{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:02:11Z","timestamp":1760709731039,"version":"3.38.0"},"reference-count":29,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T00:00:00Z","timestamp":1539561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Information Visualization"],"published-print":{"date-parts":[[2019,10]]},"abstract":"<jats:p> We investigated how different graphical representations convey the underlying uncertainty distribution in ensemble predictions. In ensemble predictions, a set of forecasts is produced, indicating the range of possible future states. Adopting a use case from life sciences, we asked non-expert participants to compare ensemble predictions of the growth distribution of individual children to that of the normal population. For each individual child, the historical growth data of a set of 20 of its best matching peers was adopted as the ensemble prediction of the child\u2019s growth curve. The ensemble growth predictions were plotted in seven different graphical formats (an ensemble plot, depicting all 20 forecasts and six summary representations, depicting the peer group mean and standard deviation). These graphs were plotted on a population chart with a given mean and variance. For comparison, we included a representation showing only the initial part of the growth curve without any future predictions. For 3\u2009months old children that were measured at four occasions since birth, participants predicted their length at the age of 2\u2009years. They compared their prediction to either (1) the population mean or to (2) a \u201cnormal\u201d population range (the mean\u2009\u00b1\u20092(standard deviation)). Our results show that the interpretation of a given uncertainty visualization depends on its visual characteristics, on the type of estimate required and on the user\u2019s numeracy. Numeracy correlates negatively with bias (mean response error) magnitude (i.e. people with lower numeracy show larger response bias). Compared to the summary plots that yield a substantial overestimation of probabilities, and the No-prediction representation that results in quite variable predictions, the Ensemble representation consistently shows a lower probability estimation, resulting in the smallest overall response bias. The current results suggest that an Ensemble or \u201cspaghetti plot\u201d representation may be the best choice for communicating the uncertainty in ensemble predictions to non-expert users. <\/jats:p>","DOI":"10.1177\/1473871618807121","type":"journal-article","created":{"date-parts":[[2018,10,15]],"date-time":"2018-10-15T10:36:01Z","timestamp":1539599761000},"page":"373-383","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Graphical uncertainty representations for ensemble predictions"],"prefix":"10.1177","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1051-5422","authenticated-orcid":false,"given":"Alexander","family":"Toet","sequence":"first","affiliation":[{"name":"Department of Perceptual and Cognitive Systems, TNO, Soesterberg, The Netherlands"}]},{"given":"Jan BF","family":"van Erp","sequence":"additional","affiliation":[{"name":"Department of Perceptual and Cognitive Systems, TNO, Soesterberg, The Netherlands"},{"name":"Research Group Human Media Interaction, University of Twente, Enschede, The Netherlands"}]},{"given":"Erik M","family":"Boertjes","sequence":"additional","affiliation":[{"name":"Department of Data Science, TNO, The Hague, The Netherlands"}]},{"given":"Stef","family":"van Buuren","sequence":"additional","affiliation":[{"name":"Department of Statistics, TNO, Leiden, The Netherlands"},{"name":"Department of Methodology & Statistics, Faculty of Social and Behavioural Sciences, University of Utrecht, Utrecht, The Netherlands"}]}],"member":"179","published-online":{"date-parts":[[2018,10,15]]},"reference":[{"key":"bibr1-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1167\/16.5.11"},{"key":"bibr2-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2013.247"},{"key":"bibr3-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1177\/1555343415591275"},{"key":"bibr4-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1177\/1090198109341533"},{"key":"bibr5-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9280.2006.01720.x"},{"key":"bibr6-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1037\/a0017327"},{"key":"bibr7-1473871618807121","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1017\/S1930297500006008","volume":"8","author":"Rinne LF","year":"2013","journal-title":"Judgm Decis Mak"},{"key":"bibr8-1473871618807121","first-page":"650","volume":"9","author":"Hermanussen M","year":"2012","journal-title":"Pediatr Endocr Rev"},{"key":"bibr9-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1159\/000365398"},{"first-page":"191","volume-title":"Proceedings of the 11th international conference on information visualization (IV \u201907)","author":"Buono P","key":"bibr10-1473871618807121"},{"key":"bibr11-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1186\/s41235-017-0076-1"},{"key":"bibr12-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1080\/13875868.2015.1137577"},{"key":"bibr13-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2607204"},{"key":"bibr14-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1016\/j.envhaz.2007.05.001"},{"key":"bibr15-1473871618807121","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.9419"},{"key":"bibr16-1473871618807121","unstructured":"Kootval H. 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