Early Community Intervention for Neighborhood Revitalization Using Artificial Intelligence and Emerging Technologies
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.
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Performance PeriodOctober 2020 - September 2022
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University of Missouri-Kansas City
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Award Number1951971
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Lead PIYugyung Lee
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Co-PIHye-Sung Han
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Co-PIJames DeLisle
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Co-PIBrent Never
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Co-PIMajid Bani-Yaghoub
Project Material
- Community-in-the-loop: Creating Artificial Process Intelligence for Co-production of City Service
- Defect prediction using deep learning with Network Portrait Divergence for software evolution
- OpenComm: Open community platform for data integration and privacy preserving for 311 calls
- Feature-Based Fusion Using CNN for Lung and Heart Sound Classification
- Event-Driven Deep Learning for Edge Intelligence (EDL-EI) †
- Facilitating program comprehension with call graph multilevel hierarchical abstractions
My research interests include Real-time and big data analytics (Deep Learning) for pervasive and distributed systems Semantic techniques for mobile and cloud-based systems and applications (Biomedical applications, Web, Mobile computing, and Social networking) Multiple agent systems for dynamic service discovery and composition for service oriented systems Cognitive robotics with IoT Analytics virtual re-ality and augmented reality for immersive intelligence