%0 Journal Article %1 Andrienko:2007:VAT:1345448.1345455 %A Andrienko, Gennady %A Andrienko, Natalia %A Wrobel, Stefan %C New York, NY, USA %D 2007 %I ACM %J SIGKDD Explor. Newsl. %K biological-sequence-analysis %P 38--46 %R 10.1145/1345448.1345455 %T Visual analytics tools for analysis of movement data %U http://doi.acm.org/10.1145/1345448.1345455 %V 9 %X With widespread availability of low cost GPS devices, it is becoming possible to record data about the movement of people and objects at a large scale. While these data hide important knowledge for the optimization of location and mobility oriented infrastructures and services, by themselves they lack the necessary semantic embedding which would make fully automatic algorithmic analysis possible. At the same time, making the semantic link is easy for humans who however cannot deal well with massive amounts of data. In this paper, we argue that by using the right visual analytics tools for the analysis of massive collections of movement data, it is possible to effectively support human analysts in understanding movement behaviors and mobility patterns. We suggest a framework for analysis combining interactive visual displays, which are essential for supporting human perception, cognition, and reasoning, with database operations and computational methods, which are necessary for handling large amounts of data. We demonstrate the synergistic use of these techniques in case studies of two real datasets.

@article{Andrienko:2007:VAT:1345448.1345455, abstract = {With widespread availability of low cost GPS devices, it is becoming possible to record data about the movement of people and objects at a large scale. While these data hide important knowledge for the optimization of location and mobility oriented infrastructures and services, by themselves they lack the necessary semantic embedding which would make fully automatic algorithmic analysis possible. At the same time, making the semantic link is easy for humans who however cannot deal well with massive amounts of data. In this paper, we argue that by using the right visual analytics tools for the analysis of massive collections of movement data, it is possible to effectively support human analysts in understanding movement behaviors and mobility patterns. We suggest a framework for analysis combining interactive visual displays, which are essential for supporting human perception, cognition, and reasoning, with database operations and computational methods, which are necessary for handling large amounts of data. We demonstrate the synergistic use of these techniques in case studies of two real datasets.}, acmid = {1345455}, added-at = {2011-06-17T01:44:19.000+0200}, address = {New York, NY, USA}, author = {Andrienko, Gennady and Andrienko, Natalia and Wrobel, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2a7fdc65907df13212f2100d38ae20c6a/ahmedjawwad4u}, description = {Visual analytics tools for analysis of movement data}, doi = {10.1145/1345448.1345455}, interhash = {9d6e50e9a9670c1e11927b4ea99fdc6c}, intrahash = {a7fdc65907df13212f2100d38ae20c6a}, issn = {1931-0145}, issue = {2}, journal = {SIGKDD Explor. Newsl.}, keywords = {biological-sequence-analysis}, month = {December}, numpages = {9}, pages = {38--46}, publisher = {ACM}, timestamp = {2011-06-17T01:45:14.000+0200}, title = {Visual analytics tools for analysis of movement data}, url = {http://doi.acm.org/10.1145/1345448.1345455}, volume = 9, year = 2007 }