  In light of the emergence of big data, 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 - that
is, a set or pattern is fractal if the scaling of far more small things than
large ones recurs multiple times (Jiang and Yin 2014) - rather than under the
second definition of fractal, which requires a power law between scales and
details (Mandelbrot 1982). The new fractal geometry is more towards living
geometry that "follows the rules, constraints, and contingent conditions that
are, inevitably, encountered in the real world" (Alexander et al. 2012, p.
395), not only for understanding complexity, but also for creating complex or
living structure (Alexander 2002-2005). This editorial 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.
