Mapping and Modeling Crime Factors in North Sumatra Using GWGPR
DOI:
https://doi.org/10.34123/jurnalasks.v18i1.972Keywords:
Akaike Information Criterion, Crime Cases, Geographically Weighted Generalized Poisson Regression, North Sumatra, Overdispersion, Spatial Heterogeneity, Spatial ModelingAbstract
Introduction/Main Objectives: Crime remains a significant social issue influenced by socio-economic factors and exhibiting spatial variation, particularly in North Sumatra Province, which recorded the highest number of criminal cases in Indonesia in 2024. This study aims to identify significant factors affecting crime and examine the spatial variation of their effects across districts/cities in North Sumatra. Background Problems: Global regression models often fail to capture crime patterns due to overdispersion and spatial heterogeneity, leading to inconsistent relationships across regions. Novelty: This study employs Geographically Weighted Generalized Poisson Regression (GWGPR), which simultaneously addresses overdispersion and spatial heterogeneity, providing a more robust localized analysis than global models. Research Methods: Using secondary data from 33 districts/cities in North Sumatra, the variables include population density, open unemployment rate, mean years of schooling, and Gini ratio. The analysis involves Poisson regression,dispersion testing,Generalized Poisson Regression, spatial heterogeneity testing, and GWGPR. Finding/Results: The significant factors affecting crime are the open unemployment rate, mean years of schooling, and population density, while the Gini ratio is not significant. Limitation: This study is limited by the use of data covering only the year 2024 and a limited set of socio-economic variables, which may not fully capture all factors associated with crime.
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