# movement-trajectory

This skill guides GPS/GNSS track cleaning, stop and trip detection, road matching, and flow aggregation. Use it when you need to turn timestamped locations into movement analysis.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 35
- Author: Muhammed Enes Duran
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/muend/geoai-skills/tree/HEAD/skills/movement-trajectory/SKILL.md
- Updated: 2026-10-17
- HTML: https://affpapa.org/en/nevibecode/skills/movement-trajectory

## What it does

The skill lays out a workflow for timestamped tracks: inspect sampling intervals, remove duplicate and outlier fixes, and split data across long gaps. It then covers stop and trip detection, road-network matching, flow maps, and origin-destination matrices. Named tools include MovingPandas, Valhalla, OSRM, and mappymatch.

Before setting distance or duration thresholds, state the projected CRS and normalize timestamps to timezone-aware UTC. Check how stop counts change when parameters vary, and report match confidence and unmatched points. Raw GPS can distort both speed and distance. The skill also treats privacy as part of delivery: aggregate tracks before sharing, document the anonymization, and do not publish individual traces without explicit clearance.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill movement-trajectory -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/movement-trajectory/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/movement-trajectory/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill movement-trajectory -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/movement-trajectory/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill movement-trajectory -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/movement-trajectory/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install muend/geoai-skills movement-trajectory
```

2. **Manually: put the files in place**
- File: `.github/skills/movement-trajectory/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill movement-trajectory -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/muend/geoai-skills --path skills/movement-trajectory
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/movement-trajectory/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add muend/geoai-skills --skill movement-trajectory -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/muend/geoai-skills/tree/HEAD/skills/movement-trajectory/SKILL.md

## When to use it
- remove duplicate and noisy GPS fixes
- split tracks into stops and trips
- match observed points to a road network
- aggregate trips into origin-destination flows
- prepare anonymized movement data for sharing

## Example prompts
- Clean these GPS tracks, then identify stops and trips for each object.
- Match the observed points to the road network and report unmatched points.
- Build an origin-destination matrix from these trips and aggregate it before sharing.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 05691aafd6f25580.

BEGIN THIRD-PARTY FILE 05691aafd6f25580
````text
---
name: movement-trajectory
description: >-
  Movement and trajectory analytics from GPS/GNSS tracks: cleaning, stop/trip
  detection, road-network map matching, speed/direction, flow aggregation,
  and origin-destination construction. Use for fleets, human mobility, animal
  tracking, AIS, or sports tracks. Trigger on GPS points, GPX, trajectories,
  stop detection, map matching, or timestamped positions per moving object.
  Also invoke for privacy, aggregation, de-identification, or release of
  individual trajectories. Use network-accessibility-analysis for
  hypothetical routes, isochrones, or static OD costs without observed tracks.
license: MIT
metadata:
  author: Muhammed Enes Duran
---

# Movement & Trajectory Analytics

Purpose: turn noisy timestamped points into defensible movement facts. The
recurring failure modes: **speed computed through GPS noise** (teleporting
points → 400 km/h pedestrians), **stops invented by signal drift**, and
**privacy-blind delivery** of individual-level traces.

## Data model first

A trajectory = ordered fixes per object: `(object_id, timestamp, x, y,
[accuracy, ...])`. Before analysis, report per object: fix count, time
span, median sampling interval, and interval distribution — **sampling
rate drives every method choice** (1 s vehicle traces and 1 fix/hour
animal tags are different problems wearing the same schema).

```python
import movingpandas as mpd
import geopandas as gpd

gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat),
                       crs=4326).to_crs(gdf_utm_epsg)
tc = mpd.TrajectoryCollection(gdf, "object_id", t="timestamp")
```

### Declare CRS and time base before any threshold

Every distance radius, speed limit and dwell duration in this skill is
meaningless until two things are stated **in the answer, before the number is
used**:

1. **The projected CRS** all distance and speed computation runs in — a "50 m
   stop radius" applied to raw lon/lat degrees is not 50 m anywhere, and the
   error scales with latitude. Name the CRS (`estimate_utm_crs()` for a local
   fleet, an equal-distance projection for continental extents).
2. **The timestamp base**, normalised to timezone-aware UTC. Fleet logs mix
   local times, DST shifts and naive strings; a dwell that straddles a DST
   boundary gains or loses an hour, and stop durations silently corrupt.

State both before proposing a radius or duration, not afterwards as a caveat.

**Declaring is not withholding.** An unknown CRS or timezone is never grounds to
stop and ask instead of answering. State it as an explicit, named assumption and
deliver the method anyway:

> Assuming a local UTM zone for distance and that timestamps are naive local time
> needing UTC normalisation — confirm both, since they change dwell durations.

Then give the cleaning steps, the parameters, and the sensitivity check. A
````
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First 2886 of 8168 bytes. © Muhammed Enes Duran · MIT · https://github.com/muend/geoai-skills/tree/HEAD/skills/movement-trajectory/SKILL.md

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---
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