参考文献

［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. C2wt8JmNBylXTqgtzKxjW73bdVWkFXntShXt7ObRR65fXfGJ5kVQqh/vzcMOqp+s