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). ```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 UT...