License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.38
URN: urn:nbn:de:0030-drops-93668
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9366/
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Kang, Chaogui ; Liu, Yu

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

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LIPIcs-GISCIENCE-2018-38.pdf (0.6 MB)

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.

BibTeX - Entry

@InProceedings{kang_et_al:LIPIcs:2018:9366,
  author =	{Chaogui Kang and Yu Liu},
  title =	{{An Analytical Framework for Understanding Urban Functionality from Human Activities (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{38:1--38:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9366},
  URN =		{urn:nbn:de:0030-drops-93668},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.38},
  annote =	{Keywords: Urban functionality, Human activity, STA cuboid, Spatiotemporal distribution, Clustering}
}

Keywords: Urban functionality, Human activity, STA cuboid, Spatiotemporal distribution, Clustering
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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