SCC-PG: Early Community Intervention for Neighborhood Revitalization Using Artificial Intelligence and Emerging Technologies

美国城市社区的房屋废弃给居民和市政府带来了深刻的挑战。在堪萨斯城，超过1万所房屋被遗弃，安全拆除每座房屋的成本为1万美元。然而，拆迁进一步削弱了弱势社区的物质和社会结构。这项提议使社区成员、城市领导人、技术专家和当地公立学校的学生在规划过程中能够利用人工智能(AI)的使用。该提案利用与市政府的合作，在城市车辆上安装摄像头，以识别住房腐烂的早期标志。该项目的目的是开发一个早期预警系统，地方政府和社区协会可以提供微观政策输入，以阻止废弃过程。该项目已经形成了一个由数据科学家、社区领袖和社会科学家组成的多学科团队，为解决废弃住房问题奠定了科学和工程基础。在这项规划拨款中，该项目集中在密苏里州堪萨斯城(KCMO)的一个小社区，以开发减少房屋遗弃问题所需的工具和规划。具体地说，该项目将把重点放在艾芬豪社区，那里居住着大约5500名居民，其中大部分是低收入少数族裔居民(95%是非洲裔美国人，35%低于贫困线)，大约40%的土地被空地和废弃物业占据。该项目的具体目标是i)通过应用深度学习技术和统计建模来提高房屋废弃的科学知识，ii)培养一个多学科和多样化的研究社区，以开发测量、预测和防止房屋废弃蔓延的工具，以及iii)将KCMO的社区利益相关者整合到一系列社区项目中，以实现最佳自我控制，以减少废弃房屋并解决遗留问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Housing abandonment in urban American communities presents a profound challenge for residents and city governments. In Kansas City, over 10,000 houses have been abandoned, with safe demolition costing $10,000 per house. However, demolition further weakens the physical and social fabric of disadvantaged neighborhoods. This proposal empowers community members, city leaders, technologists, and local public-school students in a planning process to leverage the use of Artificial Intelligence (AI). The proposal leverages collaboration with the municipal government to use cameras on city vehicles to identify early markers of housing decay. The intention is to develop an early warning system where local government and neighborhood associations can provide micro-policy inputs in order to halt the process of abandonment.This project has formed a well-integrated multidisciplinary team of data scientists, community leaders and social scientists to lay out the scientific and engineering foundations for addressing the issue of abandoned housing. In this planning grant, the project concentrate on a small neighborhood of Kansas City, Missouri (KCMO) to develop the tools and planning required for reducing the issue of housing abandonment. Specifically, the project will focus on the Ivanhoe neighborhood, which is home to around 5,500 mostly low-income minority residents (95% African-American, 35% below the poverty level) and which has about 40% of its land occupied by vacant lots and abandoned properties. Specific objectives of this project are i) enhancing scientific knowledge of housing abandonment by applying deep learning technology and statistical modeling, ii) fostering a multidisciplinary and diverse research community to develop tools for measuring, predicting, and preventing the spread of housing abandonment, and iii) integrating KCMO’s community stakeholders into a series of community projects for optimal self-control to reduce abandoned housing and resolve residual problems.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.