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UID:pretalx-foss4g-2022-DAST9N@talks.staging.osgeo.org
DTSTART;TZID=CET:20220825T175000
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DESCRIPTION:Tables are a great way to store data and this format is often u
 sed to make data available for the public on websites. While these tables 
 technically meet their intended goal of sharing data\, they do not make it
  easy to understand the spatial and temporal patterns in the data they con
 tain. In this talk\, I will demonstrate how an automated toolchain of web 
 scraping and text processing in R\, and interactive visualization in Leafl
 et is automated with GitHub Actions and applied to aid data interpretation
  and generate new insights from a daily-updated online tabular dataset usi
 ng a case study of the University of California Davis’ Potential Worksit
 e Exposure Reporting data for COVID-19. \nIn the United States\, Californi
 a Assembly Bill 685 (AB685) requires employers in the state of California 
 to notify employees of potential worksite exposures to COVID-19 to the geo
 graphic scale of individual buildings. The University of California Davis 
 meets this requirement by listing any potential exposures on a website\, g
 iving the date reported\, the dates of the potential exposure\, and the bu
 ilding name as reported by the employee. To make a map from this data\, th
 e dates and building names had to be standardized and joined to a vector l
 ayer of campus buildings before they can be added to an interactive Leafle
 t map. Because the data updates daily\, the whole process needed to be aut
 omated so no one had to run the scripts every day to update the map. The r
 esult is a map that gives uses a much clearer understanding of the spatial
  and temporal distribution of potential exposures to COVID-19 on campus.
DTSTAMP:20260411T202633Z
LOCATION:Room Limonaia
SUMMARY:Automating Generating a Web Map from Online Tabular Data: UC Davis 
 Potential Worksite Exposure Interactive Web Map - Michele Tobias
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/DAST9N/
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