Detection of meaningful locations from passive mobile positioning data using location profiling
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Mobile positioning data is a promising source for investigating people’s activity patterns. People regularly visit locations that have different functions to them. Locations with similar activity patterns can be distinguished from the data based on people’s calling activities. The problem with assigning meaning to these locations in the data is limited information about the person and access to ground truth data. The thesis proposes a method to profile locations and assign meanings to differently behaving location groups. In the course of the work, various features are added to the location points by means of which they are classified. Additionally, an expert’s opinion was considered to provide input for the classes.
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