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transbigdata-grid

Map GPS trajectory points to spatial grid cells for aggregation.

4|2|Updated Feb 4, 2026
Search Tags:#geospatial#gps#heatmap#gridding#transbigdata#geohash
One-click install
npx skills add https://github.com/ni1o1/claude-skill-transbigdata --skill transbigdata-grid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: transbigdata-grid
Source: https://github.com/ni1o1/claude-skill-transbigdata/tree/main/skills/transbigdata-grid
Command: npx skills add https://github.com/ni1o1/claude-skill-transbigdata --skill transbigdata-grid

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Transforms raw GPS and trajectory data into discrete spatial grids so users can aggregate, visualize, and analyze mobility patterns at city or regional scales.

Core Features & Use Cases

  • Generate grid parameters from bounding boxes or GeoDataFrames and create rectangular, triangular, or hexagonal grids for a study area.
  • Map GPS points to grid cells and retrieve grid centers or polygons for aggregation, heatmaps, and spatial joins.
  • Support Geohash encoding and decoding, and optimize grid parameters to improve spatial aggregation accuracy.
  • Use cases include taxi trip heatmaps, OD matrix extraction, urban hotspot detection, and combining grid outputs with administrative boundaries.

Quick Start

Create 500m rectangular grid parameters for a bounding box and map a CSV of GPS points to grid cell identifiers for aggregation.

Frequently Asked Questions about transbigdata-grid

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

FAQPage Schema
How do I map GPS trajectory points to spatial grid cells for aggregation?▼

To map GPS trajectory points to spatial grid cells, use functions that generate grid parameters from a bounding box and assign point coordinates to discrete grid identifiers for spatial aggregation. This transforms raw GPS data into structured grids.

What is the best way to create hexagonal or triangular grids for a study area from a GeoDataFrame?▼

Creating hexagonal or triangular grids from a GeoDataFrame involves generating optimized grid parameters for the geometry and applying the specified grid type to partition the study area. This enables uniform spatial analysis across the region.

Can I extract OD matrices and heatmaps from taxi telemetry data using spatial gridding?▼

You can extract OD matrices and generate heatmaps from taxi telemetry by mapping GPS points to grid cells. Aggregating these grid identifiers allows you to calculate trip densities and origin-destination pairs for urban mobility analysis.

Does this approach support Geohash encoding and decoding for spatial joins?▼

Yes, the approach supports Geohash encoding and decoding alongside rectangular, triangular, and hexagonal gridding. You can map points to grid cells and retrieve grid polygons or centers to perform spatial joins with administrative boundaries.

How do I optimize grid parameters to improve spatial aggregation accuracy for regional scale data?▼

Optimizing grid parameters for spatial aggregation involves applying parameter optimization functions to define precise grid sizes, such as a 500m rectangular grid. This improves the accuracy of density heatmaps and spatial joins across regional datasets.

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