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萎缩，有证据表明，这种趋势在许多地方不太可能逆转。以前对农村人口下降的研究主要集中在观察这些变化或促进不确定的增长战略，以试图恢复经济活动和扭转人口流失。该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。项目团队将这种方法称为农村智能收缩。目标是减轻人口流失对生活质量和社区服务的不利影响。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些社区实施农村智能收缩战略。研究团队包括来自建筑、艺术、社区和区域规划、社会学、统计学等学科的教师和研究生，以及爱荷华州城市联盟的专业人员。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，这将增加小城镇的数据利用能力。自1994年以来出现衰退迹象的萎缩城镇将与人口也在减少的类似指导社区配对，但在同一时期的纵向民意调查中，这些社区报告对生活质量的看法有所改善。许多小社区都存在农村数据不足的问题，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。该项目旨在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。为了使当地领导人能够有效地利用这些新资源，该小组正在编制课程，以提高当地在社区愿景、项目规划和数据分析方面的知识和技能。智能收缩战略、更好的数据利用和领导技能相结合，将有助于小型和不断萎缩的农村社区管理人口流失，并变得更具弹性。该项目建立在三个领域的坚实基础之上：农村人口和生活质量、欧洲和美国城市的智能收缩以及转化数据科学。该奖项反映了美国国家科学基金会的法定使命，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。

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.