Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water

加强美国基础设施(SAI)是一项NSF计划，旨在刺激以人类为中心的基础性研究和潜在的变革性研究，以加强美国的基础设施。有效的基础设施为社会经济活力和广泛的生活质量改善提供了坚实的基础。强大、可靠和有效的基础设施刺激私营部门创新，增长经济，创造就业机会，提高公共部门服务提供效率，加强社区力量，促进机会均等，保护自然环境，增强国家安全，推动美国的领导地位。要实现这些目标，需要来自科学和工程学科的专业知识。SAI侧重于人类推理和决策、治理以及社会和文化进程的知识如何能够建设和维护有效的基础设施，以改善生活和社会，并建立在技术和工程进步的基础上。获得安全的饮用水供应对所有人的健康和福利至关重要。在许多地方，私人水井是居民的主要水源。这项SAI研究项目考察了在私人水井是居民的主要水源的情况下，个人和家庭饮用水的可获得性。对于缺乏广泛社会支持的居民来说，维持安全的饮用水供应可能特别具有挑战性，这一点从与其他社区的地理联系中可见一斑。这种支持在自然灾害和中断供水的相关灾害发生后可能特别重要。该项目使用关于手机用户流动性的数据来描述居民呼吁的社会救助的特征。方法用于说明此类数据集中不同组的不同表示。该分析考虑了可能导致水质变化的其他变量，如人口和社会经济因素。水质是通过私人水井的样本和与业主的调查来评估的。该项目高度重视与包括推广服务和卫生部门在内的利益攸关方分享重要成果。该项目还将为初中和高中课程做出贡献，这些课程将在不同的公立学校环境中共享和使用。利用多个互补的数据集来研究社交网络中的优势地位如何有助于改善私人水井的水质，特别是在受到最近洪灾影响的地理环境中。社交网络是根据移动电话用户的移动性数据构建的，开发了新的算法方法来解决这些数据的典型偏差。在构建这些网络后，社会网络中位置的测量被用来预测私人油井污染的变化。为图形神经网络分析开发的算法方法将在类似的研究中有更广泛的潜在应用，这些研究试图解释大型档案数据集的代表性中的偏见，包括使弱势群体处于不利地位的偏见。该项目涉及多名学生，为职业生涯早期科学家的培训和教育做出贡献。该奖项由社会、行为和经济(SBE)科学局和地球科学局支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Strengthening American Infrastructure (SAI) is an NSF Program seeking to stimulate human-centered fundamental and potentially transformative research that strengthens America’s infrastructure. Effective infrastructure provides a strong foundation for socioeconomic vitality and broad quality of life improvement. Strong, reliable, and effective infrastructure spurs private-sector innovation, grows the economy, creates jobs, makes public-sector service provision more efficient, strengthens communities, promotes equal opportunity, protects the natural environment, enhances national security, and fuels American leadership. To achieve these goals requires expertise from across the science and engineering disciplines. SAI focuses on how knowledge of human reasoning and decision-making, governance, and social and cultural processes enables the building and maintenance of effective infrastructure that improves lives and society and builds on advances in technology and engineering.Access to a safe supply of drinking water is essential for the health and welfare of all people. In many places, private wells are the primary source of water for residents. This SAI research project examines the availability of potable drinking water to individuals and households in settings where private wells are the predominant source of water for residents. Maintaining a safe supply of drinking water may be particularly challenging for residents who lack broad access to social support, as reflected in geographic connections to other communities. This support may be especially important in the aftermath of natural disasters and related hazards that disrupt water supplies. This project uses data on the mobility of cell phone users to characterize the social assistance that residents call upon. Methods are used to account for unequal representation of different groups in such datasets. The analysis considers other variables that may cause variation in water quality, such as demographic and socioeconomic factors. Water quality is evaluated with samples of private wells and surveys with owners. The project places high priority on sharing important findings with stakeholders, including extension services and health departments. The project also contributes to middle and high school curricula that will be shared and used in diverse public school settings.Multiple, complementary datasets are leveraged to examine the ways in which advantageous positions in social networks may contribute to better water quality in private wells, particularly in geographic settings that have been impacted by recent flooding. Social networks are constructed from data on the mobility of cellular phone users, and new algorithmic approaches are developed to address the biases that typify these data. Upon constructing these networks, measures of positions in social networks are used to predict variation in the contamination of private wells. The algorithmic approaches developed for graph neural network analysis will have broader potential applications in similar research that seeks to account for biases in the representativeness of large archival datasets, including biases that disadvantage vulnerable populations. The project involves multiple students, contributing to the training and education of early-career scientists.This award is supported by the Directorate for Social, Behavioral, and Economic (SBE) Sciences and the Directorate for Geosciences.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.