  Scaling describes how a given quantity $Y$ that characterizes a system varies
with its size $P$. For most complex systems it is of the form $Y\sim P^\beta$
with a nontrivial value of the exponent $\beta$, usually determined by
regression methods. The presence of noise can make it difficult to conclude
about the existence of a non-linear behavior with $\beta\neq 1$ and we propose
here to circumvent fitting problems by investigating how two different systems
of sizes $P_1$ and $P_2$ are related to each other. This leads us to define a
local scaling exponent $\beta_{\mathrm{loc}}$ that we study versus the ratio
$P_2/P_1$ and provides some sort of `tomography scan' of scaling across
different values of the size ratio, allowing us to assess the relevance of
nonlinearity in the system and to identify an effective exponent that minimizes
the error for predicting the value of $Y$. We illustrate this method on various
real-world datasets for cities and show that our method reinforces in some
cases the standard analysis, but is also able to provide new insights in
inconclusive cases and to detect problems in the scaling form such as the
absence of a single scaling exponent or the presence of threshold effects.
