Abstract
Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover’s Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements.
Original language | English |
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Title of host publication | Geographic Information Science - 9th International Conference, GIScience 2016, Proceedings |
Publisher | Springer |
Pages | 225-239 |
Number of pages | 15 |
Volume | 9927 LNCS |
ISBN (Print) | 9783319457376 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | 9th International Conference on Geographic Information Science, GIScience 2016 - Montreal, Canada Duration: 27 Sep 2016 → 30 Sep 2016 Conference number: 9 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9927 LNCS |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference
Conference | 9th International Conference on Geographic Information Science, GIScience 2016 |
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Abbreviated title | GIScience 2016 |
Country | Canada |
City | Montreal |
Period | 27/09/16 → 30/09/16 |