https://github.com/udst/bayarea_urbansim
UrbanSim implementation for the San Francisco Bay Area
https://github.com/udst/bayarea_urbansim
bay-area data-science modeling simulation urbansim
Last synced: 30 days ago
JSON representation
UrbanSim implementation for the San Francisco Bay Area
- Host: GitHub
- URL: https://github.com/udst/bayarea_urbansim
- Owner: UDST
- Created: 2013-09-10T20:10:03.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2024-02-23T01:35:02.000Z (over 1 year ago)
- Last Synced: 2024-04-16T05:28:47.813Z (about 1 year ago)
- Topics: bay-area, data-science, modeling, simulation, urbansim
- Language: Python
- Homepage:
- Size: 64 MB
- Stars: 14
- Watchers: 16
- Forks: 27
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
DRAFT Bay Area UrbanSim (BAUS) Implementation
=======
[](https://travis-ci.org/UDST/bayarea_urbansim)
This is the DRAFT UrbanSim implementation for the Bay Area. Policy documentation for the Bay Area model is available [here](http://data.mtc.ca.gov/bayarea_urbansim/) and documentation for the UrbanSim framework is available [here](https://udst.github.io/urbansim/).
* Install Python for your OS ([Anaconda](https://www.continuum.io/downloads) 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](https://github.com/ual/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.yaml` and individual `yaml` files 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.py` for examples
* The UAL model steps document and test for required data characteristics, using the [orca_test](https://github.com/udst/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]\_parcel_data\_[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]\_building_data\_[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](http://analytics.mtc.ca.gov/foswiki/UrbanSimTwo/OutputToTravelModel)
run[num]\_pda_summaries\_[year].csv, run[num]\_juris_summaries\_[year].csv, run[num]\_superdistrict_summaries\_[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.