Cecilia Smith and Marynia Kolak

Studying the geography of health indicators in the context of socioeconomic data enables researchers to mitigate, predict, and plan for future crises. Spatial data visualization is an essential first step that facilitates the exploration of current patterns and emerging trends in public health, generating and refining hypotheses. This chapter discusses quantitative thematic maps as powerful tools for assessing and disseminating information about public health. In addition to providing a foundation for the primary types of thematic maps, this chapter reviews the key spatial literacy skills for interpreting and creating authoritative maps using public health and socioeconomic data. The authors draw on examples from the U.S. Covid Atlas, a project that visualizes COVID-19 health outcomes and community context over the course of the pandemic, to demonstrate the impact of thematic map type and choice of data classification, resolution, and scale on discerning real-world trends.

Qinyun Lin, Susan Paykin, Dylan Halpern, Aresha Martinez-Cardoso, and Marynia Kolak

Question How do the associations between structural factors and COVID-19 mortality help explain the disproportionate outcomes experienced by different racial and ethnic groups?
Findings In this cross-sectional study of 3142 counties in 50 US states and the District of Columbia, the associations between different measures of social determinants of health and COVID-19 mortality varied across racial and ethnic groups (Black or African American, Hispanic or Latinx, and non-Hispanic White populations) and different community types (rural, suburban, and urban areas).
Meaning Findings from this study suggest the need for future research that addresses health inequity and guides policies and programs by further exploring the different dimensions and regional patterns of social determinants of health.

Dylan Halpern, Qinyun Lin, Ryan Wang, Stephanie Yang, Steve Goldstein, and Marynia Kolak

COVID-19 surveillance across the United States is essential to tracking and mitigating the pandemic, but data representing cases and deaths may be impacted by attribute, spatial, and temporal uncertainties. COVID-19 case and death data are essential to understanding the pandemic and serve as key inputs for prediction models that inform policy-decisions; consistent information across datasets is critical to ensuring coherent findings. We implement an exploratory data analytic approach to characterize, synthesize, and visualize spatial-temporal dimensions of uncertainty across commonly used datasets for case and death metrics (Johns Hopkins University, the New York Times, USAFacts, and 1Point3Acres). We scrutinize data consistency to assess where and when disagreements occur, potentially indicating underlying uncertainty. We observe differences in cumulative case and death rates to highlight discrepancies and identify spatial patterns. Data are assessed using pairwise agreement (Cohen’s kappa) and agreement across all datasets (Fleiss’ kappa) to summarize changes over time. Findings suggest highest agreements between CDC, JHU, and NYT datasets. We find nine discrete type-components of information uncertainty for COVID-19 datasets reflecting various complex processes. Understanding processes and indicators of uncertainty in COVID-19 data reporting is especially relevant to public health professionals and policymakers to accurately understand and communicate information about the pandemic.

Marynia Kolak, Xun Li, Qinyun Lin, Ryan Wang, Moksha Menghaney, Stephanie Yang, Vidal Anguiano Jr

Distributed spatial infrastructures leveraging cloud computing technologies can tackle issues of disparate data sources and address the need for data-driven knowledge discovery and more sophisticated spatial analysis central to the COVID-19 pandemic. We implement a new, open source spatial middleware component (libgeoda) and system design to scale development quickly to effectively meet the need for surveilling county-level metrics in a rapidly changing pandemic landscape. We incorporate, wrangle, and analyze multiple data streams from volunteered and crowdsourced environments to leverage multiple data perspectives. We integrate explorative spatial data analysis (ESDA) and statistical hotspot standards to detect infectious disease clusters in real time, building on decades of research in GIScience and spatial statistics. We scale the computational infrastructure to provide equitable access to data and insights across the entire USA, demanding a basic but high-quality standard of ESDA techniques. Finally, we engage a research coalition and incorporate principles of user-centered design to ground the direction and design of Atlas application development.