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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): Crime Prediction, Machine Learning, Web Scraping, Artificial Intelligence, Crime Analysis.

Abstract: This paper presents an approach to municipal crime analysis and prediction through the integration of web scraping techniques and artificial intelligence. Focusing on Alvorada, Brazil, we address the challenge of limited crime data availability in small cities by developing an automated system that extracts and processes crime-related information from local news sources. Our methodology employs the Anthropic Claude AI API for structured data extraction and implements a machine learning model (Random Forest) for crime prediction. The research demonstrates the feasibility of creating crime prediction systems for small cities while identifying temporal and spatial patterns in criminal activity. Additionally, we provide a framework for future improvements through potential law enforcement partnerships and dataset expansion. This study contributes to the growing field of smart city development by offering a replicable methodology for municipalities lacking standardized crime data collecti on systems. (More)

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Paper citation in several formats:
Ortiz, P. A. P. S. and Freitas, L. O. (2025). Mapping and Predicting Crimes in Small Cities Using Web Scraping and Machine Learning. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8; ISSN 2184-4992, SciTePress, pages 888-894. DOI: 10.5220/0013421400003929

@conference{iceis25,
author={Pedro Arthur P. S. Ortiz and Leandro O. Freitas},
title={Mapping and Predicting Crimes in Small Cities Using Web Scraping and Machine Learning},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={888-894},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013421400003929},
isbn={978-989-758-749-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Mapping and Predicting Crimes in Small Cities Using Web Scraping and Machine Learning
SN - 978-989-758-749-8
IS - 2184-4992
AU - Ortiz, P.
AU - Freitas, L.
PY - 2025
SP - 888
EP - 894
DO - 10.5220/0013421400003929
PB - SciTePress

- Science and Technology Publications, Lda.
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