The GIS based Analysis Criminal Events using Analysing Crimes using Machine Learning technique

EasyChair Preprint 2616

Abstract

Crime is an alarming aspect in our culture which is a crucial challenge to avoid this. the crime has been recorded with spatially distributions spread on the map it will be difficult for the local government and formal agencies to handle because of numerical crime it needs to concentrate on the specific location and reducing the coast and reduce the places of patrolling allocation and saving time and efforts. Police old fashion methods a pin placed on the map in the map on the wall to identify the crime and their detail it has to developed and merging the advanced technique to help the security do their job in practical and efficient way and sufficient to reduce time and effort. Crime evaluation is a methodical way in which patterns and trends in crime are detected and investigated. In this study, using different data mining clustering method to examine Baltimore, Maryland's crime information. Crime statistics are derived from the standardized resources of the U.S. government. It took the form of criminal data about the state of Maryland, such as the city of Baltimore, with 340,924 cases and 16 attributes to reflect the cases from 2012-2018. Density-Based Spatial Clustering with Noise (DBSCAN) algorithms are utilized to cluster crimes incidents focused on certain predefined events and the outcome of these clusters employed to find an appropriate cluster for the identification of crimes pattern recognition. The clustering findings are visualized by geographical information system (GIS) that combines to make crime distributions on map of the city and on the real-life to the law enforcement for interactive and easy understanding. This work to support and help law enforcement authorities to reliably predict and identify crime pattern recognition in Baltimore.

Keyphrases: Clustering, Crime, DBSCAN algorithm, GIS, Geographical, Geographical Information System, geospatial, hotspot, pattern, spatial-temporal

@booklet{EasyChair:2616,
  author    = {Abbas F. Mohammed and Wadhah R. Baiee},
  title     = {The GIS based Analysis Criminal Events using Analysing Crimes using Machine Learning technique},
  howpublished = {EasyChair Preprint 2616},
  year      = {EasyChair, 2020}}