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

SCC-IRG 第 2 轨道：克服农村数据不足，提高智能生活质量和社区服务

• 批准号: 1952007
• 负责人: Kimberly Zarecor
• 金额: $ 150万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952007&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Overcoming; Rural

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.

美国许多小型农村社区正在萎缩，有证据表明，这种趋势在许多地方不太可能扭转。此前关于农村衰退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，以试图重振经济活动并扭转人口流失。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对抗。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，以支持爱荷华州一组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。该团队正在使用社区信息生态系统的原型，该原型将提高小镇的数据利用能力。自 1994 年以来出现衰退迹象的萎缩城镇将与类似的导师社区配对，这些社区也在减少人口，但在同一时期的纵向民意调查中报告称，这些社区对生活质量的看法有所改善。许多小社区都面临着农村数据不足的问题，即缺乏系统的本地数据收集以及在决策过程中缺乏对现有数据的利用。该项目旨在通过设计新的用户友好方法来收集、分析和可视化数据来克服这一缺陷。为了让当地领导人做好有效利用这些新资源的准备，该团队正在开发课程，以增强当地在社区愿景、项目规划和数据分析方面的知识和技能。智能收缩策略、更好的数据利用和领导技能的结合将帮助小型和收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上：农村人口统计和生活质量、欧美城市的智能收缩以及转化数据科学。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进行评估，认为值得支持以及更广泛的影响审查标准。

项目成果

NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data

NET-FLEET：利用异构数据实现完全去中心化联邦学习的线性收敛加速

• DOI: 10.1145/3492866.3549723
• 发表时间: 2022-08-17
• 期刊: Proceedings of the Twenty-Third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
• 作者: Xin Zhang;Minghong Fang;Zhuqing Liu;Haibo Yang;Jia Liu;Zhengyuan Zhu
• 通讯作者: Zhengyuan Zhu

Predicting resident satisfaction with public schools in small town Iowa

预测爱荷华州小镇居民对公立学校的满意度

• DOI: 10.1002/sta4.517
• 发表时间: 2023-03
• 期刊: Stat
• 影响因子: 1.7
• 作者: Batista, Ricardo;Zhu, Zhengyuan;Peters, David;Zarecor, Kimberly
• 通讯作者: Zarecor, Kimberly

Conditions facilitating aging in place in rural communities: The case of smart senior towns in Iowa

促进农村社区老龄化的条件：爱荷华州智慧老年城镇的案例

• DOI: 10.1016/j.jrurstud.2023.01.005
• 发表时间: 2023-01
• 期刊: Journal of Rural Studies
• 影响因子: 5.1
• 作者: Matysiak, Ilona;Peters, David J.
• 通讯作者: Peters, David J.

Exploring Rural Shrink Smart Through Guided Discovery Dashboards

通过引导发现仪表板探索农村智能收缩

• DOI: 10.6339/22-jds1080
• 发表时间: 2022-12
• 期刊: Journal of Data Science
• 作者: Bradford, Denise;VanderPlas, Susan
• 通讯作者: VanderPlas, Susan

Finding Resilience in Unexpected Places: Why Design Still Matters in Shrinking Rural Communities

在意想不到的地方寻找弹性：为什么设计在缩小的农村社区中仍然很重要

• DOI: 10.35483/acsa.aia.inter.21.6
• 发表时间: 2023-11
• 期刊: ACSA Press
• 作者: Luz, Ana;Das, Biswa;Peters, David J.;Drinkwater, Jennifer;Zarecor, Kimberly E.
• 通讯作者: Zarecor, Kimberly E.