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[1]Public Health Matters BlogJohn. Snow: A Legacy of Disease Detectives[EB/OL].(2017-3-14)[2021-6-24]. https://blogs.cdc.gov/publichealthmatters/2017/03/a-legacy-of-disease-detectives/.

[2]CHRISTAKIS N A,FOWLER J H. The spread of obesity in a large social network over 32 years[J]. New England journal of medicine,2007,357(4): 370-379.

[3]CHRISTAKIS N A,FOWLER J H. The collective dynamics of smoking in a large social network[J]. New England journal of medicine,2008,358(21): 2249-2258.

[4]NICKERSON D W. Is voting contagious? Evidence from two field experiments[J]. American political Science review,2008,102(1): 49-57.

[5]BRESLAU N,SCHULTZ L R,JOHNSON E O,et al. Smoking and the risk of suicidal behavior: a prospective study of a community sample[J]. Archives of general psychiatry,2005,62(3): 328-334.

[6]ARAL S,NICOLAIDES C. Exercise contagion in a global social network[J]. Nature communications,2017,8(1): 1-8.

[7]FAIRBAIRN C E,SAYETTE M A,AALEN O O,et al. Alcohol and emotional contagion: an examination of the spreading of smiles in male and female drinking groups[J]. Clinical psychological science,2015,3(5): 686-701.

[8]KOSSINETS G,WATTS D J. Empirical analysis of an evolving social network[J]. Science,2006,311(5757): 88-90.

[9]DODDS P S,MUHAMAD R,WATTS D J. An experimental study of search in global social networks[J]. Science,2003,301(5634): 827-829.

[10]SHNEIDERMAN B. A national initiative for social participation[J]. Science,2009,323(5920): 1426-1427.

[11]PARSHANI R,BULDYREV S V,HAVLIN S. Critical effect of dependency groups on the function of networks[C]// Proceedings of the National Academy of Sciences,2011,108(3): 1007-1010.

[12]WINERMAN,LEA. Social networking: crisis communication[J]. Nature,2009,457(7228):376-378.

[13]WANG X,DONG J S,CHIN C Y,et al. Semantic space: an infrastructure for smart spaces[J]. Pervasive computing IEEE,2004,3(3):32-39.

[14]HARTER A,HOPPER A,STEGGLES P,et al. The anatomy of a context-aware application[J]. Wireless networks,2002,8(2): 187-197.

[15]PHILIPOSE M,FISHKIN K P,PERKOWITZ M,et al. Inferring activities from interactions with objects[J]. IEEE pervasive computing,2004,3(4): 50-57.

[16]READES J,CALABRESE F,SEVTSUK A,et al. Cellular census: explorations in urban data collection[J]. IEEE pervasive computing,2007,6(3): 30-38.

[17]PENTLAND A,EAGLE N,LAZER D. Inferring social network structure using mobile phone data[J]. Proceedings of the National Academy of Sciences(PNAS),2009,106(36): 15274-15278.

[18]CAMPBELL A T,EISENMAN S B,LANE N D,et al. The rise of people-centric sensing[J]. IEEE internet computing,2008,12(4): 12-21.

[19]GONZALEZ M C,HIDALGO C A,BARABASI A L. Understanding individual human mobility patterns[J]. Nature,2008,453(7196): 779-782.

[20]YUAN N J,ZHENG Y,XIE X,et al. Discovering urban functional zones using latent activity trajectories[J]. IEEE transactions on knowledge and data engineering,2014,27(3): 712-725.

[21]READES J,CALABRESE F,SEVTSUK A,et al. Cellular census: explorations in urban data collection[J]. IEEE pervasive computing,2007,6(3): 30-38.

[22]LAZER D,PENTLAND A,ADAMIC L,et al. Social science. computational social science[J]. Science,2009,323(5915): 721-723.

[23]INSEL T R. Digital phenotyping: technology for a new science of behavior[J]. Jama,2017,318(13): 1215-1216.

[24]KOSINSKI M,STILLWELL D,GRAEPEL T. Private traits and attributes are predictable from digital records of human behavior[J]. Proceedings of the national academy of sciences,2013,110(15): 5802-5805.

[25]YOUYOU W,KOSINSKI M,STILLWELL D. Computer-based personality judgments are more accurate than those made by humans[J]. Proceedings of the national academy of sciences,2015,112(4): 1036-1040.

[26]SONG C,QU Z,BLUMM N,et al. Limits of predictability in human mobility[J]. Science,2010,327(5968): 1018-1021.

[27]ALESSANDRETTI L,ASLAK U,LEHMANN S. The scales of human mobility[J]. Nature,2020,587(7834): 402-407.

[28]WANG P. Bridging human mobility and urban growth[J]. Nature computational science,2021,1(12): 778-779.

[29]ZHENG Y,LIU F,HSIEH H P. U-air: When urban air quality inference meets big data[C]//Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. 2013: 1436-1444.

[30]ZHENG Y,LIU T,WANG Y,et al. Diagnosing New York city 's noises with ubiquitous data[C]//Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2014: 715-725.

[31]ALESSANDRETTI L. What human mobility data tell us about COVID-19 spread[J]. Nature Reviews Physics,2022,4(1): 12-13.

[32]GOZZI N,TIZZONI M,CHINAZZI M,et al. Estimating the effect of social inequalities on the mitigation of COVID-19 across communities in Santiago de Chile[J]. Nature communications,2021,12(1): 1-9.

[33]SCHWALBE M C,COHEN G L,ROSS L D. The objectivity illusion and voter polarization in the 2016 presidential election[J]. Proceedings of the national academy of sciences,2020,117(35): 21218-21229.

