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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. [ABSTRACT FROM AUTHOR]

The Zones of Confidence (ZOC) is a composite data quality indicator used in Electronic Navigational Charts. Accident reports show that failing to account for chart data quality can result in maritime accidents and loss of life. ZOC overlays are intended to help mariners in identifying potential seafloor hazards and in plotting routes safe for the vessel, but a major concern with the ZOC concept has been the utilized symbology with glyphs consisting of stars. Due to its recognized deficiencies, star-symbology has been rejected for use with the successor of ZOC, the Quality of Bathymetric Data (QoBD). This work presents a research effort toward a new QoBD representation. We define the requirements for the new coding scheme to be effective and propose two texture schemes incorporating countable elements, one consisting of lines and one of dot clusters. For comparison, we developed three alternative, color-based, coding schemes based on ideas previously expressed in the maritime community. Lastly, we present the design, dissemination, and results of an online user survey carried out to evaluate the five coding schemes. The survey results demonstrate that the proposed textures are the most preferred coding schemes among survey respondents. [ABSTRACT FROM AUTHOR]

Geographic Information Systems (GIS) are valuable for displaying and analyzing spatial data, revealing spatial patterns that may otherwise go unnoticed. The ubiquity of web and mobile platforms, used to create and share geographic information has reified the value of GIS to a wider audience bringing new popularity to GIS. As a result, GIS and Geographic Information Science (GIScience) are becoming increasingly sought after in interdisciplinary research, which often addresses multifaceted, real-world problems. To understand the necessary level of GIS expertise for a specific interdisciplinary research project, we suggest that the team early on identify the role of GIS within the research. Is it simply the use of GIS as a Tool, employing a GIS Toolmaker for bespoke applications or a GIScientist for new forms of spatial analyses and guidance on social, ethical and spatial ambiguities? To allay future challenges and miscommunication in interdisciplinary research involving GIS, three case studies are presented and common themes related to challenges for the GIScientists are shared. [ABSTRACT FROM AUTHOR]

The communication of data uncertainty is a crucial problem in data science, information visualization, and geographic information science (GIScience). Effective ways to communicate the uncertainty of data enables data consumers to interpret the data as intended by the producer, reducing the possibilities of misinterpretation. In this article, we report on an empirical investigation of how sound can be used to convey information about data uncertainty in an intuitive way. To answer the research question How intuitive are sound dimensions to communicate uncertainty? we carry out a cognitive experiment, where participants were asked to interpret the certainty/uncertainty level in two sounds A and B (N = 33). We produce sound stimuli by varying sound dimensions, including loudness, duration, location, pitch, register, attack, decay, rate of change, noise, timbre, clarity, order, and harmony. In the stimuli, both synthetic and natural sounds are used to allow comparison. The experiment results identify three sound dimensions (loudness, order, and clarity) as significantly more intuitive to communicate uncertainty, providing guidelines for sonification and information visualization practitioners. [ABSTRACT FROM AUTHOR]

We review recent developments in cartographic research in North America, in the context of informing the 29th International Cartographic Conference, and 18th General Assembly in 2019. The titles of papers published since 2015 in four leading cartographic journals yielded a corpus of 245 documents containing 1109 unique terms. These terms were analyzed using Latent Dirichlet Allocation and by visual analytics to produce 14 topic groups that mapped onto five classes. These classes were named as information visualization, cartographic data, spatial analysis and applications, methods and models, and GIScience. The classes were then used as themes to discuss the recent cartographic literature more broadly, first, to review recent trends in the research and to identify research gaps, and second, to examine prospects for new research over the next 20 years. A conclusion draws some broad findings from the review, suggesting that cartographic research in the future will be aimed less at dealing with data, and more at generating insight and knowledge to better inform society about global challenges. [ABSTRACT FROM AUTHOR]

