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While control room design often incorporates human factors engineering to identify the information presented, it is not unusual for control room visual encodings to rely closely on operator experience and historical encodings, which may be contrary to recommended guidelines from the visualization literature. Such approaches may contribute to the development of sub\u2010optimal operator mental models of the systems they monitor. In this article, we describe a design study in which we assessed the control room context from a visualization perspective and identified a set of 15 visualization guidelines, matching each of these to the domain requirements of the control room. Inspired by these guidelines, we design a visual encoding for alerts occurring on a power system schematic single line diagram, which is commonly used to identify and respond to system alerts arising from faults and conditions that require operator attention in power system control rooms. We report on the results of an experiment conducted with both domain experts and non\u2010experts. Our results show support for designs that follow our visualization guidelines via faster response times. Accuracy is improved in cases where information is directly encoded by our designs. We also find increased accuracy for domain experts, but expert response times were biased towards more familiar visual encodings in comparison to non\u2010experts. This emphasizes the importance of established mental maps for control room operators and the need for careful change management in updating control room visualizations.<\/jats:p>","DOI":"10.1111\/cgf.70458","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:14:08Z","timestamp":1781763248000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Visualization Guidelines for the Control Room: An Evaluation of Visual Alert Encoding with Domain Experts"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6146-7147","authenticated-orcid":false,"given":"P.","family":"Baudains","sequence":"first","affiliation":[{"name":"Department of Informatics King's College London"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1919-7726","authenticated-orcid":false,"given":"A.I.","family":"Meso","sequence":"additional","affiliation":[{"name":"Institute of Psychiatry Psychology and Neuroscience King's College London"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9411-0726","authenticated-orcid":false,"given":"G.","family":"Todeschini","sequence":"additional","affiliation":[{"name":"Department of Engineering King's College London  London United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2875-6793","authenticated-orcid":false,"given":"R.","family":"Borgo","sequence":"additional","affiliation":[{"name":"Department of Informatics King's College London"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/1473871611415997"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.3758\/s13414-024-02903-9"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0018720812452592"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.812677"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/0018720819835990"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2012.251"},{"key":"e_1_2_11_8_2","unstructured":"BorgoR. 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