Discussion:
STURLA captured urban structure and characterized the physical property
of ST in Philadelphia as previously done in NYC (Hamstead et al., 2016)
and Berlin (Kremer et al., 2018), despite variation in size, demography,
and historical planning. This suggests that urban areas may be subject
to similar processes that result in between city-redundant spatial
organizations (Votsis & Haavisto, 2019). Likewise, STURLA may be suited
for understanding urban biogeography, environmental justice, and city
planning for a sustainable future. Global analyses of cities may also
identify clusters of urban areas that would benefit from similar
management practices. Likewise, STURLA offers a computationally
inexpensive alternative to network analyses of urban structure (Zhong et
al., 2014).
One of the main limitations of STURLA classification is the binary
nature of class assignment. If the STURLA grid were shifted it would
change the relative proportions of the within class elements (e.g. trees
decrease and other elements increase). Despite this variation, STURLA
classes are a discrete countable number and have a Poisson distribution.
Thus, the ranked order abundances of different STURLA classes should not
vary in the most frequent classes. For example, since ‘tgpl’ is common
in Philadelphia, a reduction in a large number of ‘tgpl’ classes in the
city would be relatively less influential than additions/reductions of
less common class.