Introduction
Urban spatial structure is important to understanding social and
ecological interactions between the build and natural environment and
provides a bridge to sustainable development (Zhou et al., 2017).
Characteristics including vegetation and other landcover classes
influence, and can be used to estimate ecological functions (Bastian et
al., 2014; van Oudenhoven et al., 2012) and forecast changes(Dietze,
2019; Dietze et al., 2018) that are crucial under global change
scenarios. Identifying patterns and processes of the structure-function
relationship in the urban landscape in the context of environmental and
ecological processes is challenging due to variable density and patchy
spatial patterns (Pickett & Cadenasso, 2008).
While it is well established that urban areas host ecological
communities subject to unique stressors (Jones & Harrison, 2004; Joyner
et al., 2019; Reese et al., 2016) absent in natural systems (e.g
pollution, high population density), the influence of landscape
heterogeneity on the environment is poorly described. Functional
classification of urban structure is necessary for understanding the
nature of social and ecological relationships in urban areas (Cadenasso
et al., 2007; McPhearson et al., 2016; Zhou et al., 2014). Over the last
decade, fine-scale landcover classification for urban areas have been
developed (MacFaden et al., 2012; Pickard et al., 2015) that allows more
nuanced analyses of urban landcover. While some functional
classification approaches have been suggested (see for example Cadenasso
et al., 2007), major challenges remain in integration of spatial
structure and configuration that allows scalable and reproducible
analysis of relationships between urban form and process.
A major barrier for understanding the relationship between urban
structure and environmental function is the lack of independent
measurement of the fine-scale spatial variability of the distribution of
environmental and ecological variables. Particularly important is the
vertical dimension (e.g. building height) and variation of the
three-dimensional landscape that is rarely addressed (Alavipanah et al.,
2017) in ecological studies. Where independent measurements exist, such
as data from Environmental Protection Agency (EPA) air pollution
monitoring stations or United States Geographical Survey (USGS) water
monitoring sites, the spatial distribution is not sufficient to allow
intra-urban analysis. Surface temperature is one example of a physical
property of the urban environment that has been used in research
addressing landcover (Zhou et al., 2011), urban heat islands (Rosenzweig
et al., 2009; Zhao et al., 2011), and ecosystem services (Schwarz et
al., 2011). Likewise, ST structures patterns of taxonomic and functional
biodiversity (Scherrer & Körner, 2011; Zogg et al., 1997), hydrology
(Reyes et al., 2018), air quality (Li et al., 2018; Sillman & Samson,
1995), and social variables relevant for studies of environmental
injustice (Huang & Cadenasso, 2016; Zhang et al., 2017). We employ ST
as a proxy for a wide range of potential variables of interest across
biotic and abiotic dimensions.
To account for the heterogenous vertical dimension of the built
enviroment in urban lanscscape, we employ The STructure of URban
Landscapes (STURLA) classification (Hamstead et al., 2016). STURLA is a
new classification procedure developed by co-PI Kremer that incorporates
the complexity of urban land cover structures, including the vertical
dimensions of the built environment. The novelty in the STURLA approach
is that it offers a composite functional classification of urban
structure, including the vertical dimension, that is automated, and thus
can be applied to wide geographic regions systematically. STURLA has has
been used to identified patterns of microbial biogeography in the
atmosphere of Philadelphia (J. Stewart et al., 2020), and ST in NYC
(Hamstead et al., 2016) and Berlin (Kremer et al., 2018).
The objectives of this short study are to identify if STURLA could
explain the variation of urban structure in a new model city
(Philadelphia), and quantify this variation using a physical property of
the environment (ST). Results suggest STURLA identifies common urban
structure units that encompass the majority of the variation in the
urban landscape strucutre. Moreover, when correlated to surface
temperature, these common urban structure classifications exhibit
distinct temperature signatures for different urban structure units with
temperature trends dramatically similar between Berlin and NYC. Here, we
contribute to the developing literature on the urban structure-function
relationship using STURLA in Philadelphia.