Document Open Access Logo

An Analytical Framework for Understanding Urban Functionality from Human Activities (Short Paper)

Authors Chaogui Kang , Yu Liu



PDF
Thumbnail PDF

File

LIPIcs.GISCIENCE.2018.38.pdf
  • Filesize: 0.57 MB
  • 8 pages

Document Identifiers

Author Details

Chaogui Kang
  • School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, China
Yu Liu
  • Institute of Remote Sensing and Geographical Information Systems, Peking University, 5 Yiheyuan Road, Beijing, China

Cite AsGet BibTex

Chaogui Kang and Yu Liu. An Analytical Framework for Understanding Urban Functionality from Human Activities (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 38:1-38:8, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.38

Abstract

The intertwined relationship between urban functionality and human activity has been widely recognized and quantified with the assistance of big geospatial data. In specific, urban land uses as an important facet of urban structure can be identified from spatiotemporal patterns of aggregate human activities. In this article, we propose a space, time and activity cuboid based analytical framework for clustering urban spaces into different categories of urban functionality based on the variation of activity intensity (T-fiber), mixture (A-fiber) and interaction (I- and O-fiber). The ability of the proposed framework is empirically evaluated by three case studies.

Subject Classification

ACM Subject Classification
  • General and reference → General literature
Keywords
  • Urban functionality
  • Human activity
  • STA cuboid
  • Spatiotemporal distribution
  • Clustering

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Michael Batty. Urban modelling. Cambridge University Press Cambridge, 1976. Google Scholar
  2. Luís MA Bettencourt. The origins of scaling in cities. Science, 340(6139):1438-1441, 2013. Google Scholar
  3. Susan L Handy, Marlon G Boarnet, Reid Ewing, and Richard E Killingsworth. How the built environment affects physical activity. American Journal of Preventive Medicine, 23(2):64-73, 2002. Google Scholar
  4. Chaogui Kang, Stanislav Sobolevsky, Yu Liu, and Carlo Ratti. Exploring human movements in singapore: a comparative analysis based on mobile phone and taxicab usages. In Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, pages 1-7. ACM, 2013. Google Scholar
  5. T Warren Liao. Clustering of time series data-a survey. Pattern Recognition, 38(11):1857-1874, 2005. Google Scholar
  6. Xi Liu, Chaogui Kang, Li Gong, and Yu Liu. Incorporating spatial interaction patterns in classifying and understanding urban land use. International Journal of Geographical Information Science, 30(2):334-350, 2016. Google Scholar
  7. Yu Liu, Xi Liu, Song Gao, Li Gong, Chaogui Kang, Ye Zhi, Guanghua Chi, and Li Shi. Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3):512-530, 2015. Google Scholar
  8. Yu Liu, Fahui Wang, Yu Xiao, and Song Gao. Urban land uses and traffic `source-sink areas': Evidence from gps-enabled taxi data in shanghai. Landscape and Urban Planning, 106(1):73-87, 2012. Google Scholar
  9. Gang Pan, Guande Qi, Zhaohui Wu, Daqing Zhang, and Shijian Li. Land-use classification using taxi gps traces. IEEE Transactions on Intelligent Transportation Systems, 14(1):113-123, 2013. Google Scholar
  10. Jonathan Reades, Francesco Calabrese, and Carlo Ratti. Eigenplaces: analysing cities using the space-time structure of the mobile phone network. Environment and Planning B: Planning and Design, 36(5):824-836, 2009. Google Scholar
  11. Jameson L Toole, Michael Ulm, Marta C González, and Dietmar Bauer. Inferring land use from mobile phone activity. In Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, pages 1-8. ACM, 2012. Google Scholar
  12. Mao Ye, Krzysztof Janowicz, Christoph Mülligann, and Wang-Chien Lee. What you are is when you are: The temporal dimension of feature types in location-based social networks. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 102-111, New York, NY, USA, 2011. ACM. Google Scholar
  13. Mengxue Yue, Chaogui Kang, Clio Andris, Yu Liu, Kun Qin, and Qingxiang Meng. Understanding the interplay between bus, metro and cab ridership dynamics in shenzhen, china. Transactions in GIS, 2018. URL: http://dx.doi.org/10.1111/tgis.12340.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail