Abstract
Connected devices, paradigms of the Internet of Things and Big Data increasingly define our everyday life. In this context, modern automobiles, which are characterized by an increase of electronic components and extensive sensor devices, potentially are becoming a new kind of mobile and anytime accessible sensors. In this context “Extended Floating Car Data” (XFCD) is a rich geocoded dataset for vehicle, traffic, and environment data, augmenting more traditional geospatial databases. This paper deals with the approach to collect and use this data from automobiles for context-aware geospatial analyses by combining the sensor parameters with a spatial and temporal component. These data concern the concept of XFCD as geo-information and needs to be made available and applicable to spatio-temporal visualization. For this approach, research already conducted should be considered and findings should be used for more in-depth research.
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Acknowledgement
This research work is funded with a PhD scholarship by the German Research Foundation (DFG) within the research training group 1539 “Visibility and Visualisation - Hybrid Forms of Pictorial Knowledge” at the University of Potsdam. This support is gratefully acknowledged. The above PhD project is supervised by Hartmut Asche (University of Potsdam) and Frank Heidmann (Potsdam University of Applied Sciences).
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Voland, P. (2016). Processing and Geo-visualization of Spatio-Temporal Sensor Data from Connected Automotive Electronics Systems. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_23
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