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首页> 外文期刊>International journal of digital crime and forensics >Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context
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Spatio-Temporal Crime Analysis Using KDE and ARIMA Models in the Indian Context

机译：在印度语境中使用KDE和Arima模型的时空犯罪分析

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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.
机译：在印度等发展中国家，犯罪在经济增长和繁荣中发挥了不利作用。随着拖欠拖欠的增加，执法部门需要最佳地部署资源以保护公民。数据挖掘和预测分析提供了最佳选择。本文介绍了在印度和班加罗尔市犯罪的各种来源收集的新闻饲料数据。然后将罪行分类为地理密度和犯罪模式，例如日期的时间，以确定和可视化国家和区域犯罪的分布，例如盗窃，谋杀，酗酒，攻击等。总共有68种犯罪类型字典关键字根据收集一年的新闻源数据分为六个类。内核密度估计方法用于识别犯罪的热点。在ARIMA模型的帮助下，对数据执行时间序列预测。犯罪模式的多样性在数据挖掘平台的帮助下以可定制的方式可视化。

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