[34]CHANG H,LI L,HUANG J,et al. Tracking traffic congestion and accidents using social media data: A case study of Shanghai[J]. Accident analysis & prevention,2022,169: 106618.

[35]SAKAKI T,OKAZAKI M,MATSUO Y. Earthquake shakes twitter users: real-time event detection by social sensors[C]//Proceedings of the 19th International Conference on World Wide Web. 2010: 851-860.

[36]WEN F,ZHANG Z,HE T,et al. AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove[J]. Nature communications,2021,12(1): 1-13.

[37]JUANG P,OKI H,WANG Y,et al. Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet[C]//Proceedings of the 10th International Conference on Architectural Support for Programming Languages and Operating Systems. 2002: 96-107.

[38]BROCKMANN D,HUFNAGEL L,GEISEL T. The scaling laws of human travel[J]. Nature,2006,439(7075): 462-465.

[39]XU F,LI Y,JIN D,et al. Emergence of urban growth patterns from human mobility behavior[J]. Nature computational science,2021,1(12): 791-800.

[40]IACUS S M,SANTAMARIA C,SERMI F,et al. Human mobility and COVID-19 initial dynamics[J]. Nonlinear dynamics,2020,101(3): 1901-1919.

[41]WU Y,MOORING T A,LINZ M. Policy and weather influences on mobility during the early US COVID-19 pandemic[J]. Proceedings of the national academy of sciences,2021,118(22): e2018185118.

[42]MCCALLUM I,KYBA C C M,BAYAS J C L,et al. Estimating global economic well-being with unlit settlements[J]. Nature communications,2022,13(1): 1-8.

[43]YEH C,PEREZ A,DRISCOLL A,et al. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa[J]. Nature communications,2020,11(1): 1-11.

[44]FAN L,LI T,YUAN Y,et al. In-home daily-life captioning using radio signals[C]//European Conference on Computer Vision.2020: 105-123.

[45]WU D,ZHANG D,XU C,et al. Device-free WiFi human sensing: from pattern-based to model-based approaches[J]. IEEE communications magazine,2017,55(10),91-97.

[46]DUTTA P,AOKI P M,KUMAR N,et al. Common sense: participatory urban sensing using a network of handheld air quality monitors[C]//Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems. 2009: 349-350.

[47]LIU L,LIU W,ZHENG Y,et al. Third-eye: A mobilephone-enabled crowdsensing system for air quality monitoring[J]. Proceedings of the ACM on interactive,mobile,wearable and ubiquitous technologies,2018,2(1): 1-26.

[48]MAISONNEUVE N,STEVENS M,NIESSEN M E,et al. NoiseTube: Measuring and mapping noise pollution with mobile phones[M]//Information technologies in environmental engineering. Berlin: Springer,2009: 215-228.

[49]RANA R,CHOU C T,BULUSU N,et al. Ear-Phone: a context-aware noise mapping using smart phones[J]. Pervasive and mobile computing,2015,17: 1-22.

[50]RA M R,LIU B,LA PORTA T F,et al. Medusa: a programming framework for crowdsensing applications[C]//Proceedings of the 10th International Conference on Mobile Systems,Applications,and Services. 2012: 337-350.

[51]SIMOENS P,XIAO Y,PILLAI P,et al. Scalable crowd-sourcing of video from mobile devices[C]//Proceeding of the 11th Annual International Conference on Mobile Systems,Applications,and Services. 2013: 139-152.

[52]MINSON S E,BROOKS B A,GLENNIE C L,et al. Crowdsourced earthquake early warning[J]. Science advances,2015,1(3): e1500036.

[53]YI F,YU Z,ZHUANG F,et al. Neural network based continuous conditional random field for fine-grained crime prediction[C]//IJCAI. 2019: 4157-4163.

[54]HE T,BAO J,LI R,et al. Detecting vehicle illegal parking events using sharing bikes' trajectories[C]//KDD. 2018: 340-349.

[55]GEBRU T,KRAUSE J,WANG Y,et al. Using deep learning and Google street view to estimate the demographic makeup of neighborhoods across the United States[J]. Proceedings of the national academy of sciences,2017,114(50): 13108-13113.

[56]MUELLER H,GROEGER A,HERSH J,et al. Monitoring war destruction from space using machine learning[J]. Proceedings of the national academy of sciences,2021,118(23): e2025400118.

[57]AXELROD R. The evolution of cooperation: revised edition[M]. New York:Basic Books,2006.

[58]SCHELLING T C. Models of segregation[J]. The American economic review,1969,59(2): 488-493.

[59]VINYALS O,BABUSCHKIN I,CZARNECKI W M,et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning[J]. Nature,2019,575(7782): 350-354.

[60]UCINET SOFTWARE. https://sites.google.com/site/ucinetsoftware/home.

[61]BASTIAN M,HEYMANN S,JACOMY M. Gephi: an open source software for exploring and manipulating networks[C]//Proceedings of the International AAAI Conference on Web and Social Media. 2009,3(1): 361-362.

[62]OTASEK D,MORRIS J H,BOU ÇAS J,et al. Cytoscape automation:empowering workflowbased network analysis [J] . Genome biology,2019,20(1):1-15.

[63]NOOK E C,HULL T D,NOCK M K,et al. Linguistic measures of psychological distance track symptom levels and treatment outcomes in a large set of psychotherapy transcripts[J]. Proceedings of the national academy of sciences,2022,119(13): e2114737119.

[64]LEE K,AGRAWAL A,CHOUDHARY A. Real-time disease surveillance using twitter data: demonstration on flu and cancer[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013: 1474-1477. MbYADFuMtowUARVgMZHAxA5yAtSn7Cx64XAzRFYpBfwFnV22mrFhJpSsKYetTg9k

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