This repository contains the analysis of the Toronto Robberies dataset, covering the period from 2014 to June 2022. The analysis aims to identify patterns and trends in robbery incidents across the city using various data analytics techniques.
|
|
|
Comparison of areas targeted by robbers in Toronto.
Points on the Toronto map show the types of locations most likely to be targeted. Green circles on the map represent incidents of Break and Enter, while purple circles indicate incidents of Auto Theft. |
|
The project was developed as part of the EECS 6414 - Data Analytics and Visualization course at York University. The primary tools used for the analysis include Python for data processing and Tableau for data visualization.
Time Series Analysis: Examination of robbery trends over the years to detect any significant changes or correlations with city demographics or economic events. Seasonality Patterns: Identification of seasonal patterns in robbery incidents to aid law enforcement agencies in resource allocation. Spatial Analysis: Visualization of robbery hotspots across Toronto to assist in targeting high-risk areas. Premises Analysis: Analysis of the types of locations most frequently targeted by robbers.
The analysis involved several steps:
Data Cleaning: Handling missing values, removing duplicates, and correcting errors. Exploratory Data Analysis: Initial exploration to understand the distribution and characteristics of the data. Visualization: Creation of histograms, scatter plots, and heat maps to uncover patterns and relationships. Statistical Analysis: Application of statistical methods, including k-means clustering, to identify outliers and ensure robustness. Findings The analysis revealed key insights into the temporal and spatial distribution of robbery incidents in Toronto, helping inform strategies for crime prevention and resource allocation by law enforcement agencies.
Potential extensions of this analysis include:
Robbery-Income Relationship: Correlating crime rates with neighborhood income data. Comparative Analysis: Comparing robbery incidents across different cities in Canada or globally. Community Policing Impact: Analyzing the effectiveness of community policing measures like security cameras on robbery incidents.