Crowd-sourced cartography: Measuring socio-cognitive distance for urban areas based on crowd's movement

UbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Volumn , Issue , 2012, Pages 935-942

  Wakamiya, Shoko ^a   Lee, Ryong ^b   Sumiya, Kazutoshi ^a  

^a UNIVERSITY OF HYOGO   (Japan)

^b NATIONAL INSTITUTE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY   (Japan)

Author keywords

Cartography; Location based social network; Socio cognitive Map; Urban analytics

Indexed keywords

DYNAMIC CHANGES; HUMAN MOBILITY; LOCATION BASED; LOCATION-BASED SOCIAL NETWORK SITES; LOCATION-BASED SOCIAL NETWORKS; RAPID URBANIZATIONS; SOCIO-COGNITIVE; URBAN ANALYTICS;

MAPPING; MAPS; SOCIAL NETWORKING (ONLINE); UBIQUITOUS COMPUTING;

URBAN GROWTH;

EID: 84879483473     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. A. M. Andrew. Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters, 9(5):216-219, 1979.
2. F. Aurenhammer. Voronoi diagrams-a survey of a fundamental geometric data structure. ACM Comput. Surv., 23(3):345-405, 1991.
3. S. Byers and A. Raftery. Nearest-neighbour clutter removal for estimating features in spatial point processes. Journal of the American Statistical Association, 93:577-584, 1998.
4. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of the Second Intl. Conference on Knowledge Discovery and Data Mining, pp. 226-231, 1996.
5. F. Grabler, M. Agrawala, R. W. Sumner, and M. Pauly. Automatic generation of tourist maps. ACM Trans. Graph., 27(3):100:1-100:11, Aug. 2008.
6. J. Kruskal and M. Wish. Multidimensional scaling. In Sage University Papers on Quantitative Applications in the Social Sciences, pp. 07-011, 1978.
7. T. Kurashima, T. Tezuka, and K. Tanaka. Blog Map of Experiences: Extracting and Geographically Mapping Visitor Experiences from Urban Blogs. In Proc.of the 13th Intl. Conference on Web Information System Engineering, pp. 496-503, 2005.
8. R. Lee, S. Wakamiya, and K. Sumiya. Discovery of unusual regional social activities using geo-tagged microblogs. World Wide Web Special Issue on Mobile Services on the Web, 14(4):321-349, 2011.
9. R. Lee, S. Wakamiya, and K. Sumiya. Urban area characterization based on crowd behavioral lifelogs over Twitter. Personal and Ubiquitous Computing, pp. 1-16, 2012.
10. A. Mislove, S. Lehmann, Y. Y. Ahn, J. P. Onnela, and J. N. Rosenquist. Understanding the Demographics of Twitter Users. In Proc. of the 5th Intl. AAAI Conference on Weblogs and Social Media, pp. 133-140, 2011.
11. Pulse of the Nation: U.S. Mood Throughout the Day inferred from Twitter: http://www.ccs.neu.edu/home/amislove/twittermood/
12. H. Shu, C. Qi, and G. Edwards. Computing geographical reality with animated map language. In Proc of the 16th Intl. Conference on Artificial Reality and Telexistence-Workshops, pp. 52-56, 2006.
13. S. Wakamiya, R. Lee, and K. Sumiya. Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from Twitter. In Proc. of the 3rd ACM SIGSPATIAL Intl. Workshop on Location-Based Social Networks, pp. 10:1-10:9, 2011.
14. J. Yuan, Y. Zheng, and X. Xie. Discovering Regions of Different Functions in a City Using Human Mobility and POIs. In Proc. of the 18th ACM SIGKDD Conference. on Knowledge Discovery and Data Mining, pp. 133-140, 2012. Japan