{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:03:38Z","timestamp":1774451018217,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brazilian National Council for Scientific and Technological Development (CNPq)","award":["316963\/2021-6"],"award-info":[{"award-number":["316963\/2021-6"]}]},{"name":"Brazilian National Council for Scientific and Technological Development (CNPq)","award":["E-26\/202.915\/2019"],"award-info":[{"award-number":["E-26\/202.915\/2019"]}]},{"name":"Brazilian National Council for Scientific and Technological Development (CNPq)","award":["E-26\/211.134\/2019"],"award-info":[{"award-number":["E-26\/211.134\/2019"]}]},{"name":"FAPERJ","award":["316963\/2021-6"],"award-info":[{"award-number":["316963\/2021-6"]}]},{"name":"FAPERJ","award":["E-26\/202.915\/2019"],"award-info":[{"award-number":["E-26\/202.915\/2019"]}]},{"name":"FAPERJ","award":["E-26\/211.134\/2019"],"award-info":[{"award-number":["E-26\/211.134\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>Probability distributions are omnipresent in data analysis. They are often used to model the natural uncertainty present in real phenomena or to describe the properties of a data set. Designing efficient visual metaphors to convey probability distributions is, however, a difficult problem. This fact is especially true for geographical data, where conveying the spatial context constrains the design space. While many different alternatives have been proposed to solve this problem, they focus on representing data variability. However, they are not designed to support spatial analytical tasks involving probability quantification. The present work aims to adapt recent non-spatial approaches to the geographical context, in order to support probability quantification tasks. We also present a user study that compares the efficiency of these approaches in terms of both accuracy and usability.<\/jats:p>","DOI":"10.3390\/mti6070053","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:12:40Z","timestamp":1657757560000},"page":"53","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data"],"prefix":"10.3390","volume":"6","author":[{"given":"Sanjana","family":"Srabanti","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA"}]},{"given":"Carolina","family":"Veiga","sequence":"additional","affiliation":[{"name":"Instituto de Computa\u00e7\u00e3o, Universidade Federal Fluminense, Niter\u00f3i 24210-310, RJ, Brazil"}]},{"given":"Edcley","family":"Silva","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3868-8886","authenticated-orcid":false,"given":"Marcos","family":"Lage","sequence":"additional","affiliation":[{"name":"Instituto de Computa\u00e7\u00e3o, Universidade Federal Fluminense, Niter\u00f3i 24210-310, RJ, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-4609","authenticated-orcid":false,"given":"Nivan","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Centro de Inform\u00e1tica, Universidade Federal de Pernambuco, Recife 50740-560, PE, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8612-5805","authenticated-orcid":false,"given":"Fabio","family":"Miranda","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/MCSE.2016.7","article-title":"Reducing the analytical bottleneck for domain scientists: Lessons from a climate data visualization case study","volume":"18","author":"Dasgupta","year":"2016","journal-title":"Comput. 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