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

SCC-Planning：小型和萎缩社区智能决策的数据驱动框架

基本信息

批准号：
1736718
负责人：
Kimberly Zarecor
金额：
$ 10万
依托单位：
Iowa State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736718&HistoricalAwards=false
关键词：
SCC Planning Data Driven Framework

项目摘要

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.

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。有证据表明，这些趋势持续了几十年，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缩小的同时保护生活质量和社区基础设施。该项目旨在填补这一空白，为小社区开发一个新的收缩智能概念，利用数据驱动的工具，帮助他们积极规划收缩。规划阶段的目标是进行试点研究，以测试这些工具在爱荷华州的可行性和可靠性。试点研究将使用来自广泛可用来源的数据，如社交媒体，人口普查，州和市政数据库，与传统指标进行比较，包括来自爱荷华州纵向民意调查的独特基线数据。该试点研究有三个目标：1）证明将智能收缩概念应用于农村社区的可行性，2）通过数据驱动分析评估测量智能收缩的可行性，以及3）测试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民意调查等定性指标的结果，来确定智能收缩指标的代理指标。这些代理将取代传统的收集生活质量数据的方法，这些方法耗时，昂贵且在大地理区域内不完整。在规划阶段，我们将建立智能收缩类型的标准，并选择爱荷华州的六到八个代表性社区进行深入分析。该研究将是变革性的小型和萎缩的社区的研究，因为其强大的综合方法，结合定量数据驱动的分析与智能收缩的定性理解，通过社区参与，空间分析和验证地面数据收集。这种综合方法建立了一个新的框架，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自己的生活质量。这种方法还将为美国各地的社区提供新的机会，使他们能够做出明智的决定，在衰退迹象出现之前减轻萎缩的负面影响。在处理小型和农村社区时，该项目引起了对研究文献中代表性不足的案例的关注。这些知识将通过爱荷华州及其他地方的多个场所，包括通过爱荷华州州立大学推广和外联中心，传播给利益攸关方和公众。所有可扩展数据管道和可视化技术都将通过开源协议获得许可。