Spatial Explanation and Planning for Resilience of Community-Based Small Businesses to Environmental Shocks

该项目旨在研究社区小企业如何适应环境冲击，并在不断变化的气候中促进经济增长，通过基于空间的规划透镜完成研究。通过汇集来自社会生态系统，社区经济发展和人类行为的知识和理论，该研究有可能产生有价值的见解和方法，以支持CSB在各个层面的气候适应。虽然在创造充满活力的社区，提高生活质量和促进劳动力多样性方面发挥着至关重要的作用，但CSB非常容易受到环境冲击。研究和参与活动为创造更加多样化和公平的环境奠定了科学基础，并使当地社区能够制定建设复原力的战略和政策。此外，该项目还为学生、企业主、从业人员和政策制定者提供了丰富的学习机会。社区小型企业（CSB）必须随着时间的推移保持和发展其客户群。然而，如果没有认真考虑或妥善协调这些设施的位置、土地使用和基础设施支持，就低估了它们对环境冲击的风险。该项目的目的是获得一个定量的，系统的，和普遍的了解如何在他们的社区CSB位置和其他空间因素的具体特点有助于客户的需求模式和整体弹性。该研究团队利用新的大型数据集开发了一个理论指导的框架，该框架结合了人工智能来协助空间规划。该框架旨在预测CSB的短期适应能力并评估长期适应措施。虽然最初的重点是美国东南部受洪水影响的沿海社区，但开发的方法可以广泛应用于其他自然灾害类型和其他地区。该奖项反映了NSF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。

The project aims to study how community-based small businesses (CSB) adapt to environmental shocks and promote economic growth in a changing climate, with research being completed through a spatially based planning lens. By bringing together knowledge and theories from socio-ecological systems, community economic development, and human behaviors, the research has the potential to generate valuable insights and methods for supporting CSB climate adaptation at various levels. While playing a vital role in creating vibrant communities, improving quality of life, and fostering workforce diversity, CSB are highly vulnerable to environmental shocks. The research and engagement activities lay the scientific foundation for creating more diverse and equitable environments and enabling local communities to develop strategies and policies for building resilience. Additionally, the project provides rich learning opportunities for students, business owners, practitioners, and policymakers.Community-based small businesses (CSB) must maintain and grow their customer base over time. However, their risk to environmental shocks has been underestimated when their location, land use, and infrastructure support are not carefully considered or well coordinated. The project aims to gain a quantitative, systematic, and generalized understanding of how specific characteristics of CSB locations and other spatial factors in their communities contribute to customer demand patterns and overall resilience. The research team leverages new and large datasets to develop a theory-guided framework that incorporates artificial intelligence to assist in spatial planning. This framework is designed to anticipate short-term resilience and evaluate long-term adaptation measures for CSB. While the initial focus is on coastal communities in the US Southeast affected by flooding, developed methods could be broadly applied to other natural disaster types and in other regions.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.