2012
Journal article  Restricted

Discovering the geographical borders of human mobility

Rinzivillo S., Mainardi S., Pezzoni F., Coscia M., Pedreschi D., Giannotti F.

Artificial Intelligence  Community discovery  Mobility data mining 

The availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.

Source: KI. Künstliche Intelligenz (Oldenbourg) 26 (2012): 253–260. doi:10.1007/s13218-012-0181-8

Publisher: Böttcher-IT-Verl., Bremen , Germania


Metrics



Back to previous page
BibTeX entry
@article{oai:it.cnr:prodotti:219028,
	title = {Discovering the geographical borders of human mobility},
	author = {Rinzivillo S. and Mainardi S. and Pezzoni F. and Coscia M. and Pedreschi D. and Giannotti F.},
	publisher = {Böttcher-IT-Verl., Bremen , Germania},
	doi = {10.1007/s13218-012-0181-8},
	journal = {KI. Künstliche Intelligenz (Oldenbourg)},
	volume = {26},
	pages = {253–260},
	year = {2012}
}

DATA SIM
Data Science for Simulating the Era of Electric Vehicles


OpenAIRE