DRAFT Bay Area UrbanSim (BAUS) Implementation
This is the DRAFT UrbanSim implementation for the Bay Area. Policy documentation for the Bay Area model is available here and documentation for the UrbanSim framework is available here.
- Install Python for your OS (Anaconda highly suggested)
- Clone this repository
- Install dependencies using
pip install -r requirements.txt(may need to uncomment out pandana) - Get data using
python baus.py -c --mode fetch_data(you will need an appropriately configured AWS credentials file which you must get from your MTC contact) - Preprocess data using
python baus.py -c --mode preprocessing - Run a simulation using
python baus.py -c(default mode is simulation)
An overview of baus.py
baus.py is a command line interface (cli) used to run Bay Area UrbanSim in various modes. These modes currently include:
- estimation, which runs a series of models to save parameter estimates for all statistical models
- simulation, which runs all models to create a simulated regional growth forecast
- fetch_data, which downloads large data files from Amazon S3 as inputs for BAUS
- preprocessing, which performas long-running data cleaning steps and writes newly cleaned data back to the binary h5 file for use in the other steps
- baseyearsim which runs a "base year simulation" which summarizes the data before the simulation runs (during simulation, summaries are written after each year, so the first year's summaries are after the base year is finished - a base year simulation writes the summaries before any models have run)
Urban Analytics Lab (UAL) Improvements
Data schemas
- Builds out the representation of individual housing units to include a semi-persistent tenure status, which is assigned based on characteristics of initial unit occupants
- Joins additional race/ethnicity PUMS variables to synthetic households [NB: currently missing from the reconciled model, but will be re-added]
- Adds a representation of market rents alongside market sale prices
Model steps
- Residential hedonics predict market rents and sale prices separately, with rents estimated from Craigslist listings
- Household move-out choice is conditional on tenure status
- Household location choice is modeled separately for renters and owners, and includes race/ethnicity measures as explanatory variables
- Developer models are updated to produce both rental and ownership housing stock
Notebooks, work history, code samples, etc are kept in a separate bayarea_urbansim_work repository.
Current status (August 2016)
- All of the UAL alterations have been refactored as modular orca steps
- This code is contained in
baus/ual.py,configs/ual_settings.yamland individualyamlfiles as needed for regression models that have been re-estimated - There are no changes to
urbansim,urbansim_defaults, or MTC's orca initialization and model steps - MTC and UAL model steps can be mixed and matched by passing different lists to orca; see
run.pyfor examples - The UAL model steps document and test for required data characteristics, using the orca_test library
Outputs from Simulation (written to the runs directory)
ALL OUTPUT IN THIS DIRECTORY IS NOT OFFICIAL OUTPUT. PLEASE CONTACT MTC FOR OFFICIAL OUTPUTS OF THE LAST PLAN BAY AREA.
[num] = a positive integer used to identify each successive run. This number usually starts at 1 and increments each time baus.py is called.
Many files are output to the runs/ directory. They are described below.
| filename | description |
|---|---|
| run[num]_topsheet_[year].csv | An overall summary of various housing and employment outcomes summarized by very coarse geographies. |
| run[num]_parcel_output.csv | A csv of all new built space in the region. This has a few thousand rows and dozens of columns which contain various inputs and outputs, as well as debugging information which helps explain why each development was picked by UrbanSim. |
| run[num]_parceldata\[year].csv | A CSV with parcel level output for all parcels with lat, lng and includes change in total_residential_units and change in total_job_spaces, as well as zoned capacity measures. |
| run[num]_buildingdata\[year].csv | The same as above but for buildings. |
| run[num]_taz_summarie\s_[year].csv | A CSV for input to the MTC travel model |
| run[num]_pdasummaries\[year].csv, run[num]_jurissummaries\[year].csv, run[num]_superdistrictsummaries\[year].csv | Similar outputs to the taz summaries but for each of these geographies. Used for understanding the UrbanSim forecast at an aggregate level. |
| run[runnum]_dropped_buildings.csv | A summary of buildings which were redeveloped during the simulated forecast. |
| run[runnum]_simulation_output.json | Used by the web output viewer. |
Directory structure
- baus/ contains all the Python code which runs the BAUS model.
- data/ contains BAUS inputs which are small enough to store and render in GitHub (large files are stored on Amazon S3) - this also contains lots of scenario inputs in the form of csv files. See the readme in the data directory for detailed docs on each file.
- configs/ contains the model configuration files used by UrbanSim. This also contains settings.yaml which provides simulation inputs and settings in a non-tabular form.
- scripts/ these are one-off scripts which are used to perform various input munging and output analysis tasks. See the docs in that directory for more information.