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

CHS：小型：协作研究：使信息沙漠可见：计算模型、公民技术使用的差异和城市决策

• 批准号: 1816080
• 负责人: Erik Johnston
• 金额: $ 15.54万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816080&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Making

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.

这项研究将开发一种基础工具，以了解如何使用公民技术以及信息不平等的表现。 来自新的公民技术的用户数据揭示了公民信息环境中不平等现象，直到最近才可以使用。由于大部分数据在人口统计学或地理空间上是由于不同的人数据关系而产生的，因此计算社会科学家使用了数据建模和算法技术来调整数据并在数据处理过程中消除偏见。但是，这种方法限制了我们对创建偏见信息的理解，以及我们解决城市信息不平等和偏见的数据创造的能力。因此，随着城市通过信息和通信技术实现的电子政务的过渡，它们可能会将过去的不平等投射到未来的智能城市中，因此需要一种新的方法。这项创新的研究分析了波士顿311系统的数据，以使用计算和定性方法向市政府向市政府报告非紧急性问题，以识别，分类和理解众包市政系统已经制度化的信息差异，从而抑制了智能的城市过渡，从而抑制了智能的城市过渡，并抑制了信息的信息。对于波士顿及其公民来说，这项研究可以改善该市311系统的功能和权益。由此产生的见解和工具也可以为其他城市的智能城市技术实施，确定现有城市数据集的潜在扭曲以及可以改善决策做出和公平提供所有居民服务的潜在扭曲。该研究将分为三个阶段。首先，将六年的公民，人口普查和地理空间数据与对用户的访谈结合在一起，然后进行分析，以发现“信息荒漠”的社会技术维度，这些概念和物理空间是本地信息在多种基础设施中不良嵌入的概念和物理空间，而不是在城市的其他地区可用。这项研究将开发一个概念模型，以确定信息荒漠的位置和方式，根据相关社区特征确定信息沙漠的类型；并且，评估信息沙漠与数据偏见的主要人口统计和地理空间特征之间的关系。其次，研究团队将对公民利益相关者进行半结构化访谈，以收集视觉分析工具的用户要求，并为初始模型验证地面真相。基于此，将创建一个视觉分析工具，以显示不同类型的信息沙漠，其原因和预期结果。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进行参与式建模活动，以使用本地上下文信息来完善计算模型。此外，通过其他用户研究，将提高视觉分析工具的可用性。由此产生的信息沙漠的概念和计算模型将支持一种精致的视觉分析工具，该工具显示了信息沙漠及其特征。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估评估的评估值得支持的。

项目成果

For impactful community engagement: check your role

为了进行有影响力的社区参与：检查您的角色

• DOI: 10.1145/3401720
• 发表时间: 2020
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: Pine, Kathleen H.;Hinrichs, Margaret M.;Wang, Jieshu;Lewis, Dana;Johnston, Erik
• 通讯作者: Johnston, Erik