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.