Thoughts on “How fast is a cow? Cross-Scale Analysis of Movement Data”

It was interesting to see a direct link to Scale (the presentation I gave this week) in the very first paragraph of this paper. It just reaffirmed how scale is a central tenet of many different sub-fields of GIS, from uncertainty to VGI to movement data in this particular case.

The authors enumerate the many different factors which influence the collection of movement data, from sampling method to measurement of distance (euclidian vs. network) and the nature of the space being traversed. One of the concerns they highlight is “sinful simulation” and this reminds me of our discussions of abstraction pertaining to algorithms, agent based modelling, and spatial data mining. For all these methods, the information lost in order to model behaviour or trends is always a concern and I wonder what steps are taken to address the loss of spatial or other crucial dimensions for movement data.

Another common theme discussed by the authors is the issue of relativity and absoluteness. In their decision to focus on temporal scale, they reiterate that as with slope, “there is no true speed at a given timestamp” (403) because this is dependent on the speed at adjacent points, and is relative. But they say that the speed is dependent on the scale at which it it measured and this confused me because whether they measured it in cm/s or inch/minute, is it the unit which they are using to speak about granularity? Because if so, then regardless of the unit of measurement the speed should be the same. I wonder what they mean by there is no absolute speed at a given timestamp if they are referring to it in terms of a scale issue and not a relative measurement/sampling issue.

The authors contend that the nascency of the field of movement data analysis means that researchers rarely question the choice of a particular temporal scale or parameter definition, and this is definitely an important issue as we have seen with the illustration of the MAUP and gerrymandering. The fact that all these subfields are subsumed within the umbrella of GIS, and that researchers tend to have some “horizontal” knowledge about how methods have been developed and critiqued in other fields, hopefully means that they can adopt the same critical attitude and lessons learned from the past towards this new domain of research.


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