- tps_covariates.py
- Import 'student_geos_.csv' (base) files (provided by TPS)
- Aggregate files together, reshape (long by tract, wide by year)
- Export 'tps_student_counts_by_tract.csv'
- tulsa_parcels.py
- Import 'TulsaParcelsNeighborhood.csv' (base) file (provided by CoT)
- Keep desired fields
- Export 'tulsa_parcels.csv'
- tulsa_parcel_tps_tract_association.Rmd
- Import 'tulsa_parcels.csv'
- Associate parcel geocodes to geotracts (same as that in 'tps_student_counts_by_tract.csv'
- Export 'tulsa_parcel_tps_tract_association.csv'
- base_analytical_file.py
- Import 'tps_student_counts_by_tract.csv' (Student Count Data)
- Import 'tulsa_parcel_tps_tract_association.csv' (Parcel Data)
- Filter, normalize, reshape, split (by parcel types)
- Merge together parcel and student count data
- Export 'base_file_‹wide/long›_‹residential/commercial/combo›.csv' (analytical) files
- feature_selection_methods.py
- Define feature selection methods
- udp_feature_selection.py
- Iterate over each group type (residential, commercial, combo) and year (2013-2018) combination:
- Apply each feature selection method to the target year, using parcel data from prior years
- Combine year-to-year results for each group type
- Export 'analysis_‹residential/commercial/combo›.csv' (analytical result) files