Understanding Public Safety Trends in Calgary through data mining
It provides incremental insights for city managers to improve community safety strategies in Calgary.
This paper analyzed open datasets in Calgary to identify patterns in community crimes, disorders, and traffic incidents, finding that crime rates are strongly linked to population density while pet registration has minimal impact.
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.