Collaborative Research: Interactions of Sustainable Urban Design with Gentrification Processes

合作研究：可持续城市设计与绅士化进程的相互作用

• 批准号: 2312048
• 负责人: Charlyn Pearsall
• 金额: $ 22.47万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312048&HistoricalAwards=false
• 关键词: Collaborative; Research; Interactions; Sustainable; Urban

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 inurban 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.

全世界的城市旨在推进可持续的建筑环境，以降低碳发射措施，并提高城市居民的谋生性。从人口普查中的Google Street View和人口统计学数据中，研究团队将确定绅士化的物理人口统计学，以分析这些工具。在过去的二十年中更好地理解可持续发展的高档化如何以及如何进行效果和能力的成果，社区和公共研究所，以及更好地分析，预测和解决SUF可染色开发的负面结构，确定较脆弱的社区，并提高公平的可持续性倡议是一个关键的知识差距，而城市可持续性计划（即改进，绿色和住房）影响了由绅士化的流离失所。和社会人口统计学数据。和生命周期阶段，由可持续性计划驱动的啤酒。 （GSV）具有机器学习的图像数据（ML）识别和分类绿色的指标； ENT绿色三局限制最终，该项目重点介绍了伟大干预措施影响绅士化过程以推动更公平的可持续性政策和计划。科学计划。该奖项反映了任务，并被认为是值得使用Tounlectul优点的审查标准的值得支持态度的。