Movement, tracks, speed, and dwell
Combine positions and timestamps to reason about motion without confusing recorded facts with values you derive from them.
Why this matters
A track is more than a line on a map. Time tells you sequence and lets you calculate speed, dwell, progress, and transitions between areas.
Learn it
A movement dataset usually contains time-ordered positions. A LineString can summarize the path, while point observations preserve individual timestamps and other state. Speed is derived from distance divided by elapsed time. Dwell describes remaining within an area or near a location for a period. Keep the original observations so derived values can be recalculated.
Work through an example
If a vehicle reports once every minute, the map can connect positions into a track while still retaining each timestamp and telemetry record.
position₁ @ t₁ → position₂ @ t₂
speed ≈ distance(position₁, position₂) / (t₂ − t₁)Practice
- 1Take three timed positions and compute one approximate speed using the distance lesson.
- 2Identify where a LineString is useful and where individual timed points are still necessary.
- 3Define a simple dwell rule in plain language.
Try it
Haversine distance
The Haversine formula estimates great-circle distance between two longitude/latitude points by treating Earth as a sphere. It is useful for many distance checks, but it is an approximation rather than a full ellipsoidal geodesic.
What to watch for
A line connecting sparse positions does not prove the object followed every point along that line between observations.
Where this takes you
Use this for vehicles, wildlife, field teams, trips, shipments, robots, and other moving sources.