SB-79 Analysis

A geospatial analysis tool to visualize California Senate Bill 79 (SB-79) housing density impacts on parcels near high-quality transit stops. This project creates interactive maps showing which parcels would be upzoned under SB-79 and calculates potential housing capacity.

✨Live Demo✨

What is SB-79?

SB-79 is California housing legislation that allows increased residential density near high-quality transit stops.

For more details take a look at the legislation: https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202520260SB79 or the pdf in this repo

Features

• Automated Data Collection: Can pull real-time data from California State APIs (city boundaries, transit stops, parcels, zoning)
• SB-79 Tier Classification: Categorizes parcels by distance from transit stops (200ft, quarter-mile, half-mile zones)
• Capacity Calculations: Computes potential and net increase housing capacity per SB-79 regulations
• Local Caching: GeoPackage storage for faster repeat runs without API calls
• Static Deployment: Frontend deployable to any static hosting (Cloudflare Pages, Netlify, etc.)
• Currently Supports: Berkeley, CA

Useful links

• https://berkeley.maps.arcgis.com/apps/webappviewer/index.html?id=2c7dfafbb1f64e159f4fdf28a52f51c6&showLayers=Berkeley%20Parcels;Base%20Data;Planning%20and%20Building
  • Web viewer for the various berkeley apis that exist

Project Structure

sb79-analysis/
├── backend/                    # Python data processing
│   ├── berkeley.py            # Main script to fetch and process data
│   ├── config.py              # Configuration and API endpoints
│   ├── data_store.py          # Local data storage utilities (GeoPackage)
│   └── data/                  # Cached data (generated)
│       ├── berkeley_data.gpkg              # GeoPackage with all layers
│       └── berkeley_data.metadata.json     # Data source metadata
├── public/                    # Frontend (static site - deployment ready)
│   ├── index.html            # Main map interface
│   ├── style.css             # Styles
│   ├── map.js               # MapLibre GL JS implementation
│   └── data/                # Generated GeoJSON files (for map)
│       ├── city_boundary.geojson
│       ├── transit_stops.geojson
│       ├── parcels.geojson
│       └── map_metadata.json
├── 20250SB79_84.pdf          # SB-79 legislation text
├── pyproject.toml            # Python dependencies (uv)
└── README.md

How to Use

Installation

1. Ensure you have Python 3.14+ installed and uv
2. Use uv sync to install dependencies:

uv sync

Running the Analysis

First run (fetch data from APIs):

cd backend
# In config.py, set: USE_LOCAL_DATA = False
uv run berkeley.py

This will:

1. Fetch city boundary from California State Geoportal
2. Fetch high-quality transit stops within Berkeley
3. Fetch zoning districts from Berkeley's GIS
4. Fetch all parcels within 0.5 miles of transit stops (in three zones: 200ft, quarter-mile, half-mile)
5. Add zoning information to each parcel using spatial join
6. Filter for residential, commercial, and mixed-use parcels only (ZONECLASS: R-, C-, ES-R)
7. Filter out parcels with zero lot size
8. Calculate potential capacity based on SB-79 density limits and lot size

   density_value = {
      DENSITY_200FT = 160 du/acre
      DENSITY_QUARTER_MILE = 140 du/acre
      DENSITY_HALF_MILE = 120 du/acre
   }
   total_net_capacity = 0
   for parcel in all parcels:
      parcel_capacity = max(parcel_area * density_value - existing_capacity)
      total_net_capacity += parcel_capacity

   • To see the actual formula see add_potential_and_net_capacity() in berkeley.py
9. Remove duplicate parcels sharing the same centroid (keeps only parcels with BLDSQFTTAXABLE = 0)
10. Save all data to backend/berkeley_data.gpkg for future use
11. Export GeoJSON files to public/data/ for the map

Subsequent runs (use cached data):

cd backend
# In config.py, set: USE_LOCAL_DATA = True
uv run berkeley.py

This loads data from the local GeoPackage, which is much faster.

Viewing Results

The script will output capacity calculations:

Example:

✓ Capacity Summary by Tier Zone w/ net increase calculations:
  - 200ft zone: 121 existing / 3155 potential (22 parcels)
  - Quarter mile zone: 6012 existing / 24595 potential (1604 parcels)
  - Half mile zone: 14672 existing / 55678 potential (4762 parcels)
  - Total: 20805 existing / 83430 potential units
  - Net new capacity: 83430 units

Viewing the Interactive Map

Local Development:

cd public
python -m http.server 8000
# Open http://localhost:8000 in your browser

Configuration

Edit config.py to customize:

• API endpoints (change city APIs for other locations)
• SB-79 density limits (DENSITY_200FT, DENSITY_QUARTER_MILE, DENSITY_HALF_MILE)
• Data caching behavior (USE_LOCAL_DATA)
• Map display settings

TODOs

Features

• Add zones that can be deferred to the next RHNA cycle
  • Very High Fire Hazard Severity Zones
  • Areas vulnerable to 1 foot of sea level rise
  • Sites with a locally designated historical resource (designated as of 1/1/2025)
  • Sites and station areas meeting minimum local zoning standards:
    • A site allowing at least 50% of the density & FAR allowed by SB 79
    • A station area where at least 33% of sites allow at least half the density/FAR allowed by SB 79, and where the station area cumulatively allows for at least 75% of the aggregate density as SB 79
    • A station area that is primarily “low resource” on the TCAC Opportunity Maps, and that cumulatively allows at least 40% of the aggregate density as SB 79
    • Any site within a low resource area if the city cumulatively allows at least 50% of the aggregate transit-oriented density as SB 79

General Code Cleanup

• Confirm the Net Capacity Calculation with someone
• Look into making API request batching a generic so we don't repeat functionality get_zoning_districts and get_parcels_near_transit_stops
• Look into moving data saving out of this function so we always pull from local data and update with a different function