Collaborative Research: Interactions of Sustainable Urban Design with Gentrification Processes

Cities around the world aim to advance sustainable and resilient built environments that equitably reduce carbon emissions, mitigate heat island effects, and enhance urban livability. However, these initiatives can increase housing prices and the cost of living, ultimately displacing long-time residents through a process called green gentrification. This research will evaluate and predict green gentrification associated with various sustainability initiatives in

urban neighborhoods by examining and comparing historical and current imagery from Google Street View and demographic data from the Census Bureau. Using Artificial Intelligence tools, the research team will identify the physical indicators and sociodemographic metrics of green gentrification to analyze gentrification processes vis-à-vis urban sustainability initiatives.

These tools will be developed using the City of Philadelphia as a case study and the sustainability initiatives it has implemented over the last two decades. These initiatives include green space development, urban agriculture, tree planting, energy efficient retrofits, cycle lanes, public transit, and solar energy installations. The research will be an important step towards addressing significant societal challenges in Philadelphia and other urban contexts.

Urban policymakers and planners will gain a better understanding of how sustainability policies and programs influence gentrification and how to mitigate its effects and improve equitable outcomes. Furthermore, communities and public institutions will be better able to analyze, predict, and address the negative consequences of sustainable development, identify the most vulnerable neighborhoods, and advance equitable sustainability initiatives.

There is a critical knowledge gap in understanding how, when, and which urban sustainability programs (i.e., improvements to transit, greenspace, and housing) impact gentrification-led displacement. In this research, the investigators will develop new models and methods that rely on recent advances in Machine Learning and the availability of high-volume spatiotemporal and sociodemographic data.

The research team will develop methods at the intersection of urban analytics and built environment-centered predictive analyses to forecast and map gentrification susceptibility. The team will integrate these forecasts with models of urban building energy use, greenspace development, and transit systems to identify gentrification processes, in all its variants and lifecycle stages, that are driven by sustainability programs.

The research project will harness artificial intelligence image recognition methods with Machine Learning algorithms, urban energy modeling, and sociodemographic data with the following three outcomes: (i) Development of Artificial Intelligence computer vision methods applied to Google Street View (GSV) image data with a Machine Learning (ML) algorithm to identify and categorize indicators of green gentrification; (ii) Integration of sociodemographic and energy data with the GSV-ML model developed in part (i) to evaluate the relationship between green gentrification and sustainable interventions. This integrated model will use Machine Learning to quantify the predictive power of different urban greening features on neighborhood gentrification susceptibility and develop a tentative forecast of gentrification for the study area; (iii) Elicidation of sustainable urban design and policies that are underpinned by social justice and equity concerns and prevent green gentrification.

Ultimately, this project focuses on predicting the ways in which greening interventions impact gentrification processes to advance more equitable sustainable urban policies and programs.

This collaborative project is co-funded by the CBET/ENG Environmental Sustainability program and the BCS/SBE Human-Environmental and Geographical Sciences program.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.