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Measuring Space-Time Prism Similarity Through Temporal Profile Curves

  • Harvey J. MillerEmail author
  • Martin Raubal
  • Young Jaegal
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Space-time paths and prisms based on the time geographic framework model actual (empirical or simulated) and potential mobility, respectively. There are well-established methods for quantitatively measuring similarity between space-time paths, including dynamic time warping and edit-distance functions. However, there are no corresponding measures for comparing space-time prisms. Analogous to path similarity, space-time prism similarity measures can support comparison of individual accessibility, prism clustering methods and retrieving prisms similar to a reference prism from a mobility database. In this paper, we introduce a method to calculate space-time prism similarity through temporal sweeping. The sweeping method generates temporal profile curves summarizing dynamic prism geometry or semantic content over the time span of the prism’s existence. Given these profile curves, we can apply existing path similarity methods to compare space-time prisms based on a specified geometric or semantic prism. This method can also be scaled to multiple prisms, and can be applied to prisms and paths simultaneously. We discuss the general approach and demonstrate the method for classic planar space-time prisms.

Keywords

Activity space Time geography Similarity Accessibility 

References

  1. Andrienko N, Andienko G, Pelekis N, Spaccapietra S (2008) Basic concepts of movement data. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, Heidelberg, pp 15–38CrossRefGoogle Scholar
  2. Batty M (2010) Space, scale, and scaling in entropy maximizing. Geogr Anal 42:395–421CrossRefGoogle Scholar
  3. Briggs D (2005) The role of GIS: coping with space (and time) in air pollution exposure assessment. J Toxicol Environ Health Part A 68:1243–1261CrossRefGoogle Scholar
  4. Burns LD (1979) Transportation, temporal and spatial components of accessibility. Lexington Books, LexingtonGoogle Scholar
  5. Dodge S, Laube P, Weibel R (2012) Movement similarity assessment using symbolic representation of trajectories. Int J Geogr Inf Sci 26:1563–1588CrossRefGoogle Scholar
  6. Espeter M, Raubal M (2009) Location-based decision support for user groups. J Location Based Serv 3:165–187CrossRefGoogle Scholar
  7. Giorgino T (2009) Computing and visualizing dynamic time warping alignments in R: the dtw package. J Stat Softw 31:1–24CrossRefGoogle Scholar
  8. Gudmundsson J, Laube P, Wolle T (2012) Computational movement analysis. In: Kresse W, Danko DM (eds) Springer handbook of geographic information. Springer, Berlin, pp 423–438Google Scholar
  9. Hägerstrand T (1970) What about people in regional science? Pap Reg Sci Assoc 24:1–12CrossRefGoogle Scholar
  10. Janowicz K, Raubal M, Kuhn W (2011) The semantics of similarity in geographic information retrieval. J Spat Inf Sci 2:29–57Google Scholar
  11. Kobayashi T, Miller HJ (2014) Exploratory visualization of collective mobile objects data using temporal granularity and spatial similarity. In: Cervone G, Lin J, Waters N (eds) Data mining for geoinformatics: methods and applications. Springer, pp 127–154Google Scholar
  12. Kuijpers B, Othman W (2009) Modeling uncertainty of moving objects on road networks via space-time prisms. Int J Geogr Inf Sci 23:1095–1117CrossRefGoogle Scholar
  13. Long JA, Nelson TA (2012) Time geography and wildlife home range delineation. J Wildl Manage 76:407–413CrossRefGoogle Scholar
  14. Long JA, Nelson TA (2013) A review of quantitative methods for movement data. Int J Geogr Inf Sci 27:292–318CrossRefGoogle Scholar
  15. Miller HJ (1991) Modeling accessibility using space-time prism concepts within geographical information systems. Int J Geogr Inf Syst 5:287–301CrossRefGoogle Scholar
  16. Miller HJ (2005) A measurement theory for time geography. Geogr Anal 37:17–45CrossRefGoogle Scholar
  17. Miller HJ, Bridwell SA (2009) A field-based theory for time geography. Ann Assoc Am Geogr 99:49–75CrossRefGoogle Scholar
  18. Nanni M, Kuijpers B, Körner C, May M, Pedreschi D (2008) Spatio-temporal data mining. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, pp 267–296Google Scholar
  19. Okabe A, Sugihara K (2012) Spatial analysis along networks: statistical and computational methods. WileyGoogle Scholar
  20. O’Sullivan D, Unwin D (2010) Geographic information analysis, 2nd edn. Wiley, HobokenCrossRefGoogle Scholar
  21. Pfoser D, Jensen CS (1999) Capturing the uncertainty of moving-object representations. In: Güting RH, Papadias D, Lochovsky F (eds) Advances in spatial databases: 6th international symposium (SSD’99), vol 1651. Springer Lecture Notes in Computer Science, Berlin, pp 111–131Google Scholar
  22. Pred A (1977) The choreography of existence: comments on Hagerstrand’s time-geography and its usefulness. Econ Geogr 53:207–221CrossRefGoogle Scholar
  23. Raubal M, Miller HJ, Bridwell S (2004) User-centered time geography for location-based services. Geografiska Annaler B 86(4):245–265CrossRefGoogle Scholar
  24. Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26:43–49CrossRefGoogle Scholar
  25. Sinha G, Mark DM (2005) Measuring similarity between geospatial lifelines in studies of environmental health. J Geogr Syst 7:115–136CrossRefGoogle Scholar
  26. Song Y, Miller HJ (2014) Simulating visit probability distributions within planar space-time prisms. Int J Geogr Inf Sci 28:104–125CrossRefGoogle Scholar
  27. Winter S, Yin ZC (2010a) The elements of probabilistic time geography. Geoinformatica 15:417–434CrossRefGoogle Scholar
  28. Winter S, Yin ZC (2010b) Directed movements in probabilistic time geography. Int J Geogr Inf Sci 24:1349–1365CrossRefGoogle Scholar
  29. Yuan Y, Raubal M (2012) Extracting dynamic urban mobility patterns from mobile phone data. In: Xiao N, Kwan M-P, Goodchild M, Shekhar S (eds) Geographic information science—seventh international conference, GIScience 2012, Columbus, Ohio, USA, Sept 18–21 2012, Proceedings. Springer, Berlin, pp 354-367Google Scholar
  30. Yuan Y, Raubal M (2014) Measuring similarity of mobile phone user trajectories: a spatio-temporal edit distance method. Int J Geogr Inf Sci 28:496–520CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Harvey J. Miller
    • 1
    Email author
  • Martin Raubal
    • 2
  • Young Jaegal
    • 1
  1. 1.The Ohio State UniversityColumbusUSA
  2. 2.ETH ZurichZurichSwitzerland

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