Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context

Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context

Prathap Rudra Boppuru (Christ University (Deemed), India) and Ramesha K. (Dr. Ambedkar Institute of Technology, India)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IJDCF.2020100101

Abstract

In developing countries like India, crime plays a detrimental role in economic growth and prosperity. With the increase in delinquencies, law enforcement needs to deploy limited resources optimally to protect citizens. Data mining and predictive analytics provide the best options for the same. This paper examines the news feed data collected from various sources regarding crime in India and Bangalore city. The crimes are then classified on the geographic density and the crime patterns such as time of day to identify and visualize the distribution of national and regional crime such as theft, murder, alcoholism, assault, etc. In total, 68 types of crime-related dictionary keywords are classified into six classes based on the news feed data collected for one year. Kernel density estimation method is used to identify the hotspots of crime. With the help of the ARIMA model, time series prediction is performed on the data. The diversity of crime patterns is visualized in a customizable way with the help of a data mining platform.
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2. Literature Review

Criminology has two research areas – one that seeks to understand the development of criminal offenders and the other that aims to understand the evolution of crime events (Fayyad, 2012). Rational choice theory of criminology explains that perpetrators select targets and identify the means to achieve their goals based on rational decisions. Routine activity theory explains that the offender must be present along with other crime favorable circumstances at the same time for the occurrence of the crime. Crime pattern theory combines rational choice and routine activity theory and emphasizes the importance of place in crime events (Green, 2002). Significantly dense population growth increases opportunities for crime. The complexities of large-scale, urban, residential development and the challenges of embedding crime prevention during this period of rapid and sustained population growth are studied in Australia (Clancey, Kent, Lyons & Westcott, 2017).

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