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forecast_region_flow

Predict future regional flow trends from historical activity series or trajectory points.

Updated Jun 2, 2026
Search Tags:#forecasting#trajectory analysis#urban planning#spatiotemporal analysis#regional flow
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill forecast-region-flow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forecast_region_flow
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/spatiotemporal_trajectory/forecast_region_flow
Command: npx skills add https://github.com/bettercallfan/deerflow --skill forecast-region-flow

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill predicts future people or vehicle flow for target regions based on historical regional activity series or raw trajectory points, helping with regional decision support.

Core Features & Use Cases

  • Forecasting: Predicts future regional flow trends.
  • Input Types: Accepts historical regional activity series or raw trajectory points.
  • Use Case: For city planners who need to anticipate future traffic patterns or for event organizers looking to predict crowd flow in a specific area.

Quick Start

Run the forecast_region_flow skill with the following command:

python3 scripts/forecast_region_flow.py \
  --input /path/to/region_heat.jsonl \
  --output-dir /path/to/output \
  --group-col geohash \
  --metric-col activity_count \
  --time-col time_bucket \
  --time-bucket day \
  --forecast-steps 3

Frequently Asked Questions about forecast_region_flow

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

FAQPage Schema
How do I forecast regional flow trends from historical trajectory data?▼

You can forecast regional flow trends by processing historical regional activity series or raw trajectory points using Python scripts. The skill analyzes spatiotemporal data to predict future people or vehicle flow patterns for target regions.

What input formats are needed for regional flow prediction?▼

Regional flow prediction requires historical data provided as regional activity series or raw trajectory points in JSONL format. You must specify columns for geographic grouping, activity metrics, and time buckets to run the forecast.

Can I predict crowd flow for event management using spatiotemporal analysis?▼

Yes, spatiotemporal analysis supports event management by predicting future crowd flow in specific areas. The skill processes historical regional activity series to help organizers anticipate attendance patterns.

How do I run the regional flow forecasting script with custom time intervals?▼

Run the Python script by specifying the time bucket parameter, such as day, along with input path, geographic grouping column, and forecast steps. This configures the script to predict flow patterns for your desired time intervals.

Does urban planning traffic forecasting work with raw trajectory points?▼

Yes, urban planning traffic forecasting accepts raw trajectory points as input. The skill processes this spatiotemporal data to predict future vehicle flow patterns, supporting city planners in anticipating traffic trends.

What is the best way to predict future people flow for city planning decisions?▼

The best way to predict future people flow is using historical regional activity series analyzed through Python scripts. This approach provides data-driven forecasts to support regional decision-making in urban planning contexts.

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