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- Different types of crimes in different areas:
- Group the data by street and analyze the number of individual crime types on each street.
- Visualize the results using bar charts or other suitable graphs.
- Most common types of crime, time of day, and day on certain streets:
- Filter the data for each street and analyze the most common crime types, days, and hours.
- Use bar charts, pie charts, or heat maps for visualization.
- The number of certain crimes in certain places:
- Conduct a temporal analysis to identify trends in specific crime types over time.
- Use line charts or other time series.
- Over time, the number of common crimes increases:
- Analyze the general trend of common crimes across the dataset.
- Consider creating a time series chart to visualize changes.
- General residential areas with crime:
- Group the data by neighborhoods to identify areas with higher crime rates.
- Visualize the results with maps or bar graphs.
- Time analysis:
- Analyze data based on temporal factors such as month, day of the week, and time.
- Identify patterns and trends over time with appropriate visualizations.
- Map Display:
- Use the latitude and longitude data to draw the map.
- Color-code or size-code data points based on the frequency of crime in each location.
- Correlation analysis:
- Use statistical methods to identify relationships between different variables (eg, time of day, day, month) and types of crime.
- Visualize correlations using correlation matrices or scatter plots.
- Analysis of shooting data:
- Analyze shot data separately, and identify patterns and correlations with other variables.
- Visualize image events on a map and examine temporal patterns.
- Forecasting models:
- Depending on the nature of the data set, you can build predictive models to predict future crimes or classify incidents into different categories.
- Common algorithms are decision trees, random forests or neural networks.
