Abstract

Discerning the relationship between urban structure and function is crucial for sustainable city planning and requires examination of how components in urban systems are organized in three-dimensional space. The Structure of Urban Landscape (STURLA) classification accounts for the compositional complexity of urban landcover structures including the built and natural environment. Building on previous research, we develop a STURLA classification for Philadelphia, PA and study the relationship between urban structure and land surface temperature. We evaluate the results in Philadelphia as compared to previous case studies in Berlin, Germany and New York City, United States. In Philadelphia, STURLA classes hosted ST that were unique and significantly different as compared to all other classes. We find a similar distribution of STURLA class composition across the three cities, though NYC and Berlin showed strong correlation with each other but not with Philadelphia. Our research highlights the use of STURLA classification to capture a physical property of the urban landscape

Highlights

  • Urban spatial structure is important to understanding social and ecological interactions between the built and natural environment and provides a bridge to sustainable development (Zhou et al, 2017)

  • Structure of Urban Landscape (STURLA) classes were able to identify the role of urban structure influencing surface temperature (ST) (Figure 1B)

  • The prevalence and distribution of the STURLA classes in Philadelphia differs from what we found in previous studies of urban structure New York City (NYC) and Berlin (Figure 2)

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Summary

Introduction

Urban spatial structure is important to understanding social and ecological interactions between the built and natural environment and provides a bridge to sustainable development (Zhou et al, 2017). 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 and Cadenasso, 2008). Functional classification of urban structure is necessary for understanding the nature of social and ecological relationships in urban areas (Cadenasso et al, 2007; Zhou et al, 2014; McPhearson et al, 2016). While some functional classification approaches have been suggested (see for STURLA-ST Philadelphia 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

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