The Challenge
Analyzing crime data in Montreal to identify patterns and trends that can help law enforcement agencies and city planners make informed decisions to improve public safety.
The Approach
1. Data Collection: Gathered crime data from public databases and police reports.
2. Data Cleaning: Cleaned and preprocessed the data to ensure accuracy and consistency.
3. Data Analysis: Used Python and SQL to analyze the data and identify patterns and trends.
4. Data Visualization: Created interactive dashboards using Tableau to visualize the data and make it accessible to stakeholders.
5. Stakeholder Engagement: Worked with law enforcement and city planners to ensure the dashboards met their needs and provided actionable insights.
The Outcome
The project resulted in an interactive dashboard that provides real-time insights into crime patterns and trends in Montreal. Law enforcement agencies and city planners can now make data-driven decisions to improve public safety.
Users Can:
- Access real-time data on crime patterns and trends in Montreal.
- Visualize crime data through interactive dashboards.
- Identify hotspots and trends to inform law enforcement strategies.
- Make data-driven decisions to improve public safety.
Key Features
- Real-time Data: Provides up-to-date information on crime patterns and trends.
- Interactive Dashboards: Allows users to explore the data through interactive visualizations.
- Hotspot Identification: Identifies areas with high crime rates to inform law enforcement strategies.
- Stakeholder Engagement: Ensures the dashboards meet the needs of law enforcement and city planners.
Technologies Used
- Python: For data cleaning and analysis.
- SQL: For querying and managing the data.
- Tableau: For data visualization.