Introduction
Urban spatial structure is important to understanding urban
social-ecological interactions and provides a bridge to planning
sustainable cities (Zhou et al., 2017). Urban structure 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). However, defining urban structure and key
relationships between structure and ecological processes is challenging
in landscapes characterized by 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 is currently unknown. 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 selected urban areas have been
developed (MacFaden et al., 2012; Pickard et al., 2015) that allows more
nuanced understanding of urban landcover. While some functional
classification approaches have been suggested (see for example Cadenasso
et al., 2007), still major challenges remain in integration of spatial
structure and configuration that allows automated and unbiased analysis
of fine scale 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 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. Landsat thermal bands have been used in research addressing
landcover (Zhou et al., 2011), urban heat island (Rosenzweig et al.,
2009; Zhao et al., 2011), and urban 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). Thus we use ST
as a proxy for a wide range of potential variables of interest.
To account for the heterogenous vertical dimension of the built
enviroment in urban lanscscape in a reproducable and scalable way, we
employ STURLA classification (Hamstead et al., 2016). STURLA has
identified patterens 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). In summary, the urban landscape
is characterized as a discrete number composite landclasses that
characterize the natural and built envirnment in Phildelphia, PA, USA.
The city is one of the poorest cities in the US, with 26 percent of its
population living in poverty (PEW, 2017). It is also one of the most
segregated cities in the US, with African American and Asian populations
concentrated in neighborhoods in West and North Philadelphia
respectively (The Brookings Institution, 2003). The city’s population
peaked in 1950 with over 2 million people, and was declining until 2010
when is started growing again. Recently, Philadelphia is experiencing
strong , yet uneven economic resurgence reflected in job growth and
rising housing prices (PEW, 2017).
Philadelphia’s urban structure emerged through the evolution of its
original plan, laid out by William Penn in 1643. It has a gridded layout
with mostly low and mid-rise residential buildings. A long time
“gentleman’s agreement” kept Penn’s statue on top of city hall as the
highest building in the city, preventing high-rise development for
decades until the 1980s. The most common residential structures in the
city are rowhouses. Rowhouses commonly occupy a narrow street frontage
and are attached to other homes on both sides (Simmons Schade et al.,
2008). Aside from the build environment, green space in the city
includes 19% tree cover and 24% grass-shrub cover that are distributed
unevenly across the city with some neighborhoods densely vegetated and
others with little to no green space (O’Neil-Dunne, 2011). Part of the
city’s sustainability plan, Greenworks Philadelphia, includes a goal of
tree canopy cover of 30% in all city neighborhoods by 2025 (City of
Philadelphia, 2015a). However, until recently, the only publicly
available data for a comprehensive analysis of the city’s green space
has been NLCD landuse-landcover datasets that do not have the spatial
resolution and functional categories required to identify small and
fragmented patches of green in the city. In 2011, a fine scale dataset
of Philadelphia landcover has been released (City of Philadelphia, 2011)
that is used here as the basis for the STURLA. Empirical evidence from
two cities, Berlin and New York City (NYC), were compared (Larondelle et
al., 2014) and more detailed analysis of within class and neighborhood
effects were performed in a Berlin case study (Kremer et al., 2018).
The objectives of this short study were to identify if STURLA could
explain the variation of urban structure in a new model city
(Philadelphia), and quantify this variation using a physical propoerty
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 by adding a third case study city of different
, Philadelphia, and comparing the results to previous studies.