extract_stay_points

Extract stay points from trajectory data into JSONL files.

Updated Jun 2, 2026

Search Tags:#GPS #urban planning #geohash #mobile analytics #trajectory data #stay point #OD

Ready to run

npx skills add https://github.com/bettercallfan/deerflow --skill extract-stay-points

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: extract_stay_points
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/spatiotemporal_trajectory/extract_stay_points
Command: npx skills add https://github.com/bettercallfan/deerflow --skill extract-stay-points

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of identifying and extracting meaningful stay points from various trajectory data sources, such as GPS, check-in, signaling, taxi, or cleaned trajectory data.

Core Features & Use Cases

Stay Point Detection: Detects places where a user has stayed within a specified spatial radius for a minimum dwell time.
Data Formats: Supports input data in CSV, TSV, JSON, JSONL, trajectory records, and staypoints JSONL formats.
Use Case: Ideal for analyzing user movement patterns, urban region profiling, or identifying anomalies in stay durations.

Quick Start

Run the following command to extract stay points from your trajectory data:

cd /mnt/skills/custom/extract_stay_points
python3 scripts/extract_stay_points.py --input /path/to/trajectory_data.csv --output-dir /path/to/output

Frequently Asked Questions about extract_stay_points

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

FAQPage Schema

How do I extract stay points from GPS trajectory data?▼

Stay point detection identifies locations where a user remains within a specified spatial radius for a minimum dwell time. It extracts these meaningful stops from GPS trajectory data, outputting detailed stay point records to JSONL files for mobility analysis.

What input formats are supported for trajectory data stay point extraction?▼

Stay point extraction supports input data in CSV, TSV, JSON, JSONL, trajectory records, and staypoints JSONL formats. This allows you to process diverse sources including GPS, check-in, signaling, and cleaned taxi trajectory data directly.

Can I use Python to identify user dwell locations for urban planning?▼

Yes, you can use the Python script to identify user dwell locations for urban planning. It detects stay points based on spatial radius and dwell time thresholds, enabling urban region profiling and user movement pattern analysis from trajectory data.

What is the best way to analyze movement patterns and OD from mobile signaling data?▼

Analyzing movement patterns and OD from mobile signaling data is best achieved by first extracting stay points. The script processes signaling data to find dwell locations, outputting JSONL files that isolate meaningful stops for user behavior study.

What spatial radius and dwell time parameters are needed to detect stay points?▼

Detecting stay points requires defining a specific spatial radius and a minimum dwell time threshold. These parameters filter trajectory data to isolate locations where users remained within the defined boundary for the specified duration.