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.

世界各地的城市旨在推进可持续和抵抗力的建筑环境，从而同样减少碳排放，减轻热岛影响并增强城市宜居性。但是，这些举措可以提高住房价格和生活成本，最终通过称为绿色绅士化的过程来取代长期居民。这项研究将通过检查和比较来自Google Street View的历史和当前图像以及人口普查局的人口数据，评估和预测与Inurban社区各种可持续性倡议相关的绿色绅士化。研究小组使用人工智能工具，将确定绿色绅士化的物理指标和社会人口统计学指标，以分析有关城市可持续性计划的绅士化过程。这些工具将以费城市作为案例研究以及过去二十年来实施的可持续性计划开发。这些举措包括绿色空间开发，城市农业，种植，节能改造，自行车道，公共交通和太阳能装置。这项研究将是解决费城和其他城市环境中重大社会挑战的重要一步。城市决策者和计划者将更好地了解可持续性政策和计划如何影响绅士化以及如何减轻其影响并改善公平成果。此外，社区和公共机构将更好地分析，预测和解决可持续发展的负面后果，确定最脆弱的社区，并提高公平的可持续性计划。了解如何，何时和哪些城市可持续性计划（即改进过境，绿色和住房）如何影响绅士化领导的流离失所。在这项研究中，研究人员将开发新的模型和方法，这些模型和方法依赖于机器学习的最新进展以及大量时空和社会人口统计学数据的可用性。研究团队将在城市分析的交集和以环境为中心的预测分析的交集中开发方法，以预测和映射绅士化敏感性。该团队将将这些森林与城市建筑能源使用，Greenspace开发和过境系统的模型相结合，以确定由可持续性计划驱动的所有变体和生命周期阶段的绅士化过程。该研究项目将通过机器学习算法，城市能源建模和社会人口统计学数据来利用人工智能图像识别方法，并使用以下三个结果来利用：（i）使用用于Google Street View（GSV）图像数据的人工智能计算机视觉方法开发使用机器学习（ML）algorithm来识别和分类绿色绿色绿色绿色培训指标； （ii）将社会人口统计学和能量数据与（i）部分开发的GSV-ML模型的整合在一起，以评估绿色绅士化和可持续干预措施之间的关系。该综合模型将使用机器学习来量化不同城市绿色特征在邻里高档化易感性上的预测能力，并对研究领域的高档化进行初步的预测； （iii）以社会正义和公平关注并防止绿色绅士化为基础的可持续城市设计和政策启发。最终，该项目着重于预测绿色干预措施影响绅士化过程的方式，以推动更公平的可持续城市政策和计划。该协作项目由CBET/ENG环境可持续性计划共同提供，BCS/BCS/SBE人类环境和地理科学的授予，并反映了nsf的统治，并以此为基础，并以此为基础。智力优点和更广泛的影响审查标准。