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In light of the emergence of big data, I have advocated and argued for aparadigm shift from Tobler's law to scaling law, from Euclidean geometry tofractal geometry, from Gaussian statistics to Paretian statistics, and - moreimportantly - from Descartes' mechanistic thinking to Alexander's organicthinking. Fractal geometry falls under the third definition of fractal - thatis, a set or pattern is fractal if the scaling of far more small things thanlarge ones recurs multiple times (Jiang and Yin 2014) - rather than under thesecond definition of fractal, which requires a power law between scales anddetails (Mandelbrot 1982). The new fractal geometry is more towards livinggeometry that "follows the rules, constraints, and contingent conditions thatare, inevitably, encountered in the real world" (Alexander et al. 2012, p.395), not only for understanding complexity, but also for creating complex orliving structure (Alexander 2002-2005). This editorial attempts to clarify whythe paradigm shift is essential and to elaborate on several concepts, includingspatial heterogeneity (scaling law), scale (or the fourth meaning of scale),data character (in contrast to data quality), and sustainable transport in thebig data era.

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