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classify_trajectory_state

Classify user trajectories into motion states using Python scripts with geohash diversity features.

Updated Jun 2, 2026
Search Tags:#trajectory analysis#urban planning#geohash#user movement#spatiotemporal data
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill classify-trajectory-state
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: classify_trajectory_state
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/spatiotemporal_trajectory/classify_trajectory_state
Command: npx skills add https://github.com/bettercallfan/deerflow --skill classify-trajectory-state

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of classifying user trajectories into various motion states, providing valuable insights into user movement patterns.

Core Features & Use Cases

  • Trajectory State Classification: Classify user trajectories into states such as stationary, directed migration, frequent circulation, random walk, cruising, long-stay, or semantic mobility behavior.
  • Algorithmic Approach: Utilizes features like average speed, spatial span, directionality, and geohash diversity for accurate classification.
  • Use Case: For urban planning and analysis, this Skill can help identify patterns in population movement, optimizing city services and infrastructure.

Quick Start

Use the classify_trajectory_state skill to classify the trajectory states of users from the provided 'staypoints.jsonl' file.

Frequently Asked Questions about classify_trajectory_state

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

FAQPage Schema
How do I classify user trajectory motion states from spatiotemporal data?▼

You can classify user trajectory motion states by running the classify_trajectory_state.py script. It evaluates average speed, spatial span, directionality, and geohash diversity to categorize user movements for urban planning analysis.

What trajectory motion states can be identified using geohash diversity and speed?▼

Trajectory state classification identifies stationary, directed migration, frequent circulation, random walk, cruising, long-stay, and semantic mobility behavior by analyzing average speed, spatial span, directionality, and geohash diversity.

How do I start classifying user movement patterns from a staypoints.jsonl file?▼

To classify user movement patterns, execute the classify_trajectory_state skill using your staypoints.jsonl file as input. The script processes the spatiotemporal data to output specific trajectory motion states.

Can I use this trajectory analysis approach for urban planning and population movement?▼

Yes, trajectory state classification is designed for urban planning and analysis. It helps identify patterns in population movement, optimizing city services and infrastructure based on user trajectory states.

What features are required to classify directed migration or random walk trajectories?▼

Classifying directed migration or random walk trajectories requires features like average speed, spatial span, directionality, and geohash diversity. These metrics are calculated by the Python script to determine the motion state.

Does trajectory state classification require specific Python dependencies or environments?▼

Trajectory state classification requires the classify_trajectory_state.py script for execution. It processes spatiotemporal data without additional dependencies to output user movement pattern classifications.

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