Covid-19 Mapping with Microsoft Power BI

Keywords: COVID19, map, Los Angeles, visualization, dashboard

Abstract

The purpose of the map and dashboard is twofold. First, Microsoft’s Power BI software presents a reasonably easy means by which many can make reasonably robust data visualizations with minimal expertise in mapping and programming languages. Unlike some competing data visualization and online mapping software packages that require specialized software or skills, ordinary citizens and students, new to data collection and management can create an appealing dashboard-style presentation within a few minutes with minimal guidance. Familiarity with the function and logic of spreadsheet software seems to be the only prerequisite. Many businesses, colleges, and universities already provide access to the Microsoft Office suite of software, further extending the appeal of using Power BI. The COVID-19 dashboard featuring case data by date, city, county, and / or neighborhood in Southern California was created in less than one-half hour using a connection to publicly available data shared by the Los Angeles Times via GitHub. The map allows visitors to select individual communities from a map, which prompts dynamically linked graphs and tables to display data for only that community. The second purpose of this dashboard is to allow users to rapidly analyze local data trends, perhaps following school openings / closings, changes in the status of businesses, or social protests movements. Epidemiologists have expressed concern over the effects of the protest marches and untimely changes in rules regarding businesses and schools on COVID-19 rates. Compounding the need for such a map are concerns about differential COVID-19 infection rates in communities of color, whose citizens were already at an elevated risk of death from COVID-19. This map and dashboard demonstrates that these concerns were not unfounded.

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Published
2020-10-31
How to Cite
Graves, S., & He, L. (2020). Covid-19 Mapping with Microsoft Power BI. Terra Digitalis, 4(2), 1-5. https://doi.org/10.22201/igg.25940694e.2020.2.74
Section
Articles with maps