CHS: Small: Collaborative Research: Making Information Deserts Visible: computational models, disparities in civic technology use, and urban decision making

这项研究将开发一个基础工具，用于了解公民技术如何使用以及信息不平等如何在城市中表现出来。 新的公民技术的用户数据揭示了公民信息环境中的不平等现象，这些数据只是最近才出现。由于大部分数据由于不同的人-数据关系而在人口统计学或地理空间上存在偏差，因此计算社会科学家使用数据建模和算法技术来调整数据并消除数据处理过程中的偏差。然而，这种方法限制了我们对如何以及为什么产生有偏见的信息的理解，以及我们解决城市信息不平等和有偏见的数据创造的能力。因此，随着城市向信息和通信技术支持的电子政务过渡，它们可能会将过去的不平等投射到未来的智慧城市中，因此需要采取新的方法。这项创新研究分析和可视化了波士顿311系统的数据，用于向市政府报告非紧急问题，使用计算和定性方法来识别，分类和理解众包市政系统正在制度化的信息差异，抑制智慧城市转型，并使信息沙漠永久化。对于波士顿及其市民来说，这项研究可以改善该市311系统的功能和公平性。由此产生的见解和工具还可以为其他城市实施智慧城市技术提供信息，识别现有城市数据集的潜在扭曲，并提出可能的纠正措施，以改善决策和为所有居民公平提供服务。研究将分三个阶段进行。首先，六年的公民，人口普查和地理空间数据将与用户访谈相结合，然后进行分析，以发现“信息沙漠”的社会技术层面，这是概念和物理空间，当地信息在各种基础设施中嵌入不足和/或比城市其他地区更少。这项研究将建立一个概念模型，以确定信息沙漠的位置和方式，根据相关的社区特征确定信息沙漠的类型，并评估信息沙漠与数据偏差的主要人口和地理空间特征之间的关系。其次，研究团队将与公民利益相关者进行半结构化访谈，以收集用户对可视化分析工具的需求，并验证初始模型的基本事实。在此基础上，将创建一个可视化分析工具，以显示不同类型的信息沙漠，其原因和预期的结果。第三，通过迭代过程，研究团队将与市政官员和相关利益相关者开展参与式建模活动，以利用当地背景信息完善计算模型。此外，视觉分析工具的可用性将通过更多的用户研究得到改善。由此产生的信息沙漠的概念和计算模型将支持一个精致的视觉分析工具，该工具显示信息沙漠及其特征。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的知识价值进行评估而被认为值得支持影响审查标准。

This research will develop a foundational tool for understanding how civic technologies are used and how information inequalities manifest in a city. User data from new civic technologies that reveal inequalities in the information environments of citizens has only recently become available. Since a large portion of data is demographically or geospatially biased due to varying human-data relationships, computational social scientists have used data modeling and algorithmic techniques to adjust the data and remove biases during data-processing. However, this approach limits our understanding of how and why biased information is created, and our ability to address urban information inequalities and biased data-creation. Consequently, as cities transition to e-government enabled by information and communication technology, they may project the inequities of the past into the smart cities of the future, so a fresh approach is needed. This innovative research analyzes and visualizes data from Boston's 311 system for reporting non-emergency issues to the city government, using computational and qualitative approaches to identify, categorize, and understand the kinds of information disparities that are becoming institutionalized by crowdsourced municipal systems, inhibiting smart city transitions, and perpetuating information deserts. For Boston and its citizens, this research could improve both the function and the equity of the city's 311 system. The resulting insights and tools could also inform other cities' implementation of smart city technologies, identify potential distortions in existing urban datasets, and surface potential corrections that could improve decision making and equitable delivery of services for all residents. The research will be performed in three phases. First, six years of civic, census, and geospatial data will be combined with interviews with users, then analyzed to discover the socio-technical dimensions of "information deserts," which are conceptual and physical spaces where local information is poorly embedded in diverse infrastructures and/or less available than in other areas of a city. This research will develop a conceptual model to determine where and how information deserts are located, identify a typology of information deserts based on related community features; and, assess relationships between information deserts and major demographic and geospatial features of data biases. Second, the research team will perform semi-structured interviews with civic stakeholders to gather user requirements for a visual analytics tool as well as to validate the ground truths for the initial models. Based on this, a visual analytics tool will be created to show different types of information deserts, their causes, and anticipated results. Third, through an iterative process the research team will conduct participatory modeling activities with municipality officials and relevant stakeholders to refine the computational models with local contextual information. Also, the usability of the visual analytics tool will be improved with additional user studies. The resulting conceptual and computational models of information deserts will support a refined visual analytics tool that displays information deserts and their characteristics.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.