Space-Time Autoregressive Integrated Moving Average (STARIMA) Modeling for Predicting Criminal Cases of Motor Vehicle Theft in Surabaya, Indonesia

Introduction: Motor vehicle theft poses significant challenges in urban areas, particularly in large metropolitan cities like Surabaya, Indonesia's second-largest city. Surabaya's strategic economic role makes it a hotspot for criminal activities, including motor vehicle theft, driven by various socio-economic factors. Methods: This study utilizes the Space-Time Autoregressive Integrated Moving Average (STARIMA) model to predict motor vehicle theft cases across five sub-regions in Surabaya, covering the period from January 2019 to December 2023. The STARIMA model, which incorporates both temporal and spatial dependencies, offers a more robust framework for crime prediction compared to traditional models like ARIMA. The results show that STARIMA effectively captures the spatio-temporal dynamics of crime, providing valuable insights for law enforcement to develop targeted strategies that enhance public safety. Results: The model's performance was evaluated using the Root Mean Square Error (RMSE), indicating its suitability for accurate and actionable crime forecasting in Surabaya. Based on the RMSE value, the best model obtained is STARIMA (1,1,2) with a Uniform Location weighting matrix. Conclusions: This STARIMA (1,1,2) model, it is used to predict motor vehicle theft incidents in West Surabaya, Central Surabaya, South Surabaya, East Surabaya, and North Surabaya. The forecast value carried out is for a period of five months into the future. Case predictions for the next five months show fluctuations in each region of Surabaya, with the highest regions in succession being North Surabaya, East Surabaya, and South Surabaya