Analysis of Criminal Spatial Events in GIS for predicting hotspots

Abstract

The crime rate increasing in developing countries cause of the unequal distribution of psychological, economic situation. This research aims to identify the crime mapping and investigate the hotspots and analyzing the spatial crime dataset and the predict of Spatio-temporal hotspot in Baltimore city for a period from 2012 to 2018. Analyzing crime data using data mining algorithms and The Geographic Information System (GIS) of Geographic dataset visualize and it possible for law enforcement to detect spatial crime patterns map easy and flexible and different analysis to identify the crime hotspot region efficiently. analysis crime hotspot using GIS is a useful way to the recognition for crime pattern and predicting hotspot over spatial correlation, analysis spatial data and revile crime pattern future detection. using spatial correlation, the G∗ statistic has been done with hotspot analysis the Getis-Ord Gi∗ to find the result of the spatial statistics pattern. analysis the crime to predict hotspot uses spatial variation and density crimes for clarifying the positions of statistically significant crime predict hotspots and cold spots and GIS interpolation method is used for more efficient visualization. This research using Grid network hotspots are applied to the crime data of Baltimore, Maryland state to recognize the hotspots for crime data like Shooting, Homicide and Assault by threat.