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Spatial Heterogeneity, Scale, Data Character, and Sustainable Transport in the Big Data Era

  • Bin JiangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 879)

Abstract

I have advocated and argued for a paradigm shift from Tobler’s law to scaling law, from Euclidean geometry to fractal geometry, from Gaussian statistics to Paretian statistics, and – more importantly – from Descartes’ mechanistic thinking to Alexander’s organic thinking. Fractal geometry falls under the third definition of fractal given by Bin Jiang – that is, a set or pattern is fractal if the scaling of far more small things than large ones recurs multiple times – rather than under the second definition of fractal by Benoit Mandelbrot, which requires a power law between scales and details. The new fractal geometry is more towards Christopher Alexander’s living geometry, not only for understanding complexity, but also for creating complex or living structure. This short paper attempts to clarify why the paradigm shift is essential and to elaborate on several concepts, including spatial heterogeneity (scaling law), scale (or the fourth meaning of scale), data character (in contrast to data quality), and sustainable transport in the big data era.

Keywords

Scaling law Living structure Data character 

Notes

Acknowledgements

This paper was originally published as an editorial for the special issue on geospatial big data and transport [17]. It was substantially inspired by my recent panel presentation “On Spatiotemporal Thinking: Spatial heterogeneity, scale, and data character”, presented at the panel session entitled “Spatiotemporal Study: Achievements, Gaps, and Future” with the AAG 2018 Annual Meeting, New Orleans, April 10–15, 2018, and my keynote “A Geospatial Perspective on Sustainable Urban Mobility in the Era of BIG Data”, presented at CSUM 2018: Conference on Sustainable Urban Mobility, May 24–25, Skiathos Island, Greece.

References

  1. 1.
    Jiang, B., Yin, J.: Ht-index for quantifying the fractal or scaling structure of geographic features. Ann. Assoc. Am. Geogr. 104(3), 530–541 (2014)CrossRefGoogle Scholar
  2. 2.
    Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H. Freeman and Co., New York (1982)zbMATHGoogle Scholar
  3. 3.
    Alexander, C., Neis, H., Alexander, M.M.: The Battle for the Life and Beauty of the Earth. Oxford University Press, Oxford (2012)Google Scholar
  4. 4.
    Alexander, C.: The Nature of Order: An Essay on the Art of Building and the Nature of the Universe. Center for Environmental Structure: Berkeley, CA (2002–2005)Google Scholar
  5. 5.
    Goodchild, M.F.: Reimagining the history of GIS. Ann. GIS (2018).  https://doi.org/10.1080/19475683.2018.1424737
  6. 6.
    Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W.: Geographic Information Science and Systems. Wiley, Chichester (2015)Google Scholar
  7. 7.
    Tobler, W.: A computer movie simulating urban growth in the detroit region. Econ. Geogr. 46(2), 234–240 (1970)CrossRefGoogle Scholar
  8. 8.
    Jiang, B.: Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity. GeoJournal 80(1), 1–13 (2015b). Reprinted in Behnisch, M., Meinel, G. (eds.): Trends in Spatial Analysis and Modelling: Decision-Support and Planning Strategies, pp. 23–40. Springer, Berlin (2017)Google Scholar
  9. 9.
    Jiang, B., Brandt, A.: A fractal perspective on scale in geography. ISPRS Int. J. Geo-Inf. 5(6), 95 (2016).  https://doi.org/10.3390/ijgi5060095e
  10. 10.
    Jiang, B.: A complex-network perspective on Alexander’s wholeness. Physica A: Stat. Mech. Appl. 463, 475–484 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jiang, B.: Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution. Prof. Geogr. 65(3), 482–494 (2013)CrossRefGoogle Scholar
  12. 12.
    Jiang, B., Liu, X.: Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information. Int. J. Geogr. Inf. Sci. 26(2), 215–229 (2012). Reprinted in Akerkar, R. (ed.) Big Data Computing, pp. 483–500. Taylor & Francis, London (2013)Google Scholar
  13. 13.
    Jiang, B.: Head/tail breaks for visualization of city structure and dynamics. Cities 43, 69–77 (2015a). Reprinted in Capineri, C., Haklay, M., Huang, H., Antoniou, V., Kettunen, J., Ostermann, F., Purves, R. (eds.) European Handbook of Crowdsourced Geographic Information, pp. 169–183. Ubiquity Press, London (2016)Google Scholar
  14. 14.
    Descartes, R.: The Geometry of Rene Descartes. Dover Publications, New York. Translated by Smith, D.E., Latham, M.LGoogle Scholar
  15. 15.
    Jiang, B., Miao, Y.: The evolution of natural cities from the perspective of location-based social media. Prof. Geogr. 67(2), 295–306. Reprinted in Plaut, P., Shach-Pinsly, D. (eds.) ICT Social Networks and Travel Behaviour in Urban Environments, Routledge (2018)Google Scholar
  16. 16.
    Buchanan, M.: The Social Atoms: Why the Rich Get Richer, Cheaters Get Caught, and Your Neighbor Usually Looks Like You. Bloomsbury, New York (2007)Google Scholar
  17. 17.
    Jiang, B.: Editorial: spatial heterogeneity, scale, data character and sustainable transport in the big data era. ISPRS Int. J. Geo-Inf. 7(5), 167.  https://doi.org/10.3390/ijgi7050167

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Engineering and Sustainable Development, Division of GIScienceUniversity of GävleGävleSweden

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