Conclusion
In this paper we demonstrate the application of STURLA classification to
quantify the relationship between urban structure and surface
temperature in Philadelphia. We show it can be applied to cities with
different historical patterns of growth in a reproducible manner.
Furthermore, patterns in class abundance and composition can be used to
determine the surface temperature signature of a composite landscape.
Additional research is needed to compare cities of vastly different
urban structure and identify patterns in the relationship between urban
structure with social and ecological properties of the environment.
Understanding general urban structure-environmental function
relationships will help build tools for effective urban planning and
management under global change scenarios.
Table 1: 10 most common STURLA classes in Philadelphia and their ST
statistics. STURLA class codes: t-trees; g-grass; b-bare soil; w-water;
p-paved; l-low building; m-medium building