Movement Trajectory

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).

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")

Work in a projected CRS for all speed/distance computation; keep timestamps timezone-aware UTC (mixed local times create midnight teleports).

Cleaning pipeline (in order)

1. Deduplicate identical (object, timestamp) fixes.
2. Accuracy filter: drop fixes above an HDOP/accuracy threshold if

the column exists (report the threshold and % dropped).

1. Speed filter: drop fixes implying impossible speed for the mode

(walk > 15 km/h sustained, car > 200 km/h...); iterate — one bad fix creates two bad segments (mpd.OutlierCleaner).

1. Gap splitting: split trajectories at temporal gaps (e.g., > 5×

median interval) — interpolating across a tunnel/power-off invents movement.

1. Optional smoothing (Kalman/rolling median) for jittery urban-canyon

data — AFTER outlier removal, and never before stop detection tuning.

Accounting line per step: fixes in → out.

Stops and trips

Stop = spatial dwell: fixes within a distance radius for a minimum duration (mpd.TrajectoryStopDetector(max_diameter=50, min_duration=timedelta(minutes=5))). The two parameters ARE the result — report them and run a ±50% sensitivity check; urban-canyon drift mimics movement, so diameter ~30 s degrades matching sharply — report match confidence and the % of unmatched points; don't silently keep unmatched geometry.

• Never map-match animal tracks or off-road movement (obviously) — and

don't compute "distance traveled" from raw noisy fixes either (noise inflates path length ~5-20%); smooth first, state the method.

Aggregate analytics

• Flow maps / desire lines: aggregate OD pairs before plotting

(cartography-geoviz for delivery); hairball avoidance = zone-level aggregation + minimum-flow threshold.

• Density: KDE or hex-bin of fixes vs of trips — fixes overweight slow

movement (dwell = many fixes); use trip-based or time-weighted density and say which.

• Space-time clustering (co-location, convoys): ST-DBSCAN family;

cluster parameters in both space and time reported together.

• Sequence/periodicity: hour-of-day × day-of-week activity matrices per

object class before any behavioral claims.

Privacy — non-optional

Individual trajectories are personal data almost everywhere (GDPR etc.) and are notoriously re-identifiable (home/work anchor pairs identify most people). Defaults: aggregate before sharing (zones ≥ k objects, suppress cells < k, typical k=5-10), truncate trip ends near homes, and never publish raw individual traces without explicit clearance. State the anonymization applied in every deliverable.

Verification protocol

1. Speed histogram per mode after cleaning — tail must be physically

plausible.

1. Map 3 sample trajectories (raw vs cleaned vs matched) over a basemap.
2. Stop-detection sensitivity: parameters ±50%, report stop-count change.
3. OD totals reconcile with trip counts (accounting).

Pitfalls checklist

• Speeds computed across gaps or through outlier fixes.
• Distance traveled from raw (unsmoothed, unmatched) fixes.
• Stops detected with radius below GPS noise, or drift counted as trips.
• Mixed timezones / DST jumps creating phantom teleports.
• Fix-density maps read as movement-density maps.
• Individual traces shipped without aggregation/suppression.
• Trajectories split by object but not by temporal gap.

Execution contract

• Workflow: validate identifiers, time, and CRS; segment tracks; remove impossible fixes; infer stops or trips; optionally map-match; aggregate; apply privacy controls; verify.
• Decision rules: use trajectory methods for observed timestamped movement, network analysis for possible routes or access, and point-pattern methods when sequence and identity are absent.
• Verification protocol: inspect speed and gap distributions, map raw-versus-cleaned samples, perturb stop parameters, reconcile trip and OD counts, and audit disclosure risk.
• Failure modes: suppress or qualify results for timezone ambiguity, long gaps, implausible speeds, poor network matching, sparse sampling, or re-identification risk.
• Deliverables: cleaned trajectories or approved aggregates, segmentation rules, quality report, derived stop/trip tables, privacy treatment, maps, and limitations.
• Source freshness: consult [the authoritative source registry](references/authoritative-sources.md) before using format, library, or privacy guidance and record the checked date.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: muend
• Source: muend/geoai-skills
• License: MIT

Install and usage instructions live in the source repository linked above.