Department of Geography & GIScience, University of Illinois at Urbana-Champaign, United States.
Department of Urban & Regional Planning, University of Illinois at Urbana-Champaign, United States.
Health Place. 2020 Mar;62:102282. doi: 10.1016/j.healthplace.2019.102282. Epub 2020 Jan 8.
Health researchers and policy-makers increasingly use volunteered geographic information (VGI) to analyze spatial variation in health and wellbeing and to develop interventions. As socially constructed data, health VGI reflect the people who perceive issues and choose to report them, and the digital systems that structure the reporting process. We propose a conceptual framework that describes the interlocking effects of socioeconomic, behavioral, geographic, and technological processes on VGI accuracy and credibility. GIS and statistical methods are used to analyze social and geographical biases in health-related VGI through a case study of bed bug complaint data from New York City's 311 system. Reports of bed bug infestation from 311 are mapped and modeled to uncover associations with socioeconomic and built environment characteristics. Factors associated with bed bug report credibility are examined by comparing characteristics of confirmed reports with those for reports in which inspectors found no evidence of infestation (negative reports). A multilevel model of credibility incorporating report-, building-, and tract-level variables reveals strong geographical and socioeconomic biases, with negative reports generated more frequently from high-value residential buildings located in high-income neighborhoods with predominately white, non-Hispanic populations. Using 311 data for all bed bug reports, rather than confirmed reports, obscures the burden of these pests in high poverty neighborhoods and diminishes socioeconomic disparities. Mistaken reporting also has economic costs, as each report triggers an inspection by city inspectors that entails time, monetary, and opportunity costs.
健康研究人员和政策制定者越来越多地使用志愿者地理信息 (VGI) 来分析健康和福利的空间变化，并开发干预措施。作为社会构建的数据，健康 VGI 反映了感知问题并选择报告问题的人，以及构建报告过程的数字系统。我们提出了一个概念框架，描述了社会经济、行为、地理和技术过程对 VGI 准确性和可信度的相互影响。通过对纽约市 311 系统的臭虫投诉数据的案例研究，使用 GIS 和统计方法来分析与健康相关的 VGI 中的社会和地理偏差。臭虫侵扰报告通过映射和建模进行分析，以揭示与社会经济和建筑环境特征的关联。通过比较检查员发现无虫害证据（阴性报告）的报告与确认报告的特征，检查与臭虫报告可信度相关的因素。一个包含报告、建筑物和地段级变量的可信度多层模型揭示了强烈的地理和社会经济偏见，负面报告更多地来自位于高收入社区、以白人为主、非西班牙裔人口为主的高价值住宅建筑。使用所有臭虫报告的 311 数据，而不是确认报告，掩盖了这些害虫在高贫困社区的负担，并减少了社会经济差距。错误报告也会带来经济成本，因为每个报告都会触发城市检查员的检查，这需要时间、金钱和机会成本。
