Collaborative Research: Interactions of Sustainable Urban Design with Gentrification Processes

世界各地的城市都致力于推进可持续和有弹性的建筑环境，以公平地减少碳排放，缓解热岛效应，提高城市宜居性。然而，这些举措可能会提高房价和生活成本，最终通过一个被称为绿色高档化的过程取代长期居民。本研究将通过检查和比较谷歌街景的历史和当前图像以及人口普查局的人口数据，评估和预测与城市社区各种可持续发展举措相关的绿色高级化。利用人工智能工具，研究团队将确定绿色高档化的物理指标和社会人口统计指标，以分析高档化过程，并参照-à-vis城市可持续发展倡议。这些工具将以费城作为案例研究和过去二十年来实施的可持续发展举措来开发。这些倡议包括绿色空间开发、都市农业、植树、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。城市决策者和规划者将更好地了解可持续性政策和项目如何影响中产阶级化，以及如何减轻其影响并改善公平的结果。此外，社区和公共机构将能够更好地分析、预测和解决可持续发展的负面影响，确定最脆弱的社区，并推进公平的可持续发展举措。在理解如何、何时以及哪些城市可持续发展项目（即改善交通、绿地和住房）影响中产阶级化导致的流离失所方面，存在一个关键的知识缺口。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数据的可用性。研究小组将开发城市分析和以建筑环境为中心的预测分析相结合的方法，以预测和绘制高档化易感性。该团队将这些预测与城市建筑能源使用、绿色空间开发和交通系统的模型相结合，以确定由可持续发展项目驱动的所有变体和生命周期阶段的高档化过程。该研究项目将利用机器学习算法、城市能源建模和社会人口数据的人工智能图像识别方法，取得以下三个成果：(i)开发应用于谷歌街景（GSV）图像数据的人工智能计算机视觉方法，并使用机器学习（ML）算法来识别和分类绿色高档化指标；（ii）将社会人口和能源数据与第(i)部分开发的GSV-ML模型相结合，以评估绿色高档化与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化特征对社区高档化敏感性的预测能力，并对研究区域的高档化进行初步预测；（iii）制定可持续的城市设计和政策，以社会正义和公平为基础，防止绿色高档化。最后，本项目的重点是预测绿化干预对高档化进程的影响，以促进更公平的可持续城市政策和项目。该合作项目由CBET/ENG环境可持续发展项目和BCS/SBE人类环境与地理科学项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.