Part-Whole.bib

@article{alimGradientEstimationRevitalized2010,
  title = {Gradient {{Estimation Revitalized}}},
  author = {Alim, Usman and M{\"o}ller, Torsten and Condat, Laurent},
  year = {2010},
  month = nov,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {16},
  number = {6},
  pages = {1495--1504},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2010.160},
  urldate = {2024-03-04},
  abstract = {We investigate the use of a Fourier-domain derivative error kernel to quantify the error incurred while estimating the gradient of a function from scalar point samples on a regular lattice. We use the error kernel to show that gradient reconstruction quality is significantly enhanced merely by shifting the reconstruction kernel to the centers of the principal lattice directions. Additionally, we exploit the algebraic similarities between the scalar and derivative error kernels to design asymptotically optimal gradient estimation filters that can be factored into an infinite impulse response interpolation prefilter and a finite impulse response directional derivative filter. This leads to a significant performance gain both in terms of accuracy and computational efficiency. The interpolation prefilter provides an accurate scalar approximation and can be re-used to cheaply compute directional derivatives on-the-fly without the need to store gradients. We demonstrate the impact of our filters in the context of volume rendering of scalar data sampled on the Cartesian and Body-Centered Cubic lattices. Our results rival those obtained from other competitive gradient estimation methods while incurring no additional computational or storage overhead.},
  keywords = {Approximation,Body Centered Cubic Lattice,Derivative,Estimation,Frequency Error Kernel,Gradient,Image reconstruction,Interpolation,Kernel,Lattice,Lattices,Reconstruction,Sampling,Spline},
  file = {/Users/cbbcbail/Zotero/storage/LRRCI85D/Alim et al. - 2010 - Gradient Estimation Revitalized.pdf}
}

@article{bezerianosPerceptionVisualVariables2012,
  title = {Perception of {{Visual Variables}} on {{Tiled Wall-Sized Displays}} for {{Information Visualization Applications}}},
  author = {Bezerianos, Anastasia and Isenberg, Petra},
  year = {2012},
  month = dec,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {18},
  number = {12},
  pages = {2516--2525},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2012.251},
  urldate = {2024-03-05},
  abstract = {We present the results of two user studies on the perception of visual variables on tiled high-resolution wall-sized displays. We contribute an understanding of, and indicators predicting how, large variations in viewing distances and viewing angles affect the accurate perception of angles, areas, and lengths. Our work, thus, helps visualization researchers with design considerations on how to create effective visualizations for these spaces. The first study showed that perception accuracy was impacted most when viewers were close to the wall but differently for each variable (Angle, Area, Length). Our second study examined the effect of perception when participants could move freely compared to when they had a static viewpoint. We found that a far but static viewpoint was as accurate but less time consuming than one that included free motion. Based on our findings, we recommend encouraging viewers to stand further back from the display when conducting perception estimation tasks. If tasks need to be conducted close to the wall display, important information should be placed directly in front of the viewer or above, and viewers should be provided with an estimation of the distortion effects predicted by our work-or encouraged to physically navigate the wall in specific ways to reduce judgement error.},
  keywords = {Data visualization,Information analysis,Information visualization,Navigation,perception,Visual analytics,wall-displays},
  file = {/Users/cbbcbail/Zotero/storage/DBDPJGWU/Bezerianos and Isenberg - 2012 - Perception of Visual Variables on Tiled Wall-Sized.pdf;/Users/cbbcbail/Zotero/storage/F3F9BKZR/6327257.html}
}

@book{brintonGraphicMethodsPresenting1914,
  title = {Graphic Methods for Presenting Facts},
  author = {Brinton, Willard Cope},
  year = {1914},
  publisher = {New York, The Engineering Magazine Company},
  urldate = {2024-03-18},
  abstract = {xii p., 1 l., 371 p. 26 cm},
  collaborator = {{Prelinger Library}},
  langid = {english},
  lccn = {7019},
  keywords = {Graphic methods}
}

@book{brintonGraphicPresentation1939,
  title = {Graphic Presentation},
  author = {Brinton, Willard Cope},
  year = {1939},
  publisher = {New York city, Brinton associates},
  urldate = {2024-03-12},
  abstract = {512p. 23cm; Diagram on lining-papers; "First edition"},
  collaborator = {{Prelinger Library}},
  langid = {english},
  lccn = {7020},
  keywords = {Graphic methods}
}

