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Submitted
September 24, 2025
Accepted
October 14, 2025
Published
October 30, 2025
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Abstract

This study analyzes the spatio-temporal modeling of crime rates in 35 regencies and cities in Central Java using the geographically and temporally weighted regression (GTWR) method. The objective is to investigate how socio-economic factors, including the open unemployment rate, percentage of the poor population, population density, average years of schooling, job vacancies, labor force participation rate, and labor wage, influence crime rates across different regions and periods. The goodness-of-fit test results indicateed that the GTWR model had an R-squared value of 93.51%, higher than the 88.64% of the geographically weighted regression (GWR) model, demonstrating GTWR’s ability to explain crime data variations that were heterogeneous both spatially and temporally. Partial significance tests and mapping results showed that the influence of variables differed across years and regions, with population density and labor-related factors consistently being the main predictors. These findings highlight the importance of designing crime prevention policies that are locally tailored and based on spatio-temporal evidence.  

Keywords

Crime Rates Socio-Economic Factors Spatio-Temporal Heterogeneity GWR GTWR

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How to Cite
Putra, R. P., & Zai, F. N. (2025). Spatio-Temporal Modeling of Crime Rates Using Geographically and Temporally Weighted Regression . Enthusiastic : International Journal of Applied Statistics and Data Science, 5(2), 139–152. https://doi.org/10.20885/enthusiastic.vol5.iss2.art4
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