Abstract
Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority data in England whereby charts displaying multiple aspects related to the pandemic are given a geographic arrangement. These graphics are visually complex, with clutter, occlusion and salience bias an inevitable consequence. We develop a framework for describing and validating the graphics against data and design requirements. Together with an observational data analysis, this framework is used to evaluate our designs, relating them to particular data analysis needs based on the usefulness of the structure they expose. Our designs, documented in an accompanying code repository, attend to common difficulties in geovisualization design and could transfer to contexts outside of the UK and to phenomena beyond the pandemic.
Highlights
In a short period of time during the first wave of the pandemic, a wide range of visualization efforts were published and shared on-line, tracking its spread and trajectory in populations around the world [5]. Many of these analyses of area-level COVID-19 cases and deaths data attempt to grapple with a challenge familiar to geovisualization: how to compare the development of cases aggregated to area-level, whilst retaining the spatial context associated with those areas? Animation was used with great effect to communicate a sense of the pace of change and spread of cases across geographic areas [6,7] and was even used in government briefings [8]
A survey of recent glyphmap approaches for spatiotemporal analysis of COVID-19 cases data; Glyphmap designs for spatiotemporal analysis of cases data that meet our data and design requirements and that may transfer to other contexts, implemented using a high-level visualization grammar; encoding schematics, a novel means of describing design candidates, closely linked to their implementation, and which help draw attention to issues of data density and encoding effectiveness; claims around the likely effectiveness of our novel visualization designs in light of shifting data analysis needs related the pandemic
That spatial autocorrelation structure can be discerned even in the detailed 1D distribution of daily cases data further validates our approach of representing detailed case trajectories with a geographical arrangement. Though, this design is only feasible where the spatial units are regularly sized grid cells, as in our smwg, and sufficient space is available for a legible overloaded glyph in each cell; it would not transfer well to the US county data and the larger numbers of geographic areas used in the Washington Post graphic [19]. This paper adds both to applied and cartographic [17,50] literature analysing COVID19 and its spread and, more generally, to approaches in geovisualization aimed at visually analysing multivariate geospatial structure
Summary
In a short period of time during the first wave of the pandemic, a wide range of visualization efforts were published and shared on-line, tracking its spread and trajectory in populations around the world [5] Many of these analyses of area-level COVID-19 cases and deaths data attempt to grapple with a challenge familiar to geovisualization: how to compare the development of cases aggregated to area-level, whilst retaining the spatial context associated with those areas? DatR7 Cases relative to local ‘peak’—whether the daily growth in case numbers at a time point by area has reached its fastest recorded growth rate Additional to these are four Design Requirements (DesRs) to which our designs should adhere if they are to be successful in supporting detailed spatiotemporal analysis: DesR1 Concurrent—all data items must be shown simultaneously to support comparison, exploration and other synoptic tasks. A survey of recent glyphmap approaches for spatiotemporal analysis of COVID-19 cases data; Glyphmap designs for spatiotemporal analysis of cases data that meet our data and design requirements and that may transfer to other contexts, implemented using a high-level visualization grammar (ggplot2 [13]); encoding schematics, a novel means of describing design candidates, closely linked to their implementation, and which help draw attention to issues of data density and encoding effectiveness; claims around the likely effectiveness of our novel visualization designs in light of shifting data analysis needs related the pandemic
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