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As a part of Network science, individual human mobility is an emergent field which is dedicated to extracting patterns that govern human movements. Understanding human mobility has many applications in diverse areas, including spread of diseases,〔Colizza, V., Barrat, A., Barthélémy, M., Valleron, A.-J. & Vespignani, A. Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PLoS Medicine 4, 95–110 (2007)〕〔Hufnagel, L., Brockmann, D. & Geisel, T. Forecast and control of epidemics in a globalized world. Proc. Natl Acad. Sci. USA 101, 15124–15129 (2004)〕 mobile viruses,〔Epidemic spreading in scale-free networks R Pastor-Satorras, A Vespignani - Physical review letters, 2001〕 city planning,〔Horner, M. W. & O'Kelly, M. E. S Embedding economies of scale concepts for hub networks design. J. Transp. Geogr. 9, 255–265 (2001)〕〔Inferring land use from mobile phone activity JL Toole, M Ulm, MC González, D Bauer - Proceedings of the ACM SIGKDD international …, 2012〕〔Rozenfeld, H. D. et al. Laws of population growth. Proc. Natl Acad. Sci. USA 105, 18702–18707 (2008)〕 traffic engineering〔Understanding road usage patterns in urban areas P Wang, T Hunter, AM Bayen, K Schechtner… - Scientific reports, 2012〕〔Krings, G., Calabrese, F., Ratti, C. & Blondel, V. D. Urban gravity: A model for inter-city telecommunication flows. J. Stat. Mech.-Theor. Exp. L07003 (2009)〕 and financial market forecasting.〔Gabaix, X., Gopikrishnan, P., Plerou, V. & Stanley, H. E. A theory of power-law distributions in financial market fluctuations. Nature 423, 267–270 (2003)〕 == Data == In recent years, there has been a surge in large data sets available on human movements. These data sets are usually obtained from cell phone or GPS data, with varying degrees of accuracy. For example, cell phone data is usually recorded whenever a call or a text message has been made or received by the user, and contains the location of the tower that the phone has connected to as well as the time stamp.〔 In urban areas, user and the telecommunication tower might be only a few hundred meters away from each other, while in rural areas this distance might well be in region of a few kilometers. Therefore, there is varying degree of accuracy when it comes to locating a person using cell phone data. These datasets are anonymized by the phone companies so as to hide and protect the identity of actual users. As example of its usage, researchers 〔 used the trajectory of 100,000 cell phone users within a period of six months, while in much larger scale 〔 trajectories of three million cell phone users were analyzed. GPS data, on the other hand, is usually much more accurate. Because of privacy concerns, these type of data are usually much harder to acquire, and scientific work based on these data sets are sparse. Researchers have been able to extract very detailed information about the people whose data are made available to public. This has sparked a great amount of concern about privacy issues. As an example of liabilities that might happen, New York City released 173 million individual taxi trips. City officials used a very weak cryptography algorithm to anonymize the license number and medallion number, which is an alphanumeric code assigned to each taxi cab.〔http://gawker.com/the-public-nyc-taxicab-database-that-accidentally-track-1646724546〕 This made it possible for hackers to completely de-anonymize the dataset, and even some where able to extract detailed information about specific passengers and celebrities, including their origin and destination and how much they tipped.〔〔http://www.theguardian.com/technology/2014/jun/27/new-york-taxi-details-anonymised-data-researchers-warn〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Individual mobility」の詳細全文を読む スポンサード リンク
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