@article{clevelandGraphicalPerceptionGraphical1985,
  title = {Graphical {{Perception}} and {{Graphical Methods}} for {{Analyzing Scientific Data}}},
  author = {Cleveland, William S. and McGill, Robert},
  year = {1985},
  journal = {Science},
  volume = {229},
  number = {4716},
  eprint = {1695272},
  eprinttype = {jstor},
  pages = {828--833},
  publisher = {American Association for the Advancement of Science},
  issn = {0036-8075},
  urldate = {2024-03-12},
  abstract = {Graphical perception is the visual decoding of the quantitative and qualitative information encoded on graphs. Recent investigations have uncovered basic principles of human graphical perception that have important implications for the display of data. The computer graphics revolution has stimulated the invention of many graphical methods for analyzing and presenting scientific data, such as box plots, two-tiered error bars, scatterplot smoothing, dot charts, and graphing on a log base 2 scale.},
  file = {/Users/cbbcbail/Zotero/storage/NIT4MSGN/Cleveland and McGill - 1985 - Graphical Perception and Graphical Methods for Ana.pdf}
}

@article{clevelandGraphicalPerceptionTheory1984,
  title = {Graphical {{Perception}}: {{Theory}}, {{Experimentation}}, and {{Application}} to the {{Development}} of {{Graphical Methods}}},
  shorttitle = {Graphical {{Perception}}},
  author = {Cleveland, William S. and McGill, Robert},
  year = {1984},
  journal = {Journal of the American Statistical Association},
  volume = {79},
  number = {387},
  eprint = {2288400},
  eprinttype = {jstor},
  pages = {531--554},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  issn = {0162-1459},
  doi = {10.2307/2288400},
  urldate = {2024-03-18},
  abstract = {The subject of graphical methods for data analysis and for data presentation needs a scientific foundation. In this article we take a few steps in the direction of establishing such a foundation. Our approach is based on graphical perception-the visual decoding of information encoded on graphs-and it includes both theory and experimentation to test the theory. The theory deals with a small but important piece of the whole process of graphical perception. The first part is an identification of a set of elementary perceptual tasks that are carried out when people extract quantitative information from graphs. The second part is an ordering of the tasks on the basis of how accurately people perform them. Elements of the theory are tested by experimentation in which subjects record their judgments of the quantitative information on graphs. The experiments validate these elements but also suggest that the set of elementary tasks should be expanded. The theory provides a guideline for graph construction: Graphs should employ elementary tasks as high in the ordering as possible. This principle is applied to a variety of graphs, including bar charts, divided bar charts, pie charts, and statistical maps with shading. The conclusion is that radical surgery on these popular graphs is needed, and as replacements we offer alternative graphical forms-dot charts, dot charts with grouping, and framed-rectangle charts.},
  file = {/Users/cbbcbail/Zotero/storage/6WYNCPNR/Cleveland and McGill - 1984 - Graphical Perception Theory, Experimentation, and.pdf}
}

@article{clevelandGraphicalPerceptionVisual1987,
  title = {Graphical {{Perception}}: {{The Visual Decoding}} of {{Quantitative Information}} on {{Graphical Displays}} of {{Data}}},
  shorttitle = {Graphical {{Perception}}},
  author = {Cleveland, William S. and McGill, Robert},
  year = {1987},
  journal = {Journal of the Royal Statistical Society. Series A (General)},
  volume = {150},
  number = {3},
  eprint = {2981473},
  eprinttype = {jstor},
  pages = {192--229},
  publisher = {[Royal Statistical Society, Wiley]},
  issn = {0035-9238},
  doi = {10.2307/2981473},
  urldate = {2024-03-18},
  abstract = {Studies in graphical perception, both theoretical and experimental, provide a scientific foundation for the construction area of statistical graphics. From these studies a paradigm that has important applications for practice has begun to emerge. The paradigm is based on elementary codes: Basic geometric and textural aspects of a graph that encode the quantitative information. The methodology that can be invoked to study graphical perception is illustrated by an investigation of the shape parameter of a two-variable graph, a topic that has had much discussion, but little scientific study, for at least 70 years.},
  file = {/Users/cbbcbail/Zotero/storage/364Z8U77/Cleveland and McGill - 1987 - Graphical Perception The Visual Decoding of Quant.pdf}
}

