Crime Analyses Using Data Analytics

Document Type

Article

Source of Publication

International Journal of Data Warehousing and Mining

Publication Date

1-1-2022

Abstract

One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.

ISSN

1548-3924

Publisher

IGI Global

Volume

18

Issue

1

First Page

1

Last Page

15

Disciplines

Computer Sciences

Keywords

Crime Analysis, Crime Prediction, Data Analytics, Dimensionality Reduction, Machine Learning, Resource Management

Indexed in Scopus

no

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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