Geo-Spatial Crime Analysis Using Newsfeed Data in Indian Context

Geo-Spatial Crime Analysis Using Newsfeed Data in Indian Context

Prathap Rudra Boppuru (CHRIST (Deemed To be University), Bengaluru, India) and Ramesha K. (Dr. Ambedkar Institute of Technology, Bangalore, India)
DOI: 10.4018/IJWLTT.2019100103

Abstract

Social media is the platforms where users communicate, interact, share ideas, career interest, pictures, video, etc. Social media gives an opportunity to analyze the human behavior. Crime analysis using data from social media such as Newsfeeds, Facebook, Twitter, etc., is becoming one of the emerging areas of research for law enforcement organizations across the world. The intelligence gathered through data is used for identifying future attacks and plan for reinforcements. This article focuses on the implementation of textual data analytics by collecting the data from different newsfeeds and provides an optimized visualization. This article establishes a framework for better prediction of 16 types of crime in India and the Bangalore area by providing the coordinates of the crime area, along with the crime which might happen there.
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2. Literature Review

Historical crime data can be used to identify high crime areas and plan resources optimally. Predictive policing using data enables law enforcement authorities to take proactive decisions to improve response time to crime incidents (Angers, Biswas & Maiti, 2016). Knowledge acquired from the data mining techniques can be used to helping find criminals faster thereby reducing crime rate. crime prediction, a subtask of crime analysis, considers all the past crime records, classifies the crime categories and predicts the future crime. Crime prediction using pattern and association rule mining determines the chances of performing crime by the same criminal.

Research by urban activist Jane Jacobs (2012) emphasizes that natural surveillance, i.e., the presence of high density of visitors and high diversity enhance the safety of the target area and in effect reduces crime. With the help of data-driven and place-centric approach, it is possible to determine whether a particular geographic area can be identified as a future crime area proposed by X. Wang et al. (2012). Crime models based on spatial analysis created from newsfeed proposed by Jayaweera et al. (2015) can help us in understanding criminal activity better. Also, this anonymized data has the advantage of limited to no privacy risks. Combining anonymized and aggregated user data with demographics can help in identifying whether specific locations are more prone to future crimes or not.

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