Crime Mapping with Automatic Classifier System using Machine Learning and GIS
M. Deepika1, S. Ancy2
1M. Deepika, Assistant Professor, Department of Information Technology, St. Peter’s Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2S. Ancy, Assistant Professor, Department of Information Technology, Jeppiaar Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 07 December 2019 | Revised Manuscript received on 21 December 2019 | Manuscript Published on 31 December 2019 | PP: 195-198 | Volume-8 Issue-12S2 October 2019 | Retrieval Number: L103410812S219/2019©BEIESP | DOI: 10.35940/ijitee.L1034.10812S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crime rate is increasing over the years, and it remains a great challenge for the government to track the crimes convicted. Each area has a pattern of which type of crime is happening and the crime knowledge is inevitable to control from the crime happening. Crime occurs in a sequence leaving hidden patterns. Thus the crime data is to be processed for finding underlying patterns, this project finds the patterns and insights about the crime data. Majorly being an unstructured data, this is been preprocessed and checked for future values. Crimes convicted are collected from a particular area (Indore in our case) and checked for predictions using Multi class Classification Algorithms like Random Forest and the future crime to be convicted in an area is predicted and visualizations are made accordingly. Many classification algorithms like support vector machines, decision trees, and random forest are used to classify and random forest shows better accuracy. Features to be given as input and output are selected by visualizing the data by graphs and plots.
Keywords: Criminal-network Analysis, Geographic Information System, B Plots, Multi-Class Classification Problem.
Scope of the Article: Machine Learning