A Nonparametric Approach to Measure the Heterogeneous Spatial Association: Under Spatial Temporal Data

TL;DR

This paper introduces a novel entropy-based, data-driven method to measure heterogeneous spatial association in spatio-temporal data, overcoming limitations of traditional distance-based approaches and weight matrices.

Contribution

It proposes a new distribution-free approach that does not rely on distance or weight matrices, effectively capturing heterogeneity in spatial association.

Findings

The method successfully measures spatial association without geographical location data.

It handles heterogeneity and asymmetrical dependence in spatio-temporal data.

Applicable under stationary m-dependent temporal conditions.

Abstract

Spatial association and heterogeneity are two critical areas in the research about spatial analysis, geography, statistics and so on. Though large amounts of outstanding methods has been proposed and studied, there are few of them tend to study spatial association under heterogeneous environment. Additionally, most of the traditional methods are based on distance statistic and spatial weighted matrix. However, in some abstract spatial situations, distance statistic can not be applied since we can not even observe the geographical locations directly. Meanwhile, under these circumstances, due to invisibility of spatial positions, designing of weight matrix can not absolutely avoid subjectivity. In this paper, a new entropy-based method, which is data-driven and distribution-free, has been proposed to help us investigate spatial association while fully taking the fact that heterogeneity widely exist. Specifically, this method is not bounded with distance statistic or weight matrix. Asymmetrical dependence is adopted to reflect the heterogeneity in spatial association for each individual and the whole discussion in this paper is performed on spatio-temporal data with only assuming stationary m-dependent over time.