Implementasi Statistika Spatial Autoregressive Moving Average dalam Pelacakan Hubungan Spasial Antara Faktor Sosial Budaya dan Kejadian Kriminalitas - wahyu, reski; Ekawati, Darma (Journal of Mathematics: Theory and Applications, 2025)
High crime rates can have an impact on social stability, quality of life, and economic development in a region. Therefore, it is important to know the socio-cultural factors that influence crime rates in order to provide new insights to support data-based decision making. The relationship between these factors and crime is often non-linear and can be influenced by spatial effects, so a method is needed that considers spatial effects such as the Spatial Autoregressive Moving Average (SARMA). SARMA is not only able to capture spatial autocorrelation patterns, but can also identify the influence of interdependence between regions over a certain period of time. Furthermore, the Lagrange multiplier test is used to see the presence of spatial autocorrelation specifically. From the results of the analysis carried out, there are 4 significant factors, namely population density, unemployment rate, number of places of worship, and average length of schooling. Furthermore, the results of the spatial autocorrelation analysis using LISA show that there are three districts/cities identified as having spatial autocorrelation with a significance level of 0.05. This means that Bone Regency, Maros Regency, and Pangkajene Kepulauan Regency have a more significant influence on the surrounding areas.