The datasets were arranged by merging three different Land use tables, Population per zip code, Vision Zero Priority Intersections, and Collision data. For land-use, the main focus was to review the demographic data based on the crashes' location. The analytical tool to organize and simplify the data will be Python (package: Pandas), to understand the need for vision zero to include street geometry/ location as a collision factor. Using Python Data cleaning with Pandas and NumPy using the following functions:
- Dropping columns
- Changing the index of the data frame
- Arranging and organizing fields in the data
- Combining strings with NumPy clean columns
- Renaming columns and skipping rows with empty cells
- Calculating the number of collisions per zip code.
- Graphing and plotting
Python-Pandas was used to identify the locations with the highest number of collisions.