gis-data-gen

Generates seeded GeoJSON point, line, and polygon datasets within bounding boxes or masks for GIS testing and demos.

Updated Aug 27, 2026

Search Tags:#testing #benchmark #geospatial #data-generation #geojson #gis #random-data

Ready to run

npx skills add https://github.com/jumboly/gis-skills --skill gis-data-gen

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gis-data-gen
Source: https://github.com/jumboly/gis-skills/tree/main/gis-data-gen
Command: npx skills add https://github.com/jumboly/gis-skills --skill gis-data-gen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, shapely, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill provides quick generation of synthetic GIS data (points, lines, and polygons) within a geographic area, enabling reliable testing, benchmarking, and demos without real data.

Core Features & Use Cases

Generate random points, lines, and polygons inside a bounding box or GeoJSON mask.
Output a GeoJSON FeatureCollection with optional metadata and deterministic seeding for reproducibility.
Create sample administrative boundaries or test geometries via boundary fetch (Overpass integration) and polygon clipping.
Use cases include unit tests for geoprocessing pipelines, performance benchmarking, and data fixtures for demos.

Quick Start

Run the gis-data-gen tool with a bbox or mask to generate a GeoJSON FeatureCollection of random GIS data suitable for testing.

Frequently Asked Questions about gis-data-gen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate random GeoJSON data for testing a geospatial pipeline?▼

Generate random GeoJSON data by creating synthetic points, lines, and polygons within a specified bounding box or GeoJSON mask. Output a GeoJSON FeatureCollection with optional metadata and deterministic seeding to ensure reliable, reproducible test fixtures for geospatial pipelines.

Can I generate reproducible random GIS data with a fixed seed?▼

Yes, you can generate reproducible random GIS data using deterministic seeding. Specifying a fixed seed ensures the synthetic points, lines, and polygons remain identical across runs, providing consistent benchmarking and validation for geospatial workflows.

Does this geospatial data generator support polygon clipping with administrative boundaries?▼

Yes, geospatial data generation supports polygon clipping with administrative boundaries. It fetches boundaries via Overpass integration and applies clipping to create sample administrative boundaries or test geometries constrained to specific geographic areas for demos.

Do I need SciPy and Shapely installed to generate synthetic GIS data?▼

Yes, you need SciPy and Shapely installed to generate synthetic GIS data using this method. The generation processes rely on these dependencies for geometric calculations and spatial operations, with optional reproducible dependency installation available.

What's the best way to create random points within a GeoJSON mask for benchmarks?▼

The best way to create random points within a GeoJSON mask for benchmarks is using a generator that applies spatial constraints directly to the mask. This ensures all synthetic geometries stay within the specified area for accurate performance testing.