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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H. Freeman and Co., New York (1982)
Alexander, C., Neis, H., Alexander, M.M.: The Battle for the Life and Beauty of the Earth. Oxford University Press, Oxford (2012)
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)
Goodchild, M.F.: Reimagining the history of GIS. Ann. GIS (2018). https://doi.org/10.1080/19475683.2018.1424737
Longley, P.A., Goodchild, M.F., Maguire, D.J., Rhind, D.W.: Geographic Information Science and Systems. Wiley, Chichester (2015)
Tobler, W.: A computer movie simulating urban growth in the detroit region. Econ. Geogr. 46(2), 234–240 (1970)
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)
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
Jiang, B.: A complex-network perspective on Alexander’s wholeness. Physica A: Stat. Mech. Appl. 463, 475–484 (2016)
Jiang, B.: Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution. Prof. Geogr. 65(3), 482–494 (2013)
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)
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)
Descartes, R.: The Geometry of Rene Descartes. Dover Publications, New York. Translated by Smith, D.E., Latham, M.L
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)
Buchanan, M.: The Social Atoms: Why the Rich Get Richer, Cheaters Get Caught, and Your Neighbor Usually Looks Like You. Bloomsbury, New York (2007)
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
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, B. (2019). Spatial Heterogeneity, Scale, Data Character, and Sustainable Transport in the Big Data Era. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_88
Download citation
DOI: https://doi.org/10.1007/978-3-030-02305-8_88
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02304-1
Online ISBN: 978-3-030-02305-8
eBook Packages: EngineeringEngineering (R0)