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predict_next_location

Predict the next location from historical user trajectories using a Markov chain model.

Updated Jun 2, 2026
Search Tags:#trajectory analysis#geohash#mobility analysis#location prediction#destination inference
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill predict-next-location
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: predict_next_location
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/spatiotemporal_trajectory/predict_next_location
Command: npx skills add https://github.com/bettercallfan/deerflow --skill predict-next-location

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python3, scripts/predict_next_location.py, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill predicts the next location or destination based on historical user trajectories, helping to anticipate future movements.

Core Features & Use Cases

  • Location Prediction: Predicts the next stop or destination from historical geohash or coordinates.
  • Trajectory Analysis: Utilizes historical trajectory data to train a first-order transition model.
  • Use Case: For a user who frequently travels for work, this Skill can predict their next destination based on their past travel patterns.

Quick Start

Use the predict_next_location skill to predict the next location from the provided staypoint history file 'user_trajectory.jsonl'.

Frequently Asked Questions about predict_next_location

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

FAQPage Schema
How do I predict a user's next destination from historical trajectory data?▼

You can predict the next location by providing a historical user trajectory file, such as a JSONL file containing staypoints or coordinates, which the Skill uses to train a first-order Markov chain transition model.

How does a Markov chain model work for mobility analysis and next stop prediction?▼

For mobility analysis, a Markov chain model calculates transition probabilities between historical locations, enabling next stop prediction by estimating the statistical likelihood of moving from the current location to the next based on past trajectories.

Can I use geohash data for destination inference with Python?▼

Yes, you can use geohash data for destination inference with Python, as this Skill processes historical geohash or coordinate trajectories in Python to predict the next stop or final destination.

What is the best way to format staypoint history for trajectory analysis?▼

The best way to format staypoint history for trajectory analysis is using a JSONL file structure, where each line represents a user trajectory record containing sequential geohash or coordinate data for the transition model.

What are the limitations of using a first-order transition model for location prediction?▼

A limitation of using a first-order transition model for location prediction is that it only considers the immediately preceding location to infer the next destination, potentially missing complex long-term dependencies in user mobility patterns.

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