Skip to main content

Advertisement

SpringerLink
Log in

Visualizing spatial interaction characteristics with direction-based pattern maps

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

In the era of big data, large amounts of detailed spatial interaction data are readily available due to the increasing pervasiveness of location-aware devices and techniques. Such data are often applied in the research of spatial structures, land use characteristics, and human activity regularities. However, visualizing such data on maps is a great challenge. Existing approaches either encounter overlapping and intersection problems, which may cause information loss or distortion, or are unable to present interaction characteristics. Consequently, the functional and positional features of places and human mobility trends are not fully demonstrated. We propose direction-based pattern maps, a new visualization method to display the spatial interaction pattern of every place by aggregating spatial interaction data in cardinal directions. This approach can well represent local interaction characteristics and is applicable to different spatial scales. Owing to regular hexagonal tessellations, it can avoid cluttering problems and maintain a geographical context. A case study using Beijing taxi trip data is conducted to validate the usefulness of our approach.

Graphical Abstract

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abel GJ, Sander N (2014) Quantifying global international migration flows. Science 343(6178):1520–1522

    Article  Google Scholar 

  • Andrienko N, Andrienko G (2011) Spatial generalization and aggregation of massive movement data. IEEE Trans Visual Comput Graph 17(2):205–219

    Article  Google Scholar 

  • Andrienko G, Andrienko N, Fuchs G, Wood J (2017) Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Trans Visual Comput Graphics 23(9):2120–2136

    Article  Google Scholar 

  • Batten DF, Boyce DE (1987) Spatial interaction, transportation, and interregional commodity flow models. In: Nijkamp P (ed) Handbook of regional and urban economics, vol 1. Elsevier, Amsterdam, pp 357–406

    Chapter  Google Scholar 

  • Birkin M, Clarke G, Clarke M (2010) Refining and operationalizing entropy-maximizing models for business applications. Geogr Anal 42(4):422–445

    Article  Google Scholar 

  • Brewer CA, Pickle L (2002) Evaluation of methods for classifying epidemiological data on choropleth maps in series. Ann Assoc Am Geogr 92(4):662–681

    Article  Google Scholar 

  • Brewer CA, Hatchard GW, Harrower MA (2003) ColorBrewer in print: a catalog of color schemes for maps. Cartogr Geogr Inf Sci 30(1):5–32

    Article  Google Scholar 

  • Buchin K, Speckmann B, Verbeek K (2011) Flow map layout via spiral trees. IEEE Trans Visual Comput Graphics 17(12):2536–2544

    Article  Google Scholar 

  • Cano RG, Buchin K, Castermans THA, Pieterse A, Sonke WM, Speckmann B (2015) Mosaic drawings and cartograms. Comput Graph Forum 34(3):361–370

    Article  Google Scholar 

  • Carr DB, Olsen AR, White D (1992) Hexagon mosaic maps for display of univariate and bivariate geographical data. Cartogr Geogr Inf Syst 19(4):228–236

    Google Scholar 

  • Chen S, Chen S, Wang Z, Liang J, Yuan X, Cao N, Wu Y (2016). D-Map: visual analysis of ego-centric information diffusion patterns in social media. In: Proceedings of IEEE conference on visual analytics science and technology (VAST’16), pp 41–50

  • Cui W, Zhou H, Qu H, Wong PC, Li X (2008) Geometry-based edge clustering for graph visualization. IEEE Trans Vis Comput Graph 14(6):1277–1284

    Article  Google Scholar 

  • Dacey MF (1965) The geometry of central place theory. Geografiska annaler. Ser B Hum Geogr 47(2):111–124

    Article  Google Scholar 

  • Dent BD, Torguson JS, Hodler TW (1999) Cartography: thematic map design, 5th edn. WCB/McGraw-Hill, Boston

    Google Scholar 

  • Drake DAR, Mandrak NE (2010) Least-cost transportation networks predict spatial interaction of invasion vectors. Ecol Appl 20(8):2286–2299

    Article  Google Scholar 

  • Evans IS (1977) The selection of class intervals. Trans Inst Br Geogr 2(1):98–124

    Article  Google Scholar 

  • Fotheringham AS (1981) Spatial structure and distance-decay parameters. Ann Assoc Am Geogr 71(3):425–436

    Google Scholar 

  • Fotheringham AS, O’Kelly ME (1989) Spatial interaction models: formulations and applications. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Goodchild MF, Yuan M, Cova TJ (2007) Towards a general theory of geographic representation in GIS. Int J Geogr Inf Sci 21(3):239–260

    Article  Google Scholar 

  • Guo D (2007) Visual analytics of spatial interaction patterns for pandemic decision support. Int J Geogr Inf Sci 21(8):859–877

    Article  Google Scholar 

  • Guo D, Zhu X, Jin H, Gao P, Andris C (2012) Discovering spatial patterns in origin-destination mobility data. Trans GIS 16(3):411–429

    Article  Google Scholar 

  • Jenks GF (1967) The data model concept in statistical mapping. Int Yearb Cartogr 7:186–190

    Google Scholar 

  • Jenny B, Stephen DM, Muehlenhaus I, Marston BE, Sharma R, Zhang E, Jenny H (2016) Design principles for origin-destination flow maps. Cartogr Geogr Inf Sci 45(1):62–75

