Understanding Public Safety Trends in Calgary through data mining

TL;DR

This study analyzes Calgary's open datasets to identify key factors influencing public safety, using statistical and machine learning methods to inform community safety strategies.

Contribution

It introduces a comprehensive data mining approach combining geospatial visualization, correlation analysis, and predictive modeling to understand safety trends.

Findings

Crime rates are strongly linked to population density.

Pet registration has a minimal impact on safety.

Predictive models accurately identify safety risk factors.

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.