging-based methods for mining network and count data, over time and space. Using Texas highway count data, the method forecasts future AADT values at locations where no traffic detectors are present. While low-volume road counts remain difficult to predict, available expl anatory variables are very few, and extremely high-count outlier sites skew pr edictions in the data set us ed here, overall AADT-weighted median prediction error is 31% percent (across al l Texas network sites). Here, Kriging performed far better than other options for spatial extrapolation − such as assigning AADT based on a point's nearest sampling site, which yields e rrors of 80%. Beyond AADT estimation, Kriging is a promising way to explore spatial relationships across a wide variety of data sets, including, for example, pavement conditions, traffic speeds, population densities, land values, household incomes, and trip generation rates. Further refinements, including spatial autocorrelation functions based on network (rather than Euclid ean) distances and inclusion of far more explanatory variables exist, and will further enhance estimation.
    Transportation Research Record. 01/2009; 2105.
  • Source
    Xiaokun Wang, Kara Kockelman
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    ABSTRACT: In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects. Copyright Springer Science+Business Media, LLC 2007
    Transportation 02/2007; 34(3):281-300. · 1.66 Impact Factor
  • Source
    Xiaokun Wang, Kara M Kockelman
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    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. Using a mixed logit framework, this paper studies 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 paper describes the computational methods used for model estimation and application, 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 visually confirm these results and appreciate the variability in potential urban futures.
    Transportation Research Record. 01/2006; 1977.
  • Source
    Xiaokun Wang, Kara Kockelman
    Transportation Research Record. 01/2005; 1908(1):195-204.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It is found that the DSOP model yields much more accurate estimates than standard, non-spatial techniques. As for model selection, the DSOP model is clearly preferred to standard OP, dynamic OP and spatial OP models. These methods are then used to analyze land use changes over an 18-year period in Austin, Texas. In this analysis, temporal and spatial autocorrelation effects are found to be significantly positive. In addition, increases in travel times to the region's central business district (CBD) are estimated to substantially reduce land development intensity. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two-dimensional autocorrelation.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Many databases involve ordered discrete responses in a temporal and spatial context, including, for example, land development intensity levels, vehicle ownership, and pavement conditions. An appreciation of such behaviors requires rigorous statistical methods, recognizing spatial effects and dynamic processes. This study develops a dynamic spatial ordered probit (DSOP) model in order to capture patterns of spatial and temporal autocorrelation in ordered categorical response data. This model is estimated in a Bayesian framework using Gibbs sampling and data augmentation, in order to generate all autocorrelated latent variables. It incorporates spatial effects in an ordered probit model by allowing for inter-regional spatial interactions and heteroskedasticity, along with random effects across regions or any clusters of observational units. The model assumes an autoregressive, AR(1), process across latent response values, thereby recognizing time-series dynamics in panel data sets. The model code and estimation approach is tested on simulated data sets, in order to reproduce known parameter values and provide insights into estimation performance, yielding much more accurate estimates than standard, non-spatial techniques. The proposed and tested DSOP model is felt to be a significant contribution to the field of spatial econometrics, where binary applications (for discrete response data) have been seen as the cutting edge. The Bayesian framework and Gibbs sampling techniques used here permit such complexity, in world of two-dimensional autocorrelation.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Econometric models are a powerful tool for analyzing regional issues. Complex models are normally intractable and require special estimation methods. Maximum simulated likelihood estimation (MSLE) techniques have become popular in recent years, and are being included in new software releases (such as STATA and Limdep). It is important that analysts understand the relative performance of different simulation techniques under various data circumstances. This especially true in regional studies, where observations are often spatially correlated. This paper studies the performance of several simulation techniques with spatially correlated observations. Quasi Monte-Carlo (QMC) methods are found to impose a strong periodic correlation pattern across observations. While some forms of sequencing, such as scrambled Halton, Sobol and Faure, can sever correlations across dimensions of error-term integration, they cannot remove the correlation that exists across observations. When a data set's true correlation patterns clearly differ from the simulated patterns, model estimation may become inefficient; and, with finite samples, statistical identification of parameters may suffer. Fortunately, here we find that, at least within the mixed logit framework, even when observations are correlated, QMCs and hybrid methods are typically preferred to pseudo Monte-Carlo methods, thanks to their better coverage. These findings offer an important supplement to existing studies of spatial model estimation and should prove valuable for future work that requires simulated likelihoods with spatially correlated observations.

Publication Stats

53 Citations
5.09 Total Impact Points

Institutions

  • 2009
    • Bucknell University
      • Department of Civil and Environmental Engineering
      Lewisburg, Pennsylvania, United States
  • 2006–2007
    • University of Texas at Austin
      • Department of Civil, Architectural & Environmental Engineering
      Austin, Texas, United States