Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

• Published in: ISPRS international journal of geo-information Vol. 12; no. 7; p. 291
• Main Authors: Salcedo-Gonzalez, Mayra, Suarez-Paez, Julio, Esteve, Manuel, Palau, Carlos Enrique
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Artificial neural networks; Cities; Codes; Colombia; Control systems; Crime; Crime prevention; criminal activity forecast; Criminal investigations; Decision making; Deep learning; Disasters; displacement pattern detection; Enforcement; Geographic information systems; Geographical information systems; Geography; Information sources; Information systems; Jurisdiction; Law enforcement; Laws, regulations and rules; Long short-term memory; Machine learning; Multilayer perceptrons; multivariate time series; Natural disasters; Neural networks; New York; Open source software; Police; predictive geo-visualization of activity; Prevention; Programming languages; Public software; Real time; Remote sensing; situational awareness; sparse data; System effectiveness; Terrorism; Time series; Visualization; New York; Colombia
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi12070291

This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities.