Journal of Transport Geography, Volume 31, July 2013, Pages 123–131
Yiyi Wang, Kara M. Kockelman, Xiaokun (Cara) Wang
- We explore use of spatial filtering (SF) for regression model estimation.
- We compare SF models and SAR-type models, and a distance decay parameter.
- Data sets contain appraised values for private properties across Texas’ Travis County.
- SF methods allow focus on the marginal effects of policy variables and other covariates.
“This paper summarizes the literature on spatial filtering (SF) for analysis of spatial data. Given the scarcity of its application in transportation and its fledgling nature, preliminary case studies were conducted using continuous and discrete response data sets, for land values and land use, in comparison with results from spatial autoregressive (SAR) models with distance decay parameters estimated using Bayesian techniques. For both the continuous land value and binary land use cases, the SF approach demonstrates great potential as a worthy competitor to more conventional SAR-based models. In addition to offering high fit statistics, somewhat shorter computing times, and more straightforward computations, the SF approach makes explicit the patterns of spatial dependency in the land value and land use data. By controlling for these spatial relationships, the SF approach yields more reliable marginal effects of policy variables of interest. Model results confirm the important role of transportation access (as quantified using distances to a region’s central business district, and various roadway types).”