There are multiple ways to model trajectory data. This post takes a closer look at the OGC® Moving Features Encoding Extension: Simple Comma Separated Values (CSV). This standard has been published in 2015 but I haven’t been able to find any reviews of the standard (in a GIS context or anywhere else).
The following analysis is based on the official OGC trajcectory example at http://docs.opengeospatial.org/is/14-084r2/14-084r2.html#42. The header consists of two lines: the first line provides some meta information while the second defines the CSV columns. The data model is segment based. That is, each line describes a trajectory segment with at least two coordinate pairs (or triplets for 3D trajectories). For each segment, there is a start and an end time which can be specified as absolute or relative (offset) values:
@stboundedby,urn:x-ogc:def:crs:EPSG:6.6:4326,2D,50.23 9.23,50.31 9.27,2012-01-17T12:33:41Z,2012-01-17T12:37:00Z,sec
a, 10,150,11.0 2.0 12.0 3.0,walking,1
b, 10,190,10.0 2.0 11.0 3.0,walking,2
a,150,190,12.0 3.0 10.0 3.0,walking,2
c, 10,190,12.0 1.0 10.0 2.0 11.0 3.0,vehicle,1
Let’s look at the first data row in detail:
a… trajectory id
10… start time offset from
150… end time offset from
11.0 2.0 12.0 3.0… trajectory coordinates: x1, y1, x2, y2
1… type code
My main issues with this approach are
- They missed the chance to use WKT notation to make the CSV easily readable by existing GIS tools.
- As far as I can see, the data model requires a regular sampling interval because there is no way to store time stamps for intermediate positions along trajectory segments. (Irregular intervals can be stored using segments for each pair of consecutive locations.)
In the common GIS simple feature data model (which is point-based), the same data would look something like this:
The main issue here is that there has to be some application logic that knows how to translate from points to trajectory. For example, trajectory a changes from walking1 to walking2 at
2012-01-17T12:36:11Z but we have to decide whether to store the previous or the following state code for this individual point.
An alternative to the common simple feature model is the PostGIS trajectory data model (which is LineStringM-based). For this data model, we need to convert time stamps to numeric values, e.g. 2012-01-17T12:33:41Z is 1326803621 in Unix time. In this data model, the data looks like this:
a,LINESTRINGM(11.0 2.0 1326803631, 12.0 3.0 1326803771),walking,1
a,LINESTRINGM(12.0 3.0 1326803771, 10.0 3.0 1326803811),walking,2
b,LINESTRINGM(10.0 2.0 1326803631, 11.0 3.0 1326803811),walking,2
c,LINESTRINGM(12.0 1.0 1326803631, 10.0 2.0 1326803771, 11.0 3.0 1326803811),vehicle,1
This is very similar to the OGC data model, with the notable difference that every position is time-stamped (instead of just having segment start and end times). If one has movement data which is recorded at regular intervals, the OGC data model can be a bit more compact, but if the trajectories are sampled at irregular intervals, each point pair will have to be modeled as a separate segment.
Since the PostGIS data model is flexible, explicit, and comes with existing GIS tool support, it’s my clear favorite.
- Movement data in GIS: issues & ideas
- Movement data in GIS #2: visualizing individual trajectories
- Movement data in GIS #3: visualizing massive trajectory datasets
- Movement data in GIS #4: variations over time
- Movement data in GIS #5: current research topics
- Movement data in GIS #6: updates from AGILE2017
- Movement data in GIS #7: animated trajectories with TimeManager
- Movement data in GIS #8: edge bundling for flow maps
- Movement data in GIS extra: trajectory generalization code and sample data