Transformations, for many the core of analytical cartography, involve manipulations of semiotics and semantics. To develop a framework for contemporary geovisualization, we draw on concepts behind Jacques Bertin's lesser known work among Anglo-American cartographers, La graphique et le traitement graphique de l'information (1977), translated 1981 as Graphics and graphic information-processing. This book describes the transformations of semiotics and semantics in a matrix-based process to create graphics, especially geovisualizations. It offers a logical development from concepts presented in the semiology of graphics and took up semantics and communication aspects. The framework we develop starts with the insights from this book but moves beyond Bertin's questionable assumption about the necessity of a prior accord between map producer and map reader regarding semiotics and semantics for successful communication. We reconsider the involved semiotic and semantic manipulations for the discursive analysis of geovisualizations beginning with Barbara Petchenik's insight that cartographic communication accounts for biases and physiological limits. We broaden these considerations to include distributed cognition concepts from Edward Hutchins, which account for preferences and physiological limitations regarding their broader institutional and cultural settings. The revised linkage of semantics and semiotics, which understands map design as a process of developing boundary objects, focuses on the transformations for making geovisualizations. This analysis holds relevance for improvements to geovisualization and the development of enhancements for cartographic design. [ABSTRACT FROM AUTHOR]

In this article, we explore the potential of wireframe design and evaluation for interactive and web-based mapping through a case study on water level visualization. Specifically, our research informed design and development of the National Oceanic & Atmospheric Administration’s (NOAA) Lake Level Viewer (http://coast.noaa.gov/llv/), an interactive and web-based geovisualization application for the Great Lakes region of North America. As part of our overall user-centered design process, we created two sets of wireframes to evaluate two aspects of the user experience:high-fidelitywireframes to illustrate the proposedrepresentationsolution using real data andlow-fidelitywireframes to provide a rough sketch of the proposedinteractionsolution. Eighteen target users completed cognitive walkthroughs of the wireframes, with the sessions audio-recorded for subsequent transcription and qualitative data analysis. The wireframe evaluations led to a series of revisions to the functional scope and visual design of the Lake Level Viewer. The process also generated recommendations for designing water level visualizations supporting adaptive management in response to climate change as well as for leveraging wireframes in support of large-scale mapping and GIS projects. [ABSTRACT FROM PUBLISHER]

The inherent uncertainty of geospatial data has engendered a critical research agenda addressing all facets of uncertainty visualization due to the communicative efficiency of graphical representation. To organize this broad research area, we have reviewed literature ongeospatialuncertainty visualization and systematically and iteratively classified research in this field. Upon creating a classification, we developed several visual summaries over time, refining the classification and subsequent graphic as new relevant topics emerged. This visual summary extends current existing approaches to taxonomies by allowing users a quick visual overview of relevant topics in a research area at a glance. For each research paper on uncertainty visualization, this classification can be used to visually represent which domains are covered. In order to ensure that the visual summary approach and the corresponding domains developed in this article can be used reliably, we performed an inter-rater agreement task. The high agreement reveals that the domains in the classification that were identified are intuitive and can lead to objective, reproducible classifications (visual summaries) of research papers. In future research, we plan to refine the visual classification/summary approach by providing guided classification via a web interface to visually classify the entire body of literature on geospatial uncertainty visualization and visually explore any trends in research topics, how they have changed over the years, and identify sparser topics that still need to be addressed. [ABSTRACT FROM PUBLISHER]

Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification. [ABSTRACT FROM AUTHOR]

For many years, uncertainty visualization has been a topic of research in several disparate fields, particularly in geographical visualization (geovisualization), information visualization, and scientific visualization. Multiple techniques have been proposed and implemented to visually depict uncertainty, but their evaluation has received less attention by the research community. In order to understand how uncertainty visualization influences reasoning and decision-making using spatial information in visual displays, this paper presents a comprehensive review of uncertainty visualization assessments from geovisualization and related fields. We systematically analyze characteristics of the studies under review, i.e., number of participants, tasks, evaluation metrics, etc. An extensive summary of findings with respect to the effects measured or the impact of different visualization techniques helps to identify commonalities and differences in the outcome. Based on this summary, we derive “lessons learned” and provide recommendations for carrying out evaluation of uncertainty visualizations. As a basis for systematic evaluation, we present a categorization of research foci related to evaluating the effects of uncertainty visualization on decision-making. By assigning the studies to categories, we identify gaps in the literature and suggest key research questions for the future. This paper is the second of two reviews on uncertainty visualization. It follows the first that covers the communication of uncertainty, to investigate the effects of uncertainty visualization on reasoning and decision-making. [ABSTRACT FROM PUBLISHER]

Social media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS. [ABSTRACT FROM AUTHOR]