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collision-risk-python-analysis

The goal is to create a function that takes an input of collision data points and street centerline data and will output a color-coded interactive map of roads with high risk of collision. This measure will be based on collisions per length in meters for each road.

Overall Project workflow:

  • Read in road and collision data
  • Set an extent in long/lat and then convert back into a regional CRS.
  • Crop all datasets to correct extent
  • Conduct a spatial join based on distance
  • Find outliers, make boxplots, and other descriptive stats
  • Merge spatial join dataset back to original roads dataset
  • Divide counts by length column
  • Plot based on quantiles, or any other specified break method
  • Plot on interactive map using leafmap
  • Make a function that does all of these steps automatically so that this workflow can work for other parts of San Francisco, and other datasets too, if possible.

Example Output:

San Francisco Downtown: Screenshot 2026-03-09 at 12 40 08 AM

Close Up: Screenshot 2026-03-09 at 12 40 19 AM

Residential Area in SF:

Screenshot 2026-03-09 at 12 41 11 AM

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The goal is to create a function that takes an input of collision data points and street centerline data and will output a color-coded interactive map of roads with high risk of collision. This measure will be based on collisions per length in meters for each road.

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