Crime Prediction & Monitoring Framework Based on Spatial Analysis

A Novel Method of Spatiotemporal Dynamic Geo-Visualization of Criminal Data, Applied to Command and Control Centers for Public Safety

“…Some studies can serve this purpose, since their approach include one or several characteristics. In this regard, these studies include: Investigations with methodology that include georeferencing and spatial and statistical analysis to find critical points such as [5,[24][25][26][27][28] or; visualizations with predictive usefulness, such as [29][30][31][32][33]; simulations of criminal behavior aimed at analyzing collateral effects and resources allocations [34][35][36]; systems that use algorithms for trajectory analysis to identify crimes from different sources of information [37,38]; mechanisms for calculating severity of incidents determined by distance, time and type of transition, plotted on heatmaps with dynamic transition, to extract information on street crimes [39,40]; systems for merging, associating and clustering data attributes, as well as for associating with other nodes to extract patterns from evolving criminal networks, based on observation and analysis of temporary data [41][42][43]; and finally, visualizations that use comparisons to find similarities in criminal patterns through associative searches with knowledge graphs [44] and multivariate reduction plots for comparative analysis of cases [45].…”

Developing Machine Learning Based Predictive Models for Smart Policing

“…In this work, she focuses on how the temperature changes impact various types of crimes. The analysis shows that an increase in temperature will increase crimes like assaults, burglary, collective violence, domestic violence, and rape [11], [13]. No correlation was found between high temperatures and crimes like robbery, larceny, and motor vehicle theft.…”

“…Crime investigation, for example, illegal practices in the particular dimension and spatial-temporal models [11], [12], [13], [14] have been widely contemplated in recent years. Conventional crime expectation techniques incorporate grid mapping, covering ellipses, and kernel density estimation; delivering expectations dependent on the absence of uniform offense circulation.…”

A Review on Resolving Crime with Prediction

“…Then, machine learning algorithms were used to extract information from these broad data sets and to discover the secret relationships between the data used for reporting and finding the criminal trends that are essential to crime analysts to analyze those networks of criminals through interactive visualization for crime prevention. [14] Varvara Ingilevich a , Sergey Ivanov b : The primary objective of the research was to determine whether the number of crimes in a given urban area can be predicted using various statistical tools. In the process of implementing the predictive model, the clustering technique was used to determine the spatial patterns of crime and to detect factors affecting criminal activities.…”