(Department of Statistics, Universitas Islam Indonesia, Indonesia)(2) * Tuti Purwaningsih
(Universitas Islam Indonesia, Indonesia)(3) Shoffan Saifullah
(Institute of Computer Science, AGH University of Krakow, Poland)(4) Felix Andika Dwiyanto
(Institute of Computer Science, AGH University of Krakow, Poland)(5) Ilyos Rabbimov
(Center for Economic Research and Reform under Administration of the President of the Republic of Uzbekistan, Uzbekistan)*corresponding author
AbstractEconomic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
KeywordsCrime determinants; Central java; Spatial analysis; Regression analysis; Socio-economic factors
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DOIhttps://doi.org/10.31763/sitech.v3i1.1212 |
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