In recent years, big data sets from mobile phones have been used to provide increasingly accurate analyses of how we all move between home, work, and leisure, holiday and everything else. The strength of basing analyses on mobile phone data is that they provide accurate data on when, how, and how far each individual moves without any particular focus on whether they are passing geographical boundaries along the way—we simply move from one coordinate to another in a system of longitude and latitude.
“The problem with existing big data models however is that they do not capture what geographical structures such as neighbourhoods, towns, cities, regions, countries etc. mean for our mobility. This makes it difficult, for example, to generate good models for future mobility. And it is insights of this kind we need when new forms of transport crop up, or when urbanization takes hold,” explains Sune Lehmann, professor at DTU and at the University of Copenhagen.
In fact, the big data approach to modelling location data has erased the usual dimensions that characterize geographical areas and their significance for our daily journeys and patterns of movement. In mobility research these are known as scales.
“Within mobility research, things are sometimes presented as if scale does not come into the equation. At the same time, however, common sense tells us that there have to be typical trips or patterns of movement, which are determined by geography. Intuitively it seems wrong that you cannot see, for example, that a neighbourhood or urban zone has a typical area. A neighbourhood is a place where you can go down and pick up a pizza or buy a bag of sweets. It doesn’t make sense to have a neighbourhood the size of a small country. Geography must play a role. It’s a bit of a paradox,” says Laura Alessandretti, Assistant Professor at DTU and University of Copenhagen.
Read more at Technical University of Denmark
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