Spatial Heterogeneity, Scale, Data Character, and Sustainable Transport in the Big Data Era
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.
KeywordsScaling law Living structure Data character
This paper was originally published as an editorial for the special issue on geospatial big data and transport . 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.
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