Individual mobility

Last updated

Individual human mobility is the study that describes how individual humans move within a network or system. [1] The concept has been studied in a number of fields originating in the study of demographics. Understanding human mobility has many applications in diverse areas, including spread of diseases, [2] [3] mobile viruses, [4] city planning, [5] [6] [7] traffic engineering, [8] [9] financial market forecasting, [10] and nowcasting of economic well-being. [11] [12]

Contents

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. [13] 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 [13] used the trajectory of 100,000 cell phone users within a period of six months, while in much larger scale [14] trajectories of three million cell phone users were analyzed. GPS data are usually much more accurate even though they usually are, because of privacy concerns, much harder to acquire. Massive amounts of GPS data describing human mobility are produced, for example, by on-board GPS devices on private vehicles. [15] [16] The GPS device automatically turns on when the vehicle starts, and the sequence of GPS points the device produces every few seconds forms a detailed mobility trajectory of the vehicle. Some recent scientific studies compared the mobility patterns emerged from mobile phone data with those emerged from GPS data. [15] [16] [17]

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. [18] This made it possible for hackers to completely de-anonymize the dataset, and even some were able to extract detailed information about specific passengers and celebrities, including their origin and destination and how much they tipped. [18] [19]

Characteristics

At the large scale, when the behaviour is modelled over a period of relatively long duration (e.g. more than one day), human mobility can be described by three major components:

Brockmann, [20] by analysing banknotes, found that the probability of travel distance follows a scale-free random walk known as Lévy flight of form where . This was later confirmed by two studies that used cell phone data [13] and GPS data to track users. [15] The implication of this model is that, as opposed to other more traditional forms of random walks such as brownian motion, human trips tend to be of mostly short distances with a few long distance ones. In brownian motion, the distribution of trip distances are govern by a bell-shaped curve, which means that the next trip is of a roughly predictable size, the average, where in Lévy flight it might be an order of magnitude larger than the average.

Some people are inherently inclined to travel longer distances than the average, and the same is true for people with lesser urge for movement. Radius of gyration is used to capture just that and it indicates the characteristic distance travelled by a person during a time period t. [13] Each user, within his radius of gyration , will choose his trip distance according to .

The third component models the fact that humans tend to visit some locations more often than what would have happened under a random scenario. For example, home or workplace or favorite restaurants are visited much more than many other places in a user's radius of gyration. It has been discovered that where , which indicates a sublinear growth in different number of places visited by an individual . These three measures capture the fact that most trips happen between a limited number of places, with less frequent travels to places outside of an individual's radius of gyration.

Predictability

Although the human mobility is modeled as a random process, it is surprisingly predictable. By measuring the entropy of each person's movement, it has been shown [14] that there is a 93% potential predictability. This means that although there is a great variance in type of users and the distances that each of them travel, the overall characteristic of them is highly predictable. Implication of it is that in principle, it is possible to accurately model the processes that are dependent on human mobility patterns, such as disease or mobile virus spreading patterns. [21] [22] [23]

On individual scale, daily human mobility can be explained by only 17 Network motifs. Each individual, shows one of these motifs characteristically, over a period of several months. This opens up the possibility to reproduce daily individual mobility using a tractable analytical model [24]

Applications

Infectious diseases spread across the globe usually because of long-distance travels of carriers of the disease. These long-distance travels are made using air transportation systems and it has been shown that "network topology, traffic structure, and individual mobility patterns are all essential for accurate predictions of disease spreading". [21] On a smaller spatial scale the regularity of human movement patterns and its temporal structure should be taken into account in models of infectious disease spread. [25] Cellphone viruses that are transmitted via bluetooth are greatly dependent on the human interaction and movements. With more people using similar operating systems for their cellphones, it's becoming much easier to have a virus epidemic. [22]

In Transportation Planning, leveraging the characteristics of human movement, such as tendency to travel short distances with few but regular bursts of long-distance trips, novel improvements have been made to Trip distribution models, specifically to Gravity model of migration [26]

See also

Related Research Articles

<span class="mw-page-title-main">Scale-free network</span> Network whose degree distribution follows a power law

A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as

<span class="mw-page-title-main">Albert-László Barabási</span> Hungarian-American physicist (born 1967)

Albert-László Barabási is a Romanian-born Hungarian-American physicist, best known for his discoveries in network science and network medicine.

<span class="mw-page-title-main">Complex network</span> Network with non-trivial topological features

In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, climate networks and social networks.

