Crime Hotspot Identification using SVM in Machine Learning

This study is to identify and predict the criminal hotspot in crime data. Crime is one of the society’s most pressing issues and preventing it is critical. This necessitates keeping track of all crimes and maintaining a record of them for future reference based on traditional literature. This work uses the Support Vector Machine algorithm in machine learning to identify crime hotspot. Crime mapping is an important area of research for crime analysis because it allows to visualize, analyze, and track crime or illegal activities. The analyst can use crime mapping to identify high-crime areas, trends, and patterns. This paper summarizes the latest spatiotemporal crime datasets that are available publicly. The primary objective is to present recent advances in crime hotspot prediction and identification.