Effects of intrinsic sources of spatial autocorrelation on spatial regression modelling

摘要

1. Detecting and dealing with spatial autocorrelation (SA) are indispensable steps in analyses of geospatial data. Despite consensus that SA originates from both extrinsic factors and intrinsic interactions, previous studies on regression analysis of spatially autocorrelated data have rarely controlled for intrinsic sources in addition to extrinsic ones to assess ceteris paribus (i.e. causal) effects of interest, with the strict exogeneity assumption that errors containing unexplained variance are uncorrelated with explanatory variables for all observations. This assumption becomes invalid when intrinsic SA is not an external process modelled as errors and needs to be controlled for.