    Article  Google Scholar 

  • Jiang X, Zheng C, Tian Y, Liang R (2015) Large-scale taxi O/D visual analytics for understanding metropolitan human movement patterns. J Vis 18(2):185–200

    Article  Google Scholar 

  • Kamgar-Parsi B, Sander WA (1989) Quantization error in spatial sampling: comparison between square and hexagonal pixels. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition 1989 (CVPR ‘89), pp 604–611

  • Kong X, Liu Y, Wang Y, Tong D, Zhang J (2017) Investigating public facility characteristics from a spatial interaction perspective: a case study of Beijing hospitals using taxi data. ISPRS Int J GeoInf 6(2):38

    Article  Google Scholar 

  • Lam DN, Quattrochi DA (1992) On the issues of scale, resolution, and fractal analysis in the mapping sciences. Prof Geogr 44(1):88–98

    Article  Google Scholar 

  • Lam H, Bertini E, Isenberg P, Plaisant C, Carpendale S (2011). Seven guiding scenarios for information visualization evaluation. Technical Report, University of Calgary. http://innovis.cpsc.ucalgary.ca/innovis/uploads/Publications/Publications/Lam_2011_SGS.pdf

  • Liu X, Gong L, Gong Y, Liu Y (2015a) Revealing travel patterns and city structure with taxi trip data. J Transp Geogr 43:78–90

    Article  Google Scholar 

  • Liu Y, Liu X, Gao S, Gong L, Kang C, Zhi Y, Chi G, Shi L (2015b) Social sensing: a new approach to understanding our socioeconomic environments. Ann Assoc Am Geogr 105(3):512–530

    Article  Google Scholar 

  • Liu X, Kang C, Gong L, Liu Y (2016) Incorporating spatial interaction patterns in classifying and understanding urban land use. Int J Geogr Inf Sci 30(2):334–350

    Article  Google Scholar 

  • Masser I, Brown PJ (1977) Spatial representation and spatial interaction. Pap Reg Sci Assoc 38(1):71–92

    Article  Google Scholar 

  • Murray AT, Liu Y, Rey SJ, Anselin L (2012) Exploring movement object patterns. Ann Reg Sci 49(2):471–484

    Article  Google Scholar 

  • Rae A (2009) From spatial interaction data to spatial interaction information? Geovisualisation and spatial structures of migration from the 2001 UK census. Comput Environ Urban Syst 33(3):161–178

    Article  Google Scholar 

  • Robertson PK, O’Callaghan JF (1986) The generation of color sequences for univariate and bivariate mapping. IEEE Comput Graph Appl 6(2):24–32

    Article  Google Scholar 

  • Roy JR, Thill JC (2004) Spatial interaction modelling. Pap Reg Sci 83(1):339–361

    Article  Google Scholar 

  • Sahr K, White D, Kimerling AJ (2003) Geodesic discrete global grid systems. Cartogr Geogr Inf Sci 30(2):121–134

    Article  Google Scholar 

  • Taylor PJ (1975) Distance decay in spatial interactions. In: Openshaw S (ed) Concepts and techniques in modern geography. Geo Books, Norwick, pp 3–35

    Google Scholar 

  • Tobler WR (1976) Spatial interaction patterns. J Environ Syst 6(4):271–301

    Article  Google Scholar 

  • Tobler WR (1987) Experiments in migration mapping by computer. Am Cartogr 14(2):155–163

    Article  Google Scholar 

  • Trumbo BE (1981) A theory for coloring bivariate statistical maps. Am Stat 35(4):220–226

    Google Scholar 

  • Von Landesberger T, Brodkorb F, Roskosch P, Andrienko N, Andrienko G, Kerren A (2016) MOBILITYGRAPHS: visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Trans Vis Comput Graph 22(1):11–20

    Article  Google Scholar 

  • Wattenberg M (2002) Arc diagrams: visualizing structure in strings. In: Proceedings of the IEEE symposium on information visualization 2002 (INFOVIS 2002), pp 110–116

  • Wood J, Dykes J, Slingsby A (2010) Visualisation of origins, destinations and flows with OD maps. Cartogr J 47(2):117–129

    Article  Google Scholar 

  • Wüthrich CA, Stucki P (1991) An algorithmic comparison between square-and hexagonal-based grids. CVGIP: Graph Models Image Process 53(4):324–339

    Google Scholar 

  • Yang Y, Dwyer T, Goodwin S, Marriott K (2017) Many-to-many geographically-embedded flow visualisation: an evaluation. IEEE Trans Vis Comput Graph 23(1):411–420

    Article  Google Scholar 

  • Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):29

    Article  Google Scholar 

  • Zhu X, Guo D (2014) Mapping large spatial flow data with hierarchical clustering. Trans GIS 18(3):421–435

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 41830645, 41625003, and 41771425), the National Key R&D Program of China (Grant No. 2017YFB0503602), and the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems (Grant No. 011177220010020).

Author information

Authors and Affiliations

  1. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, 100871, People’s Republic of China

    Xin Yao, Lun Wu, Di Zhu, Yong Gao & Yu Liu

Authors
  1. Xin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Di Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu Liu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (MP4 12999 kb)

Supplementary material 2 (TXT 0 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, X., Wu, L., Zhu, D. et al. Visualizing spatial interaction characteristics with direction-based pattern maps. J Vis 22, 555–569 (2019). https://doi.org/10.1007/s12650-018-00543-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-018-00543-4

Keywords

Search

Navigation