Understanding Spatial Filtering for Analysis of Land Use Data Sets

This paper summarizes the literature on spatial filtering for analysis of spatial data, as proposed by Griffith (2000a). 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 models with distance decay parameters estimated using Bayesian techniques. For both the continuous land value and binary land use cases, the spatial filtering approach demonstrates great potential as a worthy competitor to more conventional Bayesian spatial-autoregressive models, offering high fit statistics, somewhat shorter computing times, and more straightforward computations.