Understanding spatial filtering for analysis of land use-transport data

摘要: This paper summarizes the literature on spatial filtering (SF) for analysis of data. Given scarcity its application in transportation and fledgling nature, preliminary case studies were conducted using continuous discrete response data sets, land values use, comparison with results from autoregressive (SAR) models distance decay parameters estimated Bayesian techniques. For both value binary use cases, SF approach demonstrates great potential as a worthy competitor to more conventional SAR-based models. In addition offering high fit statistics, somewhat shorter computing times, straightforward computations, makes explicit patterns dependency By controlling these relationships, yields reliable marginal effects policy variables interest. Model confirm important role access (as quantified distances region’s central business district, various roadway types).