Incorporating spatial interaction patterns in classifying and understanding urban land use

International Journal of Geographical Information Science

Volumn 30, Issue 2, 2016, Pages 334-350

  Liu, Xi ^a,b   Kang, Chaogui ^c   Gong, Li ^a   Liu, Yu ^a  

^a PEKING UNIVERSITY   (China)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c WUHAN UNIVERSITY   (China)

Author keywords

classification; social sensing; spatial interaction; Urban land use

Indexed keywords

CLASSIFICATION; DATA SET; GIS; LAND USE; SPATIAL ANALYSIS; SPATIAL DATA; URBAN AREA;

CHINA; SHANGHAI;

EID: 84948064862     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2015.1086923     Document Type: Article

References (37)

1. M.Batty,, 2013. The new science of cities. Cambridge, MA: MIT Press.
2. M.Boarnet, and R.Crane,, 2001. The influence of land use on travel behavior: specification and estimation strategies. Transportation Research Part A: Policy and Practice, 35 (9), 823–845.
3. L.Breiman,, 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:10.1023/A:1010933404324
4. D.Brockmann,, L.Hufnagel,, and T.Geisel,, 2006. The scaling laws of human travel. Nature, 439 (7075), 462–465. doi:10.1038/nature04292
5. F.Calabrese,, et al., 2011. Estimating origin-destination flows using mobile phone location data. IEEEPervasive Computing, 10, 36–44. doi:10.1109/MPRV.2011.41
6. F.Calabrese,, J.Reades,, and C.Ratti,, 2010. Eigenplaces: segmenting space through digital signatures. Pervasive Computing, IEEE, 9 (1), 78–84. doi:10.1109/MPRV.2009.62
7. A.P.Dempster,, N.M.Laird,, and D.B.Rubin,, 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39 (1), 1–38.
8. C.B.Do, and S.Batzoglou,, 2008. What is the expectation maximization algorithm? Nature Biotechnology, 26 (8), 897–899. doi:10.1038/nbt1406
9. V.Frias-Martinez,, et al., 2012. Characterizing urban landscapes using geolocated tweets. In: Proceedings of 2012 international conference social computing (SocialCom), 3–6 September, Amsterdam, 239–248.
10. M.Friedrich,, et al., 2010. Generating origin-destination matrices from mobile phone trajectories. Transportation Research Record: Journal of the Transportation Research Board, 2196 (1), 93–101. doi:10.3141/2196-10
11. M.C.González,, C.A.Hidalgo,, and A.-L.Barabási,, 2008. Understanding individual human mobility patterns. Nature, 453 (7196), 779–782. doi:10.1038/nature06958
12. X.Liu,, et al., 2015a. Revealing travel patterns and city structure with taxi trip data. Journal of Transport Geography, 43, 78–90. doi:10.1016/j.jtrangeo.2015.01.016
13. Y.Liu,, et al., 2015b. Social sensing: a new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105 (3), 512–530. doi:10.1080/00045608.2015.1018773
14. Y.Liu,, et al., 2014. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE, 9 (1), e86026. doi:10.1371/journal.pone.0086026
15. Y.Liu,, et al., 2012. Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106 (1), 73–87. doi:10.1016/j.landurbplan.2012.02.012
16. Y.Long, and J.-C.Thill,, in press. Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing. Computers, Environment and Urban Systems.doi:10.1016/j.compenvurbsys.2015.02.005
17. H.Miller,, 2004. Tobler’s first law and spatial analysis. Annals of the Association of American Geographers, 94 (2), 284–289. doi:10.1111/j.1467-8306.2004.09402005.x
18. G.W.Milligan, and M.C.Cooper,, 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50 (2), 159–179. doi:10.1007/BF02294245
19. C.Parent,, et al., 2013. Semantic trajectories modeling and analysis. ACM Computing Surveys, 45 (4), 42. doi:10.1145/2501654.2501656
20. F.Pedregosa,, et al., 2011. Scikit-learn: machine learning in python. The Journal of Machine Learning Research, 12, 2825–2830.
21. T.Pei,, et al., 2014. A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28 (9), 1988–2007. doi:10.1080/13658816.2014.913794
22. C.Ratti,, 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. J.Reades,, F.Calabrese,, and C.Ratti,, 2009. Eigenplaces: analysing cities using the space-time structure of the mobile phone network. Environment and Planning B: Planning and Design, 36 (5), 824–836. doi:10.1068/b34133t
24. J.-P.Rodrigue,, C.Comtois,, and B.Slack,, 2013. The geography of transport systems. New York: Routledge.
25. L.Shi,, et al., 2015. Human mobility patterns in different communities: a mobile phone data-based social network approach. Annals of GIS, 21 (1), 15–26. doi:10.1080/19475683.2014.992372
26. C.Song,, et al., 2010. Limits of predictability in human mobility. Science, 327 (5968), 1018–1021. doi:10.1126/science.1177170
27. V.Soto, and E.Frías-Martínez,, 2011a. Automated land use identification using cell-phone records. In: Proceedings of the 3rd ACM internationalworkshop on MobiArch, HotPlanet ‘11, 28 June, Bethesda, MD, 17–22.
28. V.Soto, and E.Frias-Martinez,, 2011b. Robust land use characterization of urban landscapes using cell phone data. In: Proceedings of the 1st workshop on pervasive urban applications, in conjunction with 9th international conference onpervasive computing, 12–15 June, San Francisco, CA, 1–8.
29. C.Thiemann,, et al., 2010. The structure of borders in a small world. PLoS ONE, 5 (11), e15422. doi:10.1371/journal.pone.0015422
30. J.L.Toole,, et al., 2012. Inferring land use from mobile phone activity. In: Proceedings of the ACM SIGKDD international workshop on urbancomputing, 12 August, Beijing, 1–8.
31. P.Wang,, et al., 2012. Understanding road usage patterns in urban areas. Scientific Reports, 2, 1001.
32. L.Wu,, et al., 2014. Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS ONE, 9 (5), e97010. doi:10.1371/journal.pone.0097010
33. J.Yuan,, Y.Zheng,, and X.Xie, 2012. Discovering regions of different functions in a city using human mobility and POIs. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, 12–16 August, Beijing, 186–194.
34. Y.Yue,, et al., 2014. Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies. Travel Behaviour and Society, 1 (2), 69–78. doi:10.1016/j.tbs.2013.12.002
35. Y.Zheng,, et al., 2011. Urban computing with taxicabs. In: Proceedings of the 13th internationalconference on ubiquitous computing, 17 September, Beijing, 89–98.
36. C.Zhong,, et al., 2014. Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science, 28 (11), 2178–2199. doi:10.1080/13658816.2014.914521
37. Y.Zhou,, et al., 2015. Functionally critical locations in an urban transportation network: identification and space–time analysis using taxi trajectories. Computers, Environment and Urban Systems, 52, 34–47. doi:10.1016/j.compenvurbsys.2015.03.001