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measure_spatiotemporal_entropy

Calculate spatiotemporal entropy and vitality index from OD matrices and evidence records.

Updated Jun 2, 2026
Search Tags:#data processing#entropy#urban analysis#spatiotemporal analysis#mobility complexity
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill measure-spatiotemporal-entropy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: measure_spatiotemporal_entropy
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/spatiotemporal_trajectory/measure_spatiotemporal_entropy
Command: npx skills add https://github.com/bettercallfan/deerflow --skill measure-spatiotemporal-entropy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of quantifying the complexity and diversity of regional mobility patterns by calculating spatiotemporal entropy.

Core Features & Use Cases

  • Spatiotemporal Entropy Calculation: Measures entropy of inflow origins, outflow destinations, and active time slots.
  • Vitality Index: Computes a composite vitality index to assess regional vitality.
  • Data Processing: Utilizes OD matrices and evidence records to quantify regional mobility complexity.
  • Use Case: For urban planners or analysts looking to understand the complexity and diversity of urban mobility patterns.

Quick Start

Run the 'measure_spatiotemporal_entropy' skill with an OD matrix and evidence records to analyze mobility complexity.

Frequently Asked Questions about measure_spatiotemporal_entropy

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

FAQPage Schema
How do I calculate spatiotemporal entropy for urban mobility patterns?▼

You calculate spatiotemporal entropy by processing OD matrices and evidence records with Python to measure the diversity of inflow origins, outflow destinations, and active time slots. This quantifies regional mobility complexity for urban analysis.

What is a vitality index in regional mobility analysis?▼

A vitality index in regional mobility analysis is a composite metric computed from spatiotemporal entropy. It assesses regional vitality by quantifying the complexity and diversity of inflow origins, outflow destinations, and active time slots using OD matrices.

Can I use OD matrices to quantify urban mobility complexity?▼

Yes, you can use OD matrices to quantify urban mobility complexity. By combining OD matrices with evidence records in Python, the analysis calculates spatiotemporal entropy and a composite vitality index for regional mobility patterns.

Do I need evidence records to measure spatiotemporal entropy?▼

Yes, you need evidence records alongside OD matrices to measure spatiotemporal entropy. These records provide the necessary temporal data to calculate active time slots, inflow origins, outflow destinations, and the composite vitality index.

What's the best way to analyze regional mobility diversity with Python?▼

The best way to analyze regional mobility diversity with Python is to calculate spatiotemporal entropy using OD matrices and evidence records. This approach measures inflow, outflow, and temporal diversity to compute a composite vitality index.

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