Seeing through the Crowds: Crowdmaps Visualize User-Reported Data for Public Health
by Maged N. Kamel Boulos, Geoplace.com, July 9, 2012
Crowd-sensing and citizen reporting of incidents are becoming increasingly common, with applications ranging from air-quality monitoring to building a database of all the Automated External Defibrillators in a major city (www.uphs.upenn.edu/news/News_Releases/2011/12/myheartmap-challenge) to protest movements, political activism and citizen journalism, as witnessed in the 2011/2012 “Occupy Movement” and “Arab Spring” events.A comprehensive review of the main technologies and standards involved in this domain was published in the International Journal of Health Geographics in December 2011 (dx.doi.org/10.1186/1476-072X-10-67). This article, however, focuses on the use of “crowdmaps” for visualizing crowdsourced data. Crowd-generated reports and other material often produce Big Data: large, continuous streams of data that pose major challenges when trying to visualize, understand and make sense of them, particularly when attempting to do so in real time. This article presents several examples of crowdmaps, covering a diverse range of topics in which the spatiotemporal distribution (and content) of the corresponding crowdsourced data are displayed on a familiar, interactive (geographic) map interface.
For full text of the article, visit Seeing through the Crowds: Crowdmaps Visualize User-Reported Data | Articles – Publishing Titles | GeoPlace.
Dr. Lea Shanley is the founder and former co-Chair of the Federal Community of Practice on Crowdsourcing and Citizen Science, a vibrant community of 200 federal employees from more than 35 agencies. She is also a co-founding member of the Citizen Science Association. Dr. Shanley recently served as a Presidential Innovation Fellow at NASA, where she helped to foster a culture of open innovation. Prior to this, she founded and directed the Commons Lab at the Wilson Center, served in the US Senate as a Congressional Science Fellow, and worked with local and tribal communities to develop GIS-based decision support systems for city planning, natural resource management, coastal management, and disaster response through the University of Wisconsin-Madison.
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