<span class="mw-page-title-main">Assortativity</span> Tendency for similar nodes to be connected

Assortativity, or assortative mixing, is a preference for a network's nodes to attach to others that are similar in some way. Though the specific measure of similarity may vary, network theorists often examine assortativity in terms of a node's degree. The addition of this characteristic to network models more closely approximates the behaviors of many real world networks.

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.

<span class="mw-page-title-main">Alessandro Vespignani</span> Italian-American physicist (born 1965)

Alessandro Vespignani is an Italian-American physicist, best known for his work on complex networks, and particularly for work on the applications of network theory to the mathematical modeling of infectious disease, applications of computational epidemiology, and for studies of the topological properties of the Internet. He is currently the Sternberg Family Distinguished University Professor of Physics, Computer Science and Health Sciences at Northeastern University, where he is the director of the Network Science Institute.

<span class="mw-page-title-main">Dirk Brockmann</span> German system biologist (born 1969)

Dirk Brockmann is a German physicist and Professor at the Institute for Biology at Humboldt University of Berlin and the Robert Koch Institute, Berlin. Brockmann is known for his work in complex systems, complex networks, computational epidemiology, human mobility and anomalous diffusion.

Human dynamics refer to a branch of complex systems research in statistical physics such as the movement of crowds and queues and other systems of complex human interactions including statistical modelling of human networks, including interactions over communications networks.

<span class="mw-page-title-main">Hierarchical network model</span>

Hierarchical network models are iterative algorithms for creating networks which are able to reproduce the unique properties of the scale-free topology and the high clustering of the nodes at the same time. These characteristics are widely observed in nature, from biology to language to some social networks.

The radiation law is way of modeling human mobility and it gives better empirical predictions than the gravity model of migration which is widely used in this subject.

In mathematical modeling of social networks, link-centric preferential attachment is a node's propensity to re-establish links to nodes it has previously been in contact with in time-varying networks. This preferential attachment model relies on nodes keeping memory of previous neighbors up to the current time.

<span class="mw-page-title-main">Disparity filter algorithm of weighted network</span>

Disparity filter is a network reduction algorithm to extract the backbone structure of undirected weighted network. Many real world networks such as citation networks, food web, airport networks display heavy tailed statistical distribution of nodes' weight and strength. Disparity filter can sufficiently reduce the network without destroying the multi-scale nature of the network. The algorithm is developed by M. Angeles Serrano, Marian Boguna and Alessandro Vespignani.

<span class="mw-page-title-main">Temporal network</span> Network whose links change over time

A temporal network, also known as a time-varying network, is a network whose links are active only at certain points in time. Each link carries information on when it is active, along with other possible characteristics such as a weight. Time-varying networks are of particular relevance to spreading processes, like the spread of information and disease, since each link is a contact opportunity and the time ordering of contacts is included.

<span class="mw-page-title-main">Bianconi–Barabási model</span>

The Bianconi–Barabási model is a model in network science that explains the growth of complex evolving networks. This model can explain that nodes with different characteristics acquire links at different rates. It predicts that a node's growth depends on its fitness and can calculate the degree distribution. The Bianconi–Barabási model is named after its inventors Ginestra Bianconi and Albert-László Barabási. This model is a variant of the Barabási–Albert model. The model can be mapped to a Bose gas and this mapping can predict a topological phase transition between a "rich-get-richer" phase and a "winner-takes-all" phase.

<span class="mw-page-title-main">Hub (network science)</span> Node with a number of links that greatly exceeds the average

In network science, a hub is a node with a number of links that greatly exceeds the average. Emergence of hubs is a consequence of a scale-free property of networks. While hubs cannot be observed in a random network, they are expected to emerge in scale-free networks. The uprise of hubs in scale-free networks is associated with power-law distribution. Hubs have a significant impact on the network topology. Hubs can be found in many real networks, such as the brain or the Internet.

<span class="mw-page-title-main">Mediation-driven attachment model</span>

In the scale-free network theory, a mediation-driven attachment (MDA) model appears to embody a preferential attachment rule tacitly rather than explicitly. According to MDA rule, a new node first picks a node from the existing network at random and connect itself not with that but with one of the neighbors also picked at random.

Unicity is a risk metric for measuring the re-identifiability of high-dimensional anonymous data. First introduced in 2013, unicity is measured by the number of points p needed to uniquely identify an individual in a data set. The fewer points needed, the more unique the traces are and the easier they would be to re-identify using outside information.

Local differential privacy (LDP) is a model of differential privacy with the added requirement that if an adversary has access to the personal responses of an individual in the database, that adversary will still be unable to learn much of the user's personal data. This is contrasted with global differential privacy, a model of differential privacy that incorporates a central aggregator with access to the raw data.

