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Data Team Contributing Guide
The dataset that our project is built around is the City of Los Angeles parking citation open dataset. They have an API that you can fetch data from.
The fastest way to get started analyzing the Los Angeles parking citation dataset is to open the citation analysis branch's notebook folder in Google Colab. To load data from Lucky Parking's Google Drive, reach out to a Lucky Parking project lead and have them add your email to the Lucky Parking Google Drive.
We also have code in our repository dedicated to helping volunteers analyze the dataset, found in the citation-analysis branch. Check out the README for the citation-analysis branch on how to use it.
When downloading raw citation data by "make data", an error raised: "Failed to get options via gdal-config: [Errno 2] No such file or directory: 'gdal-config' A GDAL API version must be specified. Provide a path to gdal-config using a GDAL_CONFIG environment variable or use a GDAL_VERSION environment variable."
So it seems that the gdal-config is not in one of the usual places on the PATH, so Fiona was unable to find it.
To solve it: (1) remove gdal with "conda remove gdal" (Anaconda needs to be installed first); (2) do a fresh "conda install geopandas" (3) run "conda activate lucky-parking-analysis" and "make data" again (4) If an error "No module named pip make: *** [requirements] Error 1" is raised: run "python3 -m ensurepip" then run "make data"
Marie
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Research and Discovery Overview