SCC-Planning: A Data-Driven Framework for Smart Decision-Making in Small and Shrinking Communities

自20世纪80年代以来，许多美国小城镇和农村社区一直在衰落。在中西部，大多数社区都经历了人口萎缩、年轻人外流、失业和基础设施老化的情况。有证据表明，这些趋势已经持续了几十年，不太可能逆转。然而，对小型和农村社区的研究主要集中在记录和观察衰退的各个方面或促进不确定的增长战略，而不是了解社区如何在生活质量和社区基础设施缩小的情况下保护它们。该项目旨在通过为小型社区开发一种新的收缩智能概念来填补这一空白，该概念利用数据驱动的工具来帮助他们积极规划收缩。规划阶段的目标是在爱荷华州进行一项试点研究，以测试这类工具的可行性和可靠性。试点研究将使用来自广泛可用的来源的数据，如社交媒体、人口普查、州和市政数据库，以与传统指标进行比较，包括爱荷华州纵向民意调查的独特基线数据。这项试点研究有三个目标：1)论证将收缩-智能概念应用于农村社区的可行性；2)评估通过数据驱动分析测量智能收缩的可行性；3)测试用于数据分析和与利益相关者沟通的可视化方法。该项目的中心假设是，数据驱动技术将通过使用广泛可用的数据源来估计纵向轮询等定性指标的结果，从而确定智能收缩指标的代理指标。这些指标将取代传统的收集生活质量数据的方法，这些方法既耗时、昂贵，而且在大片地理区域内不完整。在规划阶段，我们将建立智能收缩类型的标准，并在爱荷华州选择6-8个具有代表性的社区进行深入分析。这项研究将对小型和不断缩小的社区的研究产生革命性的影响，因为其强大的集成方法将定量数据驱动分析与对智能收缩的定性理解相结合，并通过社区参与、空间分析和实地数据收集进行验证。这一综合方法创建了一个新的框架，帮助社区利益攸关方了解一些小型和农村社区如何以及为什么能够在人口减少的情况下保护其生活质量。这种方法还将为美国各地的社区提供新的机会，让他们做出明智的决定，在衰落的迹象出现之前，缓解萎缩的负面影响。在解决小型和农村社区的问题时，该项目提请注意研究文献中代表性不足的案例。这一知识将通过爱荷华州内外的多个场所向利益攸关方和公众传播，包括通过爱荷华州立大学推广和外联。所有可扩展的数据管道和可视化技术都将通过开放源码协议获得许可。

Many American small towns and rural communities have been in decline since the 1980s. In the Midwest, most communities have experienced this through shrinking populations, an exodus of younger people, job losses, and aging infrastructure. Evidence shows that these trends have continued over several decades and are unlikely to be reversed. Yet the research on small and rural communities has focused primarily on documenting and observing aspects of decline or promoting uncertain growth strategies, rather than understanding how communities can protect quality of life and community infrastructure while they shrink. This project aims to fill this gap by developing a new shrink-smart concept for small communities that utilizes data-driven tools to assist them in actively planning for shrinkage. The objective of the planning phase is a pilot study to test the feasibility and reliability of such tools in Iowa. The pilot study will use data from broadly available sources, such as social media, census, state, and municipal databases, for comparison with traditional metrics including unique baseline data from longitudinal polling in Iowa. This pilot study has three goals: 1) to demonstrate the feasibility of applying the shrink-smart concept to rural communities, 2) to assess the feasibility of measuring smart shrinkage through data-driven analysis, and 3) to test visualization methods for data analysis and communication to stakeholders. The project's central hypothesis is that data-driven techniques will identify proxy metrics for indicators of smart shrinkage by using broadly available data sources to estimate the results of qualitative measures such as longitudinal polling. These proxies will replace traditional methods of collecting quality-of-life data, which are time-consuming, expensive and incomplete over large geographic areas. In the planning phase, we will establish criteria for types of smart shrinkage and select six-eight representative communities in Iowa for in-depth analysis. The research will be transformative for the study of small and shrinking communities because of its powerful integrated methodology that combines quantitative data-driven analysis with qualitative understanding of smart shrinkage that is verified through community engagement, spatial analysis, and on-the-ground data collection. This integrated methodology creates a new framework to help community stakeholders understand how and why some small and rural communities are able to protect their quality of life even as they lose population. This approach will also provide new opportunities for communities across the United States to make smart decisions that are likely to mitigate the negative effects of shrinkage before signs of decline appear. In addressing small and rural communities, this project brings attention to underrepresented cases in the research literature. This knowledge will be disseminated to stakeholders and the public through multiple venues in Iowa and beyond, including through Iowa State University Extension and Outreach. All of the extensible data pipelines and visualization techniques will be licensed through open source protocols.