So, for the final project, I have decided to work on this dataset: https://data.boston.gov/dataset/crime-incident-reports-august-2015-to-date-source-new-system
These are the steps for analysis, I will be following for our analysis:
- Variety of Crimes in Different Areas:
- Group the data by street and analyze the count of unique types of crimes on each street.
- Visualize the results using bar charts or other appropriate plots.
- Most Common Crime Types, Time, and Day on Specific Streets:
- Filter the data for each street and analyze the most common crime types, days, and hours.
- Use bar charts, pie charts, or heatmaps for visualization.
- Rise in Certain Crimes in Specific Areas:
- Perform a temporal analysis to identify trends in specific types of crimes over time.
- Use line charts or other time series visualizations.
- Common Crimes Rising Over Time:
- Analyze the overall trend of common crimes over the entire dataset.
- Consider creating a time series plot to visualize the changes.
- Common Neighborhoods with Crime:
- Group the data by neighborhood to identify areas with higher crime rates.
- Visualize the results using maps or bar charts.
- Time Analysis:
- Analyze the data based on time factors such as month, day of the week, and hour.
- Identify patterns and trends over time using appropriate visualizations.
- Map Chart Visualization:
- Utilize the latitude and longitude information to create a map chart.
- Color-code or size-code data points based on the frequency of crimes in each location.
- Correlation Analysis:
- Use statistical methods to identify correlations between different variables (e.g., time, day, month) and types of crimes.
- Visualize correlations using correlation matrices or scatter plots.
- Shooting Data Analysis:
- Analyze shooting data separately, identifying patterns, and correlations with other variables.
- Visualize shooting incidents on a map and explore temporal patterns.
- Predictive Models:
- Depending on the nature of your dataset, you can build predictive models to forecast future crime incidents or classify incidents into different categories.
- Common algorithms include decision trees, random forests, or neural networks.
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