Urban Computing Leveraging Location-Based Social Network Data: A Survey

被引：67

作者：

Silva, Thiago H. ^{[1
]}

Viana, Aline Carneiro ^{[2
]}

Benevenuto, Fabricio ^{[3
]}

Villas, Leandro ^{[4
]}

Salles, Juliana ^{[5
]}

Loureiro, Antonio ^{[3
]}

Quercia, Daniele ^{[6
]}

机构：

[1] Univ Tecnol Fed Parana, Informat, Av Sete Setembro 3165, BR-80230901 Curitiba, Parana, Brazil

[2] Inria, 1 Rue Honore dEstienne dOrves, F-91120 Palaiseau, France

[3] Univ Fed Minas Gerais, Comp Sci, Predio ICEx Pampulha, Av Antonio Carlos 6627, Belo Horizonte, MG, Brazil

[4] Univ Estadual Campinas, Comp Sci, Av Albert Einstein,1251 Cidade Univ, Campinas, SP, Brazil

[5] Microsoft Res, 14820 NE 36th St,Bldg 99, Redmond, WA 98052 USA

[6] Bell Labs, Broers Bldg 21 JJ,Thomson Ave, Cambridge CB3 0FA, England

来源：

ACM COMPUTING SURVEYS | 2019年 / 52卷 / 01期

关键词：

Urban computing; urban informatics; location-based social networks; big data; urban sensing; city dynamics; urban societies; LARGE-SCALE; HUMAN MOBILITY; SPACE; INFORMATION; TIME; PREDICTABILITY; POLLUTION;

D O I：

10.1145/3301284

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Urban computing is an emerging area of investigation in which researchers study cities using digital data. Location-Based Social Networks (LBSNs) generate one specific type of digital data that offers unprecedented geographic and temporal resolutions. We discuss fundamental concepts of urban computing leveraging LBSN data and present a survey of recent urban computing studies that make use of LBSN data. We also point out the opportunities and challenges that those studies open.

引用

页数：39

共 165 条

[1]

Abel F, 2011, LECT NOTES COMPUT SC, V6644, P375, DOI 10.1007/978-3-642-21064-8_26

[2]

Aggarwal C.C., 2012, Mining Text Data, P1, DOI [DOI 10.1007/978-1-4614-3223-4_4, 10.1007/978-1-4614-3223-4, DOI 10.1007/978-1-4614-3223-4]

[3] Mining urban recurrent congestion evolution patterns from GPS-equipped vehicle mobility data [J].

An, Shi ;

Yang, Haiqiang ;

Wang, Jian ;

Cui, Na ;

Cui, Jianxun .

INFORMATION SCIENCES, 2016, 373 :515-526

[4] Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns [J].

Andrienko, G. ;

Andrienko, N. ;

Bremm, S. ;

Schreck, T. ;

von Landesberger, T. ;

Bak, P. ;

Keim, D. .

COMPUTER GRAPHICS FORUM, 2010, 29 (03) :913-922

[5] Space, time and visual analytics [J].

Andrienko, Gennady ;

Andrienko, Natalia ;

Demsar, Urska ;

Dransch, Doris ;

Dykes, Jason ;

Fabrikant, Sara Irina ;

Jern, Mikael ;

Kraak, Menno-Jan ;

Schumann, Heidrun ;

Tominski, Christian .

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2010, 24 (10) :1577-1600

[6]

Ankerst M, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P49

[7]

[Anonymous], 2011, INT C INF KNOWL MAN, DOI DOI 10.1145/2063576.2063724

[8]

[Anonymous], 2006, Participatory sensing. pages, DOI [10.1109/MIC.2010.12, DOI 10.1109/MIC.2010.12]

[9] User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks [J].

Arcaini, Paolo ;

Bordogna, Gloria ;

Ienco, Dino ;

Sterlacchini, Simone .

INFORMATION SCIENCES, 2016, 340 :122-143

[10] Demographics, Weather and Online Reviews: A Study of Restaurant Recommendations [J].

Bakhshi, Saeideh ;

Kanuparthy, Partha ;

Gilbert, Eric .

WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2014, :443-453

← 1 2 3 4 5 6 7 8 9 10 →