Understanding Public Safety Trends in Calgary through data mining

• URL: http://arxiv.org/abs/2407.21163v1
• Date: Tue, 30 Jul 2024 20:04:51 GMT
• Title: Understanding Public Safety Trends in Calgary through data mining
• Authors: Zack Dewis, Apratim Sen, Jeffrey Wong, Yujia Zhang,
• Abstract summary: This paper utilizes statistical data from various open datasets in Calgary to uncover patterns and insights for community crimes, disorders, and traffic incidents. The findings suggest that crime rates are closely linked to factors such as population density, while pet registration has a smaller impact.
• Score: 8.419688203654946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper utilizes statistical data from various open datasets in Calgary to to uncover patterns and insights for community crimes, disorders, and traffic incidents. Community attributes like demographics, housing, and pet registration were collected and analyzed through geospatial visualization and correlation analysis. Strongly correlated features were identified using the chi-square test, and predictive models were built using association rule mining and machine learning algorithms. The findings suggest that crime rates are closely linked to factors such as population density, while pet registration has a smaller impact. This study offers valuable insights for city managers to enhance community safety strategies.

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