search_spatiotemporal

Search trajectory records with temporal and spatial filters from JSONL files.

Updated Jun 2, 2026

Search Tags:#search #filtering #trajectory #GPS #spatiotemporal #geohash #OD

Ready to run

npx skills add https://github.com/bettercallfan/deerflow --skill search-spatiotemporal

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: search_spatiotemporal
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/spatiotemporal_trajectory/search_spatiotemporal
Command: npx skills add https://github.com/bettercallfan/deerflow --skill search-spatiotemporal

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the process of searching and recalling trajectory records by providing advanced temporal and spatial filtering options, enabling users to quickly locate specific data points within large datasets.

Core Features & Use Cases

Temporal Filtering: Search records within specific time windows or hour ranges.
Spatial Filtering: Use geohash prefixes, bounding boxes, or city-specific searches.
Semantic Text Matching: Apply semantic text conditions to refine search results.
Use Case: For instance, identify all subway commutes within a specific city during rush hour using a combination of time and spatial filters.

Quick Start

Use the search_spatiotemporal skill to search for trajectory records within Beijing from 7 AM to 9 AM on a specific date.

Frequently Asked Questions about search_spatiotemporal

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

FAQPage Schema

How do I filter GPS trajectory records by time and location?▼

You can filter GPS trajectory records by applying temporal filters for specific time windows and spatial filters like geohash prefixes or bounding boxes to locate matching data points within large datasets.

Can I search JSONL trajectory data for trips within a specific bounding box?▼

Yes, you can search JSONL trajectory data using spatial filtering options including bounding boxes, geohash prefixes, and city-specific searches to narrow down matching location records.

What is the best way to find subway commutes during rush hour in a specific city?▼

Finding subway commutes during rush hour involves combining temporal filters for specific hour ranges with spatial filters and semantic text matching to refine mobility analysis results for a target city.

Does this trajectory search tool require any external dependencies?▼

No external dependencies are required to run this trajectory search tool, as it utilizes Python for filtering and text matching directly on your JSONL input files to output matches and summaries.

How do I apply semantic text conditions to refine trajectory search results?▼

You can apply semantic text conditions alongside temporal and spatial filters to refine trajectory search results, enabling you to isolate specific mobility patterns like transit modes from matched records.