<span class="mw-page-title-main">Caroline Buckee</span> Epidemiologist and Associate Professor

Caroline O'Flaherty Buckee is an epidemiologist. She is an associate professor of Epidemiology and is the associate director of the Center for Communicable Disease Dynamics, both at the Harvard T.H. Chan School of Public Health. Buckee is known for her work in digital epidemiology, where mathematical models track mobile and satellite data to understand the transmission of infectious diseases through populations in an effort to understand the spatial dynamics of disease transmission. Her work examines the implications of conducting surveillance and implementing control programs as a way to understand and predict what will happen when dealing with outbreaks of infectious diseases like malaria and COVID-2019.

Roberta Sinatra is an Italian scientist and associate professor at the IT University of Copenhagen. She is known for her work in network science and conducts research on quantifying success in science.

References

  1. ↑ Keyfitz, Nathan (1973). "Individual Mobility in a Stationary Population". Population Studies. 27 (July 1, 1973): 335–352. doi:10.2307/2173401. JSTOR   2173401. PMID   22085100.
  2. ↑ Colizza, V.; Barrat, A.; Barthélémy, M.; Valleron, A.-J.; Vespignani, A. (2007). "Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions". PLOS Medicine. 4 (1): 95–110. arXiv: q-bio/0701038 . Bibcode:2007q.bio.....1038C. doi: 10.1371/journal.pmed.0040013 . PMC   1779816 . PMID   17253899.
  3. ↑ Hufnagel, L.; Brockmann, D.; Geisel, T. (2004). "Forecast and control of epidemics in a globalized world". Proc. Natl. Acad. Sci. USA. 101 (42): 15124–15129. arXiv: cond-mat/0410766 . Bibcode:2004PNAS..10115124H. doi: 10.1073/pnas.0308344101 . PMC   524041 . PMID   15477600.
  4. ↑ Pastor-Satorras, Romualdo; Vespignani, Alessandro (2001-04-02). "Epidemic Spreading in Scale-Free Networks". Physical Review Letters. 86 (14): 3200–3203. arXiv: cond-mat/0010317 . Bibcode:2001PhRvL..86.3200P. doi:10.1103/physrevlett.86.3200. ISSN   0031-9007. PMID   11290142. S2CID   16298768.
  5. ↑ Horner, M. W.; O'Kelly, M. E. S (2001). "Embedding economies of scale concepts for hub networks design". J. Transp. Geogr. 9 (4): 255–265. doi:10.1016/s0966-6923(01)00019-9.
  6. ↑ Inferring land use from mobile phone activity JL Toole, M Ulm, MC González, D Bauer - Proceedings of the ACM SIGKDD international …, 2012
  7. ↑ Rozenfeld, H. D.; et al. (2008). "Laws of population growth". Proc. Natl. Acad. Sci. USA. 105 (48): 18702–18707. arXiv: 0808.2202 . Bibcode:2008PNAS..10518702R. doi: 10.1073/pnas.0807435105 . PMC   2596244 . PMID   19033186.
  8. ↑ Wang, Pu; Hunter, Timothy; Bayen, Alexandre M.; Schechtner, Katja; González, Marta C. (2012). "Understanding Road Usage Patterns in Urban Areas". Scientific Reports. Springer Science and Business Media LLC. 2 (1): 1001. arXiv: 1212.5327 . Bibcode:2012NatSR...2E1001W. doi: 10.1038/srep01001 . ISSN   2045-2322. PMC   3526957 . PMID   23259045.
  9. ↑ Krings, Gautier; Calabrese, Francesco; Ratti, Carlo; Blondel, Vincent D (2009-07-14). "Urban gravity: a model for inter-city telecommunication flows". Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing. 2009 (7): L07003. arXiv: 0905.0692 . doi:10.1088/1742-5468/2009/07/l07003. ISSN   1742-5468. S2CID   1445486.
  10. ↑ Gabaix, X.; Gopikrishnan, P.; Plerou, V.; Stanley, H. E. (2003). "A theory of power-law distributions in financial market fluctuations". Nature. 423 (6937): 267–270. Bibcode:2003Natur.423..267G. doi:10.1038/nature01624. PMID   12748636. S2CID   1263236.
  11. ↑ Stefano Marchetti; et al. (Jun 2015). "Small Area Model-Based Estimators Using Big Data Sources". Journal of Official Statistics. 31 (2): 263–281. doi: 10.1515/jos-2015-0017 . hdl: 11568/754495 .
