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The development of sensors such as GPS trackers that capture movement data in realtime\ud and at detailed spatial and temporal scales has transformed our ability to collect mobility\ud data (Kwan and Neutens, 2014). Currently, however, these developments in movement data\ud collection technologies are much further ahead than current methods for extracting meaningful patterns from such data (Laube et al., 2007; Long and Nelson, 2012). Furthermore, there have been very few studies that have tried to explore decision-making processes related with movements using GPS data and similar sources. There is a need therefore to discover if new forms of mobility data can be translated into new insights into movement behaviour. Here we do this through an examination of the calibration of spatial interaction with GPS data. As part of this experiment, we discover what needs to be done to transform raw GPS data into origin-destination matrices of aggregated flows. |
