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Unified underpinning of human mobility in the real world and cyberspace. (English) Zbl 1457.82163

Summary: Human movements in the real world and in cyberspace affect not only dynamical processes such as epidemic spreading and information diffusion but also social and economical activities such as urban planning and personalized recommendation in online shopping. Despite recent efforts in characterizing and modeling human behaviors in both the real and cyber worlds, the fundamental dynamics underlying human mobility have not been well understood. We develop a minimal, memory-based random walk model in limited space for reproducing, with a single parameter, the key statistical behaviors characterizing human movements in both cases. The model is validated using relatively big data from mobile phone and online commerce, suggesting memory-based random walk dynamics as the unified underpinning for human mobility, regardless of whether it occurs in the real world or in cyberspace.

MSC:

82B41 Random walks, random surfaces, lattice animals, etc. in equilibrium statistical mechanics
76A30 Traffic and pedestrian flow models

References:

[1] Lima A, De Domenico M, Pejovic V and Musolesi M 2015 Disease containment strategies based on mobility and information dissemination Sci. Rep.5 10650 · doi:10.1038/srep10650
[2] Frías-Martínez E, Williamson G and Frías-Martínez V 2011 An agent-based model of epidemic spread using human mobility and social network information Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Int. Conf. on Social Computing (SocialCom) 57-64
[3] Belik V, Geisel T and Brockmann D 2011 Natural human mobility patterns and spatial spread of infectious diseases Phys. Rev. X 1 011001 · doi:10.1103/PhysRevX.1.011001
[4] Tizzoni M et al 2014 On the use of human mobility proxies for modeling epidemics Plos ONE10 e1003716 · doi:10.1371/journal.pcbi.1003716
[5] Wesolowski A et al 2012 C. O. quantifying the impact of human mobility on malaria Science338 267-70 · doi:10.1126/science.1223467
[6] Deville P et al 2014 Dynamic population mapping using mobile phone data Proc. Natl. Acad. Sci. USA111 15888-93 · doi:10.1073/pnas.1408439111
[7] Jiang S et al 2013 A review of urban computing for mobile phone traces cuurent methods, challenges and opportunities Proc. of the 2nd ACM SIGKDD Int. Workshop on Urban Computing, UrbComp ’13 · doi:10.1145/2505821.2505828
[8] Berlingerio M et al 2013 AllAboard: a system for exploring urban mobility and optimizing public transport using cellphone data Machine Learning and Knowledge Discovery in Databases Lecture Notes in Computer Science8190 663-6 · doi:10.1007/978-3-642-40994-3_50
[9] Brockmann D, Hufnagel L and Geisel T 2006 The caling laws of human travel Nature439 462-5 · doi:10.1038/nature04292
[10] González M C, Hidalgo C A and Barabási A L 2008 Understanding individual human mobility patterns Nature453 779-82 · doi:10.1038/nature06958
[11] Song C, Qu Z, Blumm N and Barabási A L 2010 Limits of predictability in human mobility Science327 1018-21 · Zbl 1226.91058 · doi:10.1126/science.1177170
[12] Zhao Z Z, Huang Z G, Huang L, Liu H and Lai Y C 2014 Scaling and correlation of human movements in cyberspace and physical space Phys. Rev. E 90 050802(R) · doi:10.1103/PhysRevE.90.050802
[13] Blondel V D, Decuyper A and Krings G 2015 A survey of results on mobile phone datasets analysis EPJ Data Science4 1-55 · doi:10.1140/epjds/s13688-015-0046-0
[14] Song C, Koren T, Wang P and Barabási A L 2010 Modelling the scaling properties of human mobility Nat. Phys.6 818-23 · doi:10.1038/nphys1760
