Campus Martius & Cadillac Square – Enhancing the Benefits Of Community Parks

Detroit, Michigan, USA

Well designed and managed parks enhance the livability of our cities while offering tremendous benefits to surrounding communities. The tangible benefits of parks include the generation of economic wealth and improved public health. While parks are designed and managed to generate community benefits, there remains a need for tools that can more rigorously measure how communities use their parks and to quantify derived benefits on varying spatiotemporal scales. The overarching goal of the proposed project is to advance a data-driven methodology to continuously assess park use and public appreciation of benefits derived

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Current park design methodologies embody design principles that are based on historical evidence and intuitive feel for what makes a space inviting and how curation of the space can serve community needs. However, parks are rarely studied well after commissioning to assess if design assumptions remain valid and if design features are tangibly achieving the community benefits sought. This prevents managers from deploying limited resources to update and improve their park spaces in an optimal manner.

The project aims to utilize a data-driven framework that combines a scalable monitoring strategy to sense how people use park spaces with longitudinal surveys implemented to track community perceptions of park benefits over time. This project will work focus on deployments in Campus Marcius and Cadillac Square parks in Detroit.

The project conceptually organizes park function into four interconnected layers of functional abstraction. The first layer is the physical layer and consists of the spatial dimensions of the park including how the park interacts with other spaces in the city. The second layer is the asset layer consisting of the physical objects (e.g., paths, benches, trash cans, playgrounds) that service park activities. The third layer is the social layer and consists of community members, their activities, and social interactions both within the park as well as outside of the park. The final layer is a mobility layer and represents how people move within the park space, how patrons access (e.g., enter and exit) the park, and the broader suite of mobility services (e.g., public transit) that facilitate regional access. This approach to abstracting how parks function provides a framework for identifying how data can be used to study the function of each layer and provides a basis for making decisions on how parks are managed to achieve desired community outcomes.

The project will explore the adoption of computer vision using video of public open spaces from security cameras installed in parks to automate the identification of how members of the community use their parks. This will offer real-time spatiotemporal mapping of users and their activities. Specifically, it would identify how people interact with park assets (Asset Layer), classify social behavior (Social Layer) and map people movements including user access to the park (Mobility Layer). The proposal adopts advanced machine learning tools such as deep learning of image detectors to automate the process of identifying people and their activities. The continuous nature of the approach provides park managers with a deeper understanding of how their decisions will impact members of the community and a means of assessing in near real-time if their decisions have had desired outcomes.

Quantitative data on park users derived from camera images empower stakeholders to understand how park amenities are being used and allow them to map how resource investments are leading to desired park outcomes (e.g., facilitating physical and recreational activity, encouraging social interactions, improving user access to spaces infrequently used). Especially in under-resourced communities, data-driven space curation can lead to major gains in operational efficiency making park management more cost-effective thereby allowing limited budgets to do more.

Funding Source

Downtown Detroit Partnership

Quicken Loan Fund

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