Computer-aided analysis: Correlation between urban green spaces and crime rates

Yixi Kong

doi:10.54254/2755-2721/93/20240971

Research Article

Open access

Published on 8 November 2024

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Kong,Y. (2024). Computer-aided analysis: Correlation between urban green spaces and crime rates. Applied and Computational Engineering,93,179-184.

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Computer-aided analysis: Correlation between urban green spaces and crime rates

Yixi Kong *^,1,

¹ Politecnico di Milano, Milan, Italy

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/93/20240971

Abstract

Urban green spaces have been associated with various social and environmental benefits, including their potential impact on crime rates. This study investigates the correlation between urban green spaces and crime rates using a combination of Geographic Information Systems (GIS) and advanced statistical analysis. By analyzing data from a large metropolitan area, we examine how green space characteristics such as size, type, and maintenance influence crime patterns. Our findings indicate that well-maintained green spaces with diverse amenities can significantly reduce crime rates, whereas neglected areas may become hotspots for criminal activities. The study also explores temporal patterns of crime in relation to green spaces, revealing that adequate lighting and community presence are crucial for nighttime safety. Policy recommendations are provided to help urban planners and policymakers leverage green spaces to enhance urban safety. By integrating multiple analytical techniques, this research contributes to a comprehensive understanding of the role of green spaces in urban crime prevention and offers actionable insights for future urban planning.

Keywords

Urban Green Spaces, Crime Rates, GIS, Data Analysis, Urban Planning.

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References

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Cite this article

Kong,Y. (2024). Computer-aided analysis: Correlation between urban green spaces and crime rates. Applied and Computational Engineering,93,179-184.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/

ISBN：978-1-83558-627-3(Print) / 978-1-83558-628-0(Online)

Conference date: 21 November 2024

Editor：Mustafa ISTANBULLU, Xinqing Xiao

Series: Applied and Computational Engineering

Volume number: Vol.93

ISSN：2755-2721(Print) / 2755-273X(Online)

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