Detecting the dynamics of urban structure through spatial network analysis

International Journal of Geographical Information Science

Volumn 28, Issue 11, 2014, Pages 2178-2199

  Zhong, Chen ^a   Arisona, Stefan Müller ^a,b   Huang, Xianfeng ^c   Batty, Michael ^d   Schmitt, Gerhard ^a  

^a ETH ZURICH   (Switzerland)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c WUHAN UNIVERSITY   (China)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

complex networks; polycentric spatial structure; smart card data; spatial analysis; urban movements

Indexed keywords

NEIGHBORHOOD; NETWORK ANALYSIS; SPATIAL ANALYSIS; URBAN AREA; URBAN POPULATION;

SINGAPORE [SOUTHEAST ASIA];

EID: 84912523187     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2014.914521     Document Type: Article

References (36)

1. Anas, A., Arnott, R., and Small, K.A., 1998. Urban spatial structure. Journal of Economic Literature, 36 (3), 1426–1464.
2. Barthélemy, M. and Flammini, A., 2008. Modeling urban street patterns. Physical Review Letters, 100 (13), 138702. doi:10.1103/PhysRevLett.100.138702.
3. Batty, M., 1978. Speculations on an information theoretic approach to spatial representation. In: I. Masser and P.B. Brown, eds. Spatial representation and spatial interaction. Leiden: Martinus Nijhoff Social Sciences Division, 115–147.
4. Berry, B.J., 1967. Grouping and regionalizing: an approach to the problem using multivariate analysis. In: W.L. Garrison and D.F. Marble, eds. Quantitative geography part 1: economic and cultural topics. Chicago: Northwestern University, 219–251.
5. Brin, S. and Page, L., 1998. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30 (1–7), 107–117. doi:10.1016/S0169-7552(98)00110-X.
6. Cardillo, A., et al., 2006. Structural properties of planar graphs of urban street patterns. Physical Review E, 73 (6), 066107. doi:10.1103/PhysRevE.73.066107.
7. Chakirov, A. and Erath, A., 2012. Activity identification and primary location modelling based on smart card payment data for public transport. Zurich: Eidgenössische Technische Hochschule Zürich, IVT, Institute for Transport Planning and Systems.
8. Cheong, C.C. and Toh, R., 2010. Household interview surveys from 1997 to 2008 – a decade of changing travel behaviours [online]. Singapore Land Transport Authority. Available from: http://www.ltaacademy.gov.sg/LA-01Journeys_May10_files/J10May-p52Choy&Toh_HIS1997-2008.pdf [Accessed 25 April 2014].
9. Doursat, R., 2005. Topology and dynamics of complex networks. In: CS 790R Seminar modeling & simulation. Department of Computer Science & Engineering University of Nevada, Reno, Spring.
10. Guimerà, R., et al., 2005. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proceedings of the National Academy of Sciences, 102, 7794–7799.
11. Hillier, B. and Iida, S., 2005. Network and psychological effects in urban movement. In: A. Cohn and D. Mark, eds. Spatial information theory. Berlin: Springer, 475–490.
12. Jiang, S., Joseph Ferreira, J., and Gonzalez, M.C., 2012. Discovering urban spatial-temporal structure from human activity patterns. In: Proceedings of the ACM SIGKDD international workshop on urban computing, New York. New York: ACM, 95–102.
13. Lancichinetti, A. and Fortunato, S., 2009. Community detection algorithms: a comparative analysis. Physical Review E, 80 (5), 056117. doi:10.1103/PhysRevE.80.056117.
14. Liang, L., et al., 2009. Understanding individual and collective mobility patterns from smart card records: a case study in Shenzhen. In: Proceedings of the 12th international IEEE conference on Intelligent Transportation Systems, 3–7 October, St. Louis, MO, 1–6.
15. Munizaga, M.A. and Palma, C., 2012. Estimation of a disaggregate multimodal public transport origin–destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C: Emerging Technologies, 24, 9–18. doi:10.1016/j.trc.2012.01.007.
16. Newman, M.E., 2003. The structure and function of complex networks. SIAM Review, 45 (2), 167–256. doi:10.1137/S003614450342480.
17. Noulas, A. and Añel, J.A., 2012. A tale of many cities: universal patterns in human urban mobility. PloS One, 7 (5), e37027. doi:10.1371/journal.pone.0037027.
18. Nystuen, J.D. and Dacey, M.F., 1961. A graph theory interpretation of nodal regions. Papers of the Regional Science Association, 7 (1), 29–42.
19. Ordóñez Medina, A. and Erath, A., 2013. Estimating dynamic workplace capacities using public transport smart card data and a household travel survey. Paper presented at Transportation Research Board (TRB) 92nd Annual Meeting, 2344, 20–30.
20. Páez, A. and Scott, D.M., 2005. Spatial statistics for urban analysis: a review of techniques with examples. GeoJournal, 61 (1), 53–67. doi:10.1007/s10708-005-0877-5.
21. Park, J.Y., Kim, D.J., and Lim, Y., 2008. Use of smart card data to define public transit use in Seoul, South Korea. Transportation Research Record: Journal of the Transportation Research Board, 2063 (–1), 3–9. doi:10.3141/2063-01.
22. Ratti, C., et al., 2010. Redrawing the map of Great Britain from a network of human interactions. PloS One, 5 (12), e14248. doi:10.1371/journal.pone.0014248.
23. Rinzivillo, S., et al., 2012. Discovering the geographical borders of human mobility. KI-Künstliche Intelligenz, 26 (3), 253–260. doi:10.1007/s13218-012-0181-8.
24. Rodrigue, J.-P., Comtois, C., and Slack, B., 2009. The geography of transport systems. London: Routledge.
25. Rosvall, M. and Bergstrom, C.T., 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105 (4), 1118–1123. doi:10.1073/pnas.0706851105.
26. Rosvall, M., Bergstrom, C.T., and Rapallo, F., 2010. Mapping change in large networks. PloS One, 5 (1), e8694. doi:10.1371/journal.pone.0008694.
27. Roth, C., et al., 2011. Structure of urban movements: polycentric activity and entangled hierarchical flows. PloS One, 6 (1), e15923. doi:10.1371/journal.pone.0015923.
28. Simini, F., et al., 2012. A universal model for mobility and migration patterns. Nature, 484 (7392), 96–100. doi:10.1038/nature10856
29. Slater, P.B., 1976. A hierarchical regionalization of Japanese prefectures using 1972 interprefectural migration flows. Regional Studies, 10 (1), 123–132. doi:10.1080/09595237600185121.
30. Soh, H., et al., 2010. Weighted complex network analysis of travel routes on the Singapore public transportation system. Physica A: Statistical Mechanics and its Applications, 389 (24), 5852–5863. doi:10.1016/j.physa.2010.08.015.
31. Song, C., et al., 2010. Modelling the scaling properties of human mobility. Nature Physics, 6 (10), 818–823. doi:10.1038/nphys1760
32. Sun, L., et al., 2013. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences, 110 (34), 13774–13779. doi:10.1073/pnas.1306440110
33. Szell, M., et al., 2012. Understanding mobility in a social petri dish. Scientific Reports, 2, 1–6. doi:10.1038/srep00457
34. Thiemann, C., et al., 2010. The structure of borders in a small world. PloS One, 5 (11), e15422.
35. Toole, J.L., et al., 2012. Inferring land use from mobile phone activity. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing. Beijing: ACM, 1–8.
36. Zhong, C., et al., 2013. Identifying spatial structure of urban functional centers using travel survey data: a case study of Singapore. In: Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place, Orlando FL. New York: ACM, 28–33.