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settings.yaml
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settings.yaml
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space_estimation_method: 'lm'
cache_dir: "./cache"
output_dir: "./output"
plots_dir: "./output/plots"
plots: false
# Inputs
inputs:
land_use: "./data/land_use.csv"
geometry: "./data/mgra15/mgra15.shp"
raw_parking_inventory: "./data/mgra_parking_inventory.csv"
# Data input/outputs. These files serve as both inputs and output locations.
outputs:
# reduced_parking_df: "./output/reduced_parking_costs.csv" # Output from reduction and also input to imputation step
imputed_parking_df: "./output/imputed_parking_costs.csv" # Output from imputation and also input to district creation
districts_df: "./output/district_data.csv" # Output from district creation and also input to aggregation
# aggregated_street_data: "./output/aggregated_street_data.csv" # Output from network data aggregation and input to space estimation
estimated_spaces_df: "./output/estimated_spaces.csv" # Output from space estimation
expected_parking_df: './output/expected_parking_data.csv' # Output
combined_df: './output/final_parking_data.csv' # Output
# If you want to rename columns, you can do so here.
# Otherwise it return all columns (includes land_use columns))
output_columns:
expected_parking_df: # The data frame that is output from the model
# mgra: # Index included by default
# column name: optional new column name
exp_hourly:
exp_daily:
exp_monthly:
parking_type:
spaces_for_calculation: parking_spaces
estimated_spaces:
spaces:
# Parameters
walk_dist: 0.5
walk_coef: -0.3
# Which models to run, comment out any that you wish to not run.
# However, be sure that the input is ready for whatever the new first model is.
# For example, if you have parking data cleaned up already, it can be fed into the 'create_districts' model.
# Or if you manually estimated spaces, you can skip that model too.
models:
- run_reduction
- run_imputation
- create_districts
- run_space_estimation
- run_expected_parking_cost
- write_output