Treffer: Comparative analysis of crime predictions using machine learning algorithms with geospatial features.

Crime constitutes an action which is punishable by law. Crime Analysis involves the predictions of occurrence of future crime, the time and place of crime and to have insights into the trends of crime. Models are created by machine learning algorithms using the spatial, temporal and the demographic features extracted from the crime dataset. Reverse Geocoding technique is used to extract spatial features and also visualize the locations of the crime from the crime dataset using ArcGIS API of python along with WebMap and WebScene component provided by the API. Crime Analysis assists the Police, Investigation departments for the prediction of future crime and also take required actions which involves the deployment of crops in the predicted place at the time of the crime. The hotspots are identified; the hotspot is the area co-ordinates where more frequent crimes occur. After identifying hotspots, more focus is given on those crime prone areas for preventing and controlling the crime. [ABSTRACT FROM AUTHOR]

Copyright of AIP Conference Proceedings is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)