GeoJSON Map Viewer
A custom GeoJSON Map Viewer was built using AI coding agents (GPT-5.6-Sol, Claude Code, Fable 5.1) to visualize local political boundary data on an interactive OpenStreetMap The tool supports pasting GeoJSON objects (Feature, FeatureCollection, or Geometry), customizing fill color and opacity, and exporting maps as PNG images ChatGPT Work was used to extract and combine boundary data from multiple government sources to generate accurate GeoJSON polygons for specific districts Two distinct politi
Analysis
TL;DR
- A custom GeoJSON Map Viewer was built using AI coding agents (GPT-5.6-Sol, Claude Code, Fable 5.1) to visualize local political boundary data on an interactive OpenStreetMap
- The tool supports pasting GeoJSON objects (Feature, FeatureCollection, or Geometry), customizing fill color and opacity, and exporting maps as PNG images
- ChatGPT Work was used to extract and combine boundary data from multiple government sources to generate accurate GeoJSON polygons for specific districts
- Two distinct political boundaries (Granada Community Services District and Midcoast Community Council) were successfully rendered together on a single map view
Why It Matters
This demonstrates a practical workflow where AI coding agents can rapidly prototype and iterate on specialized data visualization tools without requiring deep expertise in mapping libraries or geospatial programming. It also highlights how AI assistants can bridge the gap between raw government open data and usable visual outputs by handling data extraction, transformation, and aggregation autonomously.
Technical Details
- Built using GPT-5.6-Sol for initial tool generation, with iterative refinement through Claude Code for web development and Fable 5.1 (a functional programming framework)
- Renders GeoJSON data (Feature, FeatureCollection, and Geometry types) directly on OpenStreetMap with customizable styling (fill color and opacity controls)
- Supports export functionality to PNG for sharing or documentation purposes
- GeoJSON boundary data was sourced by prompting ChatGPT Work to extract and combine files from government data sources for specific jurisdictions (GCSD and MCC boundaries near Half Moon Bay, CA)
Industry Insight
- The rapid prototyping cycle (prompt → build → iterate with multiple AI agents) shows that specialized geospatial tools can now be built in hours rather than days, lowering the barrier for non-specialists to create custom mapping solutions
- AI agents are increasingly capable of navigating and synthesizing fragmented government open data sources, suggesting a growing ecosystem of AI-assisted data engineering workflows for civic and organizational use cases
- The multi-agent collaboration pattern (GPT for ideation, Claude Code for implementation, Fable for functional refinement) represents an emerging best practice for complex tool development that professionals should adopt and study
Disclaimer: The above content is generated by AI and is for reference only.