Spatiotemporal Crime Analysis for Risk Management Using the Non-Stationary Moving Average Method

This paper presents a novel approach to spatiotemporal crime analysis, tailored for risk management and safety applications, by introducing the Non-Stationary Moving Average (NSMA) method. The NSMA method extends the standard Moving Average technique by incorporating non-stationarity, addressing the dynamic nature of crime patterns. By combining temporal smoothing with spatial clustering through the K-means algorithm, this approach enables the identification of distinct crime clusters and provides insights into temporal trends. The proposed methodology is formulated as a multicriteria optimization problem, balancing the objectives of spatial clustering and temporal regularization through a regularization parameter. The problem is solved using a subspace algorithm, similar to the approach used in K-means, which alternates between optimizing cluster centers and cluster moving averages. Applied to real-world crime data from the Czech Republic, this method demonstrates its potential to improve resource allocation and decision-making in crime prevention. The NSMA method contributes to advancing the fields of spatiotemporal analysis and risk evaluation, offering a versatile tool for addressing complex urban safety challenges.

Keywords: Moving average, Modeling, Clustering, Crime analysis, Regression.