2022/10/17 by René Westerholt · 47 citations
Economics, Econometrics and Finance · Environmental Science · Mathematics · #Artificial intelligence #Autocorrelation #Combinatorics #Computer science #Context (archaeology) #Econometrics #Economic and Environmental Valuation #Geography #Inference #Land Use and Ecosystem Services #Mathematics #Null hypothesis #Null model #Physics #Point process #Random effects model #Randomness #Spatial analysis #Spatial and Panel Data Analysis #Spatial dependence #Statistical inference #Statistical physics #Statistics
paper · pdf · doi:10.1111/gean.12349
published in Geographical Analysis 55(4), 621-650 (Wiley)
openalex publication_date 2022/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Inference procedures for spatial autocorrelation statistics assume that the underlying configurations of spatial units are fixed. However, sometimes this assumption can be disadvantageous, for example, when analyzing social media posts or moving objects. This article examines for the case of point geometries how a change from fixed to random spatial indexes affects inferences about global Moran's I, a popular spatial autocorrelation measure. Homogeneous and inhomogeneous Matérn and Thomas cluster processes are studied and for each of these processes, 10,000 random point patterns are simulated for investigating three aspects that are key in an inferential context: the null distributions of I when the underlying geometries are varied; the effect of the latter on critical values used to reject null hypotheses; and how the presence of point processes affects the statistical power of Moran's I. The results show that point processes affect all three characteristics. Inferences about spatial structure in relevant application contexts may therefore be different from conventional inferences when this additional source of randomness is taken into account.