Spatiotemporal Association Between Temperature and Assaults: A Generalized Linear Mixed-Model Approach

Abstract

We aim to analyze the association between temperature and assault at highly disaggregated spatial units with great temporal resolution to investigate their spatiotemporal dynamics. We applied generalized linear mixed models (GLMMs) to assault and weather data from 2015, aggregated weekly at 424 subdistricts in Seoul, South Korea, controlling for various socioeconomic and environmental variables. Analyses revealed a positive and significant linear association between temperature and assaults and a few small but significant interaction effects that relate to an increase in assaults. A more enhanced understanding of the spatiotemporal relationship between temperature and crime would provide useful implications for targeted crime prevention and resource allocations.

A Novel Approach for Mining Spatiotemporal Explicit and Implicit Information in Multiscale Spatiotemporal Data

“…Similar to the temporal dimension, the same adjacency or disjoint relationship exists in the spatial dimension, as shown in Figure 9. P(s1,s2) = 0 in the case (a,d), and (b,e) whose spatiotemporal correlation is as the second equation in Equation (9). P(s1,s2) = 1 in the case of (c,f).…”

“…Jin et al [8] explored the spatiotemporal relationship between regional tourism economies and other variables using a semi-parametric geographically weighted regression (GWR) model capable of capturing both global and local spatiotemporal relationships. Jung et al [9] utilized generalized linear mixed models (GLMMs) to analyze the association between temperature and aggression in space-time. While these spatiotemporal mining methods effectively consider the dynamic and heterogeneous characteristics of the spatiotemporal dimension by synergizing the temporal and spatial dimensions, a single-scale perspective limits the exploitation of the full information contained in spatiotemporal data, especially when it exists at other scales.…”