Tampa Development Coordination Locations - Datasette

This repository uses a Python script to import data from the GeoJSON endpoint into a SQLite database for use with Datasette.

Requirements

• Python 3.6+
• requests library
• sqlite3 (included with Python)
• geojson-to-sqlite tool

Installation

1. Install dependencies:

   pip install requests datasette geojson-to-sqlite

Usage

1. Run the script:

   python script.py

   The script will:

   • Fetch GeoJSON from Tampa's ArcGIS endpoint
   • Create/update SQLite database (data.db)
   • Normalize timestamps (CREATEDDATE, LASTUPDATE)
   • Track new entries with date_added
   • Mark removed entries as archived with date_archived

2. Schedule daily updates (optional):

   crontab -e

   Add this line to run at midnight daily:

   0 0 * * * cd /path/to/project && /usr/bin/python script.py

Deployment

Deploy with Datasette:

1. Start local server:

   datasette data.db

2. Deploy to cloud (optional):

   datasette publish cloudrun data.db

For more deployment options, see Datasette documentation.

Automated Deployment

This repository uses GitHub Actions to:

1. Run the script daily at midnight UTC
2. Deploy updated database to Vercel via datasette-publish-vercel

To set up automated deployment:

1. Create a Vercel account and get API token
2. Add token as GitHub repository secret named VERCEL_TOKEN
3. Enable GitHub Actions in repository settings
4. Push code to main branch to trigger initial deployment

The live deployment will be available at: https://your-project-name.vercel.app

Data Structure

The database contains these fields:

• RECORDID (Primary Key)
• Location: ADDRESS, UNIT, geometry (GeoJSON Point)
• Status: APPSTATUS, TENTATIVEHEARING, TENTATIVETIME
• Metadata: CREATEDDATE, LASTUPDATE (ISO format)
• System: date_added, date_archived, archived

Development

Temporary files:

• temp.geojson - Deleted after import
• data.db - SQLite database (add to .gitignore)

License

Code is licensed under MIT. Data sourced from City of Tampa Open Data.