[15] Zipf G K 1946 The P 1 P 2 / D hypothesis: on the intercity movement of persons Am. Sociol. Rev.11 677-86 · doi:10.2307/2087063
[16] Simini F, González M C, Maritan A and Barabási A L 2012 A universal model for mobility and migration patterns Nature484 96-100 · doi:10.1038/nature10856
[17] S̆ćepanović S et al 2015 Mobile phone call data as a reginal socio-economic proxy indicator Plos ONE10 e0124160 · doi:10.1371/journal.pone.0124160
[18] Eagle N, Macy M and Claxton R 2010 Network diversity and economic development Science328 1029-31 · Zbl 1226.91053 · doi:10.1126/science.1186605
[19] Yan X Y, Han X P, Wang B H and Zhou T 2013 Diversity of individual mobility patterns and emergence of aggregated scaling laws Sci. Rep.3 02678 · doi:10.1038/srep02678
[20] Yan X Y et al 2014 Universal predictability of mobility patterns in cities J. R. Soc. Interface11 0834 · doi:10.1098/rsif.2014.0834
[21] Hou L, Pan X, Guo Q and Liu J G 2014 Memory effect of the online user preference Sci. Rep.4 06560 · doi:10.1038/srep06560
[22] Hu Y, Zhang J, Huan D and Di Z 2011 Toward a general understanding of the scaling laws in human and animal mobility Europhys. Lett.96 38006 · doi:10.1209/0295-5075/96/38006
[23] Saramäki J et al 2013 Persistence of social signatures in human communication Proc. Natl Acad. Sci. USA111 942-47 · doi:10.1073/pnas.1308540110
[24] Yamasaki K et al 2005 Scaling and memory in volatility return intervals in financial markets Proc. Natl Acad. Sci. USA102 9424-28 · doi:10.1073/pnas.0502613102
[25] Cattuto C, Loreto V and Servedio V D P 2006 A Yule-Simon process with memory Europhys. Lett.76 208-14 · doi:10.1209/epl/i2006-10263-9
[26] Cattuto C 2006 Semiotic dynamics in online social communities Eur. Phys. J46 33-7 · doi:10.1140/epjcd/s2006-03-004-4
[27] Goh K I and Barabási A L 2008 Burstiness and memory in complex systems Europhys. Lett.81 48002 · doi:10.1209/0295-5075/81/48002
[28] Szell M et al 2012 Understanding mobility in a social petri dish Sci. Rep.2 00457 · doi:10.1038/srep00457
[29] Simon H A 1955 On a class of skew distribution functions Biometrika42 425-40 · Zbl 0066.11201 · doi:10.1093/biomet/42.3-4.425
[30] Newman M 2010 Networks: An Introduction · Zbl 1195.94003 · doi:10.1093/acprof:oso/9780199206650.001.0001
[31] Albert R and Barabási A L 2002 Statistical mechanics of complex networks Rev. Mod. Phys.47 74 · Zbl 1205.82086 · doi:10.1103/RevModPhys.74.47
[32] Barabási A L and Albert R 1999 Emergence of scaling in random networks Science286 509-12 · Zbl 1226.05223 · doi:10.1126/science.286.5439.509
[33] Levandoski J J, Sarwat M and Eldawy A 2012 LARS: a location-aware recommender system Proc. of 28th Int. Conf. on Data Engineering 450-61
[34] Zhao Z D et al 2012 Emergence of scaling in human-interest dynamics Sci. Rep.3 3472 · doi:10.1038/srep03472
[35] Calabrese F et al 2010 The geography of taste: analyzing cell-phone mobility and social events Pervasive Computing Lecture Notes in Computer Science6030 22-37 · doi:10.1007/978-3-642-12654-3_2
[36] Smoreda Z and Licoppe C 2000 Gender-specific use of the domestic telephone Soc. Psychol. Q.63 238-52 · doi:10.2307/2695871
[37] Kovanen L, Kaski K, Kertész J and Saramäki J 2013 Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences Proc. Natl Acad. Sci. USA.110 18070-5 · doi:10.1073/pnas.1307941110
[38] Blumenstock J E and Eagle N 2012 Divided we call: disparities in access and use of mobile phones in rwanda Inform. Tech & Int. Develop.
[39] Ben-Naim E and Krapivsky P L 2012 Popularity-driven networking Europhys. Lett.97 48003 · doi:10.1209/0295-5075/97/48003
[40] Pappalardo L et al 2015 Returners and explorers dichotomy in human mobility Nat. Commun.6 8166 · doi:10.1038/ncomms9166
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