Authors:
Pedro Arthur P. S. Ortiz
and
Leandro O. Freitas
Affiliation:
Polytechnic School, Federal University of Santa Maria, Av Roraima 1000, Santa Maria - RS, Brazil
Keyword(s):
Web Scraping, Urban Crime Mapping, Data Extraction, AI-Powered Text Analytics, Crime Analysis.
Abstract:
This paper presents an innovative approach to urban crime mapping through automated web scraping and data analysis techniques, addressing the challenge of limited crime data availability in smaller municipalities. Focusing on Santa Maria, Brazil, we develop a methodology to extract, process, and visualize crime-related information from local news sources. Our approach combines web scraping using Selenium, natural language processing with the Claude API, and data visualization techniques to create a comprehensive crime dataset. Through implementation, we present heat maps of crime hotspots, temporal analysis of crime patterns, and statistical correlations between crime-related factors. The research examines hourly, daily, and seasonal crime patterns, providing insights for law enforcement resource allocation. We discuss challenges and ethical considerations of using web-scraped data, including privacy concerns, reporting bias, and verification challenges. While acknowledging limitatio
ns such as data bias and accuracy concerns, this research provides a foundation for data-driven urban crime prevention strategies. The methodology offers a scalable framework that could be implemented across various urban environments, contributing to more effective crime prevention and public safety strategies.
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