  12. ↑ L. Pappalardo et al., Using Big Data to study the link between human mobility and socio-economic development, Proceedings of the 2015 IEEE International conference on Big Data, Santa Clara, CA, USA, 2015.
  13. 1 2 3 4 González, Marta C.; Hidalgo, César A.; Barabási, Albert-László (2008). "Understanding individual human mobility patterns". Nature. 453 (7196): 779–782. arXiv: 0806.1256 . Bibcode:2008Natur.453..779G. doi:10.1038/nature06958. ISSN   0028-0836. PMID   18528393. S2CID   4419468.
  14. 1 2 Limits of predictability in human mobility. C Song, Z Qu, N Blumm, AL Barabási - Science, 2010
  15. 1 2 3 Luca Pappalardo; et al. (29 January 2013). "Understanding the patterns of car travel". European Physical Journal ST. 215 (1): 61–73. Bibcode:2013EPJST.215...61P. doi:10.1140/epjst/e2013-01715-5. S2CID   120805149.
  16. 1 2 Luca Pappalardo; et al. (8 September 2015). "Returners and Explorers dichotomy in Human Mobility". Nature Communications. 6: 8166. Bibcode:2015NatCo...6.8166P. doi:10.1038/ncomms9166. PMC   4569739 . PMID   26349016.
  17. ↑ L. Pappalardo et al., Comparing general mobility and mobility by car, BRICS Countries Congress (BRICS-CCI) and 11th Brazilian Congress (CBIC) on Computational Intelligence, 2013.
  18. 1 2 "Public NYC Taxicab Database Lets You See How Celebrities Tip". Archived from the original on 2014-11-18. Retrieved 2014-11-15.
  19. ↑ Hern, Alex (2014-06-27). "New York taxi details can be extracted from anonymised data, researchers say". The Guardian.
  20. ↑ Brockmann, D.; Hufnagel, L.; Geisel, T. (2006). "The scaling laws of human travel". Nature. 439 (7075): 462–465. arXiv: cond-mat/0605511 . Bibcode:2006Natur.439..462B. doi:10.1038/nature04292. ISSN   0028-0836. PMID   16437114. S2CID   4330122.
  21. 1 2 Nicolaides, Christos; Cueto-Felgueroso, Luis; González, Marta C.; Juanes, Ruben (2012-07-19). Vespignani, Alessandro (ed.). "A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network". PLOS ONE. Public Library of Science (PLoS). 7 (7): e40961. Bibcode:2012PLoSO...740961N. doi: 10.1371/journal.pone.0040961 . ISSN   1932-6203. PMC   3400590 . PMID   22829902.
  22. 1 2 Wang, P.; Gonzalez, M. C.; Hidalgo, C. A.; Barabasi, A.-L. (2009-04-01). "Understanding the Spreading Patterns of Mobile Phone Viruses". Science. 324 (5930): 1071–1076. arXiv: 0906.4567 . Bibcode:2009Sci...324.1071W. doi:10.1126/science.1167053. ISSN   0036-8075. PMID   19342553. S2CID   11081201.
  23. ↑ Colizza, Vittoria; Barrat, Alain; Barthélemy, Marc; Vespignani, Alessandro (2007-11-21). "Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study". BMC Medicine. 5 (1): 34. arXiv: 0801.2261 . doi: 10.1186/1741-7015-5-34 . ISSN   1741-7015. PMC   2213648 . PMID   18031574.
  24. ↑ Schneider, Christian M.; Belik, Vitaly; Couronné, Thomas; Smoreda, Zbigniew; González, Marta C. (2013-07-06). "Unravelling daily human mobility motifs". Journal of the Royal Society Interface. The Royal Society. 10 (84): 20130246. doi:10.1098/rsif.2013.0246. ISSN   1742-5689. PMC   3673164 . PMID   23658117.
  25. ↑ Belik, Vitaly; Geisel, Theo; Brockmann, Dirk (2011-08-08). "Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases". Physical Review X. 1 (1): 011001. arXiv: 1103.6224 . Bibcode:2011PhRvX...1a1001B. doi: 10.1103/physrevx.1.011001 . ISSN   2160-3308.
  26. ↑ Simini, Filippo; González, Marta C.; Maritan, Amos; Barabási, Albert-László (2012-02-26). "A universal model for mobility and migration patterns". Nature. 484 (7392): 96–100. arXiv: 1111.0586 . Bibcode:2012Natur.484...96S. doi:10.1038/nature10856. ISSN   0028-0836. PMID   22367540. S2CID   4431030.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.