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Spatio-temporal Crime Analysis and Forecasting on Twitter Data Using Machine Learning Algorithms.

Meghashyam Vivek1, Boppuru Rudra Prathap1

  • 1Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India.

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Summary
This summary is machine-generated.

This study analyzes Indian crime tweets using machine learning to map and predict crime trends. It identifies the most accurate forecasting model for understanding crime patterns through social media data.

Keywords:
Crime analysisCrime preventionForecastingMachine learning algorithmsTwitter data

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Area of Science:

  • Social Computing
  • Data Science
  • Computational Criminology

Background:

  • Social media platforms have evolved into significant global communication tools.
  • User-generated content on social media influences journalism and public discourse.
  • Analyzing social media data offers novel insights into societal issues like crime.

Purpose of the Study:

  • To classify, visualize, and forecast Indian crime data sourced from Twitter.
  • To provide a spatio-temporal understanding of crime in India using advanced models.
  • To evaluate the efficacy of machine learning models in crime analysis.

Main Methods:

  • Scraping Indian crime-related tweets using the Tweepy module and '#crime' hashtag.
  • Classifying tweets based on 318 crime-specific keywords.
  • Utilizing Bokeh and gmaps for data visualization and geospatial mapping.
  • Comparing Long Short-Term Memory (LSTM), ARIMA, and SARIMA models for time series forecasting.

Main Results:

  • Successful classification and geospatial visualization of crime-related tweets across India.
  • Comparative analysis of LSTM, ARIMA, and SARIMA models for crime tweet forecasting.
  • Identification of the most accurate model for predicting crime tweet volume over time.

Conclusions:

  • Social media data, specifically Twitter, can be effectively utilized for crime analysis and monitoring in India.
  • Machine learning models offer valuable tools for understanding the spatio-temporal dynamics of crime.
  • This research provides a framework for leveraging big data for public safety and policy-making.