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https://github.com/hunterowens/usc-price-workshop
workshop for USC price students around build data driven policy briefs
https://github.com/hunterowens/usc-price-workshop
Last synced: about 2 months ago
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workshop for USC price students around build data driven policy briefs
- Host: GitHub
- URL: https://github.com/hunterowens/usc-price-workshop
- Owner: hunterowens
- License: mit
- Created: 2020-10-22T15:00:55.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2020-11-07T21:53:03.000Z (about 4 years ago)
- Last Synced: 2024-10-19T11:28:24.526Z (2 months ago)
- Language: Jupyter Notebook
- Size: 1.2 MB
- Stars: 1
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# usc-price-workshop
workshop for USC price students around build data driven policy briefs.
## simulation exercise
This workshop consists of a simulation exercise where you will develop data-driven policy and planning briefs for a theoretical LA based state lawmaker. You are charged with coming up with and negiotating a bill to solve the housing crisis.
Each group will be in charge of proposing a theoretical state level housing policy change. For example, you could propse something like last sessions [SB 1120](https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201920200SB1120) or [AB 68](https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201920200AB68)
## getting start
To start, fork this repo and name your project.
## datasets
All data is provided using an [Intake Catalog](https://intake.readthedocs.io/en/latest/catalog.html). We additionally provided interfaces to the City of LA [open data](http://data.lacity.org) and [geohub](http://geohub.lacity.org) using the [intake-dcat](https://github.com/cityoflosangeles/intake-dcat) library. The main datasets are
### Core
1) `county_parcels`: All LA County Parcels 2006-2019, including joined GEOID10 for the census tract and last year it existed.
1) `zoning_parsed`: Using the `laplan` utlity pacakge, we parsed the `zoning` file on the geohub so that you can break apart the zone strings (stuff like `R1-CDO-RIV` in consistuient parts using regular expressions.
### Aux
1) `metro_bus_stops`: in case you want to propose a transit orient policy.
1) `metro_rail_lines`: Metro rail lines
1) `metro_rail_stations`: Metro rail stops, points, not polygons
1) `metro_rapid_bus_lines`: Metro Rapid Buses
1) `metrolink_routes`: Metrolink routes
### Census Data
No data driven analysis is really complete without using American Community Survey data. We've cleaned and parsed some ACS data for you, to see the cleaning scripts see `notebooks`.
1) `census_analysis_tables`:
1) `census_cleaned`: census tables clean to outcomes of intrestes. use the analysis table for hte main data, this is a subset.
### Loading data
To load any data, simply run
```python
# NB you can call the dataset whatever you want
census_df = catalog.census_cleaned_read()
```
or
```python
gdf = catalog.metro_rail_lines.read()
```
To load data from one of the city's open data portals, you can take a look at this [demo notebook](https://github.com/CityOfLosAngeles/intake-dcat/blob/master/examples/demo.ipynb).
## submitting your work
To submit your work, commit the updated policy branch and send us the link on github.
## run on binder / running locally
If you have docker / docker-compose installed, you can simiply run
`docker-compose up` from the project root. Otherwise, you can use binder
[](https://mybinder.org/v2/gh/hunterowens/usc-price-workshop/main)