SCC-IRG Track 2: Overcoming the Rural Data Deficit to Improve Quality of Life and Community Services in Smart & Connected Small Communities

美国许多小型和农村社区正在缩小，有证据表明，这一趋势在许多地方不太可能逆转。以前关于农村衰落的研究集中在观察这些变化或推动不确定的增长战略，试图重振经济活动和扭转人口流失。这个项目提供了一种不同的方法，鼓励社区管理收缩，而不是与之抗争。该项目团队将这种方法称为农村智能收缩。目标是减轻人口减少对生活质量和社区服务的负面影响。该团队正在开发和测试新的教育资源和数字工具，以支持在爱荷华州的一组社区实施农村智能收缩战略。研究团队包括建筑、艺术、社区和区域规划、社会学和统计学等学科的教职员工和研究生，以及爱荷华州城市联盟的专业工作人员。研究目标是开发和测试农村智能收缩课程，并评估其在爱荷华州一组城镇的实施情况。该团队正在使用一个社区信息生态系统的原型，该生态系统将增加小城镇的数据利用能力。自1994年以来出现衰落迹象的缩小城镇将与类似的导师社区配对，这些社区的人口也在减少，但在同一时期的纵向民意调查中，这些社区报告说生活质量有所改善。许多小社区面临农村数据不足的情况，其定义是在决策过程中缺乏系统的当地数据收集和现有数据的利用。该项目试图通过设计新的用户友好的方法来收集、分析和可视化数据，从而克服这一缺陷。为了让当地领导人做好有效利用这些新资源的准备，该团队正在制定课程，以增强当地在社区展望、项目规划和数据分析方面的知识和技能。这种智能收缩策略、更好的数据利用率和领导技能的组合将帮助规模较小和不断缩小的农村社区管理人口流失，并变得更具弹性。该项目在三个领域建立了坚实的基础：农村人口和生活质量，欧美城市的智能收缩，以及翻译数据科学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many small and rural communities in the United States are shrinking and evidence shows that this trend is unlikely to be reversed in many places. Previous research on rural decline has focused on observing these changes or promoting uncertain growth strategies to try to revive economic activity and reverse population loss. This project offers a different approach by encouraging communities to manage shrinkage rather than fight against it. The project team calls this approach rural smart shrinkage. The goal is to mitigate the negative effects of population loss on quality of life and community services. The team is developing and testing new educational resources and digital tools to support the implementation of strategies for rural smart shrinkage in a group of Iowa communities. The research team includes faculty and graduate students from the disciplines of architecture, art, community and regional planning, sociology, and statistics and professional staff at the Iowa League of Cities. The research objectives are to develop and test a rural smart shrinkage curriculum and assess its implementation in a group of Iowa towns. The team is using a prototype of a community information ecosystem that will increase small-town capacity for data utilization. Shrinking towns showing signs of decline since 1994 will be paired with similar mentor communities that are also losing population, but which have reported improving perceptions of quality of life in longitudinal polling over the same period. Many small communities experience a rural data deficit, defined as the absence of systematic local data collection and utilization of existing data in their decision making. The project seeks to overcome this deficit by designing new user-friendly methods to collect, analyze, and visualize data. To prepare local leaders to effectively use these new resources, the team is developing curricula to enhance local knowledge and skills in community visioning, project planning, and data analysis. This combination of smart shrinkage strategies, better data utilization, and leadership skills will help small and shrinking rural communities manage population loss and become more resilient. The project builds on strong foundations in three areas: rural demography and quality of life, smart shrinkage in European and American cities, and translational data science.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.