Skip to main content

Measuring Space-Time Prism Similarity Through Temporal Profile Curves

  • Conference paper
  • First Online:
Book cover Geospatial Data in a Changing World

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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 15–38

    Chapter  Google Scholar 

  • Batty M (2010) Space, scale, and scaling in entropy maximizing. Geogr Anal 42:395–421

    Article  Google Scholar 

  • Briggs D (2005) The role of GIS: coping with space (and time) in air pollution exposure assessment. J Toxicol Environ Health Part A 68:1243–1261

    Article  Google Scholar 

  • Burns LD (1979) Transportation, temporal and spatial components of accessibility. Lexington Books, Lexington

    Google Scholar 

  • Dodge S, Laube P, Weibel R (2012) Movement similarity assessment using symbolic representation of trajectories. Int J Geogr Inf Sci 26:1563–1588

    Article  Google Scholar 

  • Espeter M, Raubal M (2009) Location-based decision support for user groups. J Location Based Serv 3:165–187

    Article  Google Scholar 

  • Giorgino T (2009) Computing and visualizing dynamic time warping alignments in R: the dtw package. J Stat Softw 31:1–24

    Article  Google Scholar 

  • Gudmundsson J, Laube P, Wolle T (2012) Computational movement analysis. In: Kresse W, Danko DM (eds) Springer handbook of geographic information. Springer, Berlin, pp 423–438

    Google Scholar 

  • Hägerstrand T (1970) What about people in regional science? Pap Reg Sci Assoc 24:1–12

    Article  Google Scholar 

  • Janowicz K, Raubal M, Kuhn W (2011) The semantics of similarity in geographic information retrieval. J Spat Inf Sci 2:29–57

    Google Scholar 

  • 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 127–154

    Google Scholar 

  • Kuijpers B, Othman W (2009) Modeling uncertainty of moving objects on road networks via space-time prisms. Int J Geogr Inf Sci 23:1095–1117

    Article  Google Scholar 

  • Long JA, Nelson TA (2012) Time geography and wildlife home range delineation. J Wildl Manage 76:407–413

    Article  Google Scholar 

  • Long JA, Nelson TA (2013) A review of quantitative methods for movement data. Int J Geogr Inf Sci 27:292–318

    Article  Google Scholar 

  • Miller HJ (1991) Modeling accessibility using space-time prism concepts within geographical information systems. Int J Geogr Inf Syst 5:287–301

    Article  Google Scholar 

  • Miller HJ (2005) A measurement theory for time geography. Geogr Anal 37:17–45

    Article  Google Scholar 

  • Miller HJ, Bridwell SA (2009) A field-based theory for time geography. Ann Assoc Am Geogr 99:49–75

    Article  Google Scholar 

  • 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 267–296

    Google Scholar 

  • Okabe A, Sugihara K (2012) Spatial analysis along networks: statistical and computational methods. Wiley

    Google Scholar 

  • O’Sullivan D, Unwin D (2010) Geographic information analysis, 2nd edn. Wiley, Hoboken

    Book  Google Scholar 

  • 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 111–131

    Google Scholar 

  • Pred A (1977) The choreography of existence: comments on Hagerstrand’s time-geography and its usefulness. Econ Geogr 53:207–221

    Article  Google Scholar 

  • Raubal M, Miller HJ, Bridwell S (2004) User-centered time geography for location-based services. Geografiska Annaler B 86(4):245–265

    Article  Google Scholar 

  • Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26:43–49

    Article  Google Scholar 

  • Sinha G, Mark DM (2005) Measuring similarity between geospatial lifelines in studies of environmental health. J Geogr Syst 7:115–136

    Article  Google Scholar 

  • Song Y, Miller HJ (2014) Simulating visit probability distributions within planar space-time prisms. Int J Geogr Inf Sci 28:104–125

    Article  Google Scholar 

  • Winter S, Yin ZC (2010a) The elements of probabilistic time geography. Geoinformatica 15:417–434

    Article  Google Scholar 

  • Winter S, Yin ZC (2010b) Directed movements in probabilistic time geography. Int J Geogr Inf Sci 24:1349–1365

    Article  Google Scholar 

  • 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 354-367

    Google Scholar 

  • Yuan Y, Raubal M (2014) Measuring similarity of mobile phone user trajectories: a spatio-temporal edit distance method. Int J Geogr Inf Sci 28:496–520

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Harvey J. Miller .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Miller, H.J., Raubal, M., Jaegal, Y. (2016). Measuring Space-Time Prism Similarity Through Temporal Profile Curves. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_4

Download citation

Publish with us

Policies and ethics