@article{converseRoleProminentNumbers2018,
  title = {The Role of ``{{Prominent Numbers}}'' in Open Numerical Judgment: {{Strained}} Decision Makers Choose from a Limited Set of Accessible Numbers},
  shorttitle = {The Role of ``{{Prominent Numbers}}'' in Open Numerical Judgment},
  author = {Converse, Benjamin A. and Dennis, Patrick J.},
  year = {2018},
  month = jul,
  journal = {Organizational Behavior and Human Decision Processes},
  volume = {147},
  pages = {94--107},
  issn = {0749-5978},
  doi = {10.1016/j.obhdp.2018.05.007},
  urldate = {2024-02-14},
  abstract = {Numerate adults can represent an infinite array of integers. When a judgment requires them to ``pick a number,'' how do they select one to represent the abstract signal in mind? Drawing from research on the cognitive psychology of number representation, we conjecture that judges who operate primarily in decimal systems simplify by initially selecting from a set of chronically accessible ``Prominent Numbers'' defined as the powers of ten, their doubles, and their halves [{\dots} 5, 10, 20, 50, 100, 200{\dots}]; then, when willing and able, refining from there. A sample of 3\,billion stock trades reveals that traders' decisions reflect Prominent-Number clustering (Study 1) and a ``natural experiment'' reveals more clustering in rushed trading conditions (Study 2). Three sets of subsequent studies provide evidence consistent with an accessibility-based account of Prominent-Number usage: Experiments show that judges rely more on Prominent Numbers when they are induced to rush rather than take their time (Studies 3a and 3b), and when they are under high versus low cognitive load (Studies 4a, 4b, and 4c); and a final correlational study shows that Prominent-Number clustering is more apparent for judgments that require judges to scan a wider range of plausible values (Study 5). This work underscores the need to differentiate between Round Numbers and Prominent Numbers, and between representational properties of graininess and accessibility, in numerical judgment.},
  keywords = {Decision making,Judgment,Number representation,Prominent numbers,Round numbers},
  file = {/Users/cbbcbail/Zotero/storage/H74ZCNFK/Converse and Dennis - 2018 - The role of “Prominent Numbers” in open numerical .pdf}
}

@article{croxtonBarChartsCircle1927,
  title = {Bar {{Charts Versus Circle Diagrams}}},
  author = {Croxton, Frederick E. and Stryker, Roy E.},
  year = {1927},
  journal = {Journal of the American Statistical Association},
  volume = {22},
  number = {160},
  eprint = {2276829},
  eprinttype = {jstor},
  pages = {473--482},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  issn = {0162-1459},
  doi = {10.2307/2276829},
  urldate = {2024-03-13},
  file = {/Users/cbbcbail/Zotero/storage/RE9JZ59I/Croxton and Stryker - 1927 - Bar Charts Versus Circle Diagrams.pdf}
}

@article{croxtonGraphicComparisonsBars1932a,
  title = {Graphic {{Comparisons}} by {{Bars}}, {{Squares}}, {{Circles}}, and {{Cubes}}},
  author = {Croxton, Frederick E.},
  year = {1932},
  journal = {Journal of the American Statistical Association},
  volume = {27},
  number = {177},
  eprint = {2277880},
  eprinttype = {jstor},
  pages = {54--60},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  issn = {0162-1459},
  doi = {10.2307/2277880},
  urldate = {2024-03-18},
  file = {/Users/cbbcbail/Zotero/storage/QSQ8VGE2/Croxton - 1932 - Graphic Comparisons by Bars, Squares, Circles, and.pdf}
}

@article{culbertsonStudyGraphComprehension1959,
  title = {A {{Study}} of {{Graph Comprehension Difficulties}}},
  author = {Culbertson, Hugh M. and Powers, Richard D.},
  year = {1959},
  journal = {Audio Visual Communication Review},
  volume = {7},
  number = {2},
  eprint = {30216875},
  eprinttype = {jstor},
  pages = {97--110},
  publisher = {Springer},
  issn = {0885-727X},
  urldate = {2024-03-18},
  file = {/Users/cbbcbail/Zotero/storage/XM458YMX/Culbertson and Powers - 1959 - A Study of Graph Comprehension Difficulties.pdf}
}

@article{cummingNewStatisticsWhy2014,
  title = {The {{New Statistics}}: {{Why}} and {{How}}},
  shorttitle = {The {{New Statistics}}},
  author = {Cumming, Geoff},
  year = {2014},
  month = jan,
  journal = {Psychological Science},
  volume = {25},
  number = {1},
  pages = {7--29},
  publisher = {SAGE Publications Inc},
  issn = {0956-7976},
  doi = {10.1177/0956797613504966},
  urldate = {2024-03-12},
  abstract = {We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/JYETALHM/Cumming - 2014 - The New Statistics Why and How.pdf}
}

@article{eellsRelativeMeritsCircles1926a,
  title = {The {{Relative Merits}} of {{Circles}} and {{Bars}} for {{Representing Component Parts}}},
  author = {Eells, Walter Crosby},
  year = {1926},
  journal = {Journal of the American Statistical Association},
  volume = {21},
  number = {154},
  eprint = {2277140},
  eprinttype = {jstor},
  pages = {119--132},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  issn = {0162-1459},
  doi = {10.2307/2277140},
  urldate = {2024-03-06},
  file = {/Users/cbbcbail/Zotero/storage/ZN7SMAFP/Eells - 1926 - The Relative Merits of Circles and Bars for Repres.pdf}
}

@misc{fewPiesDessert2007,
  title = {Save the {{Pies}} for {{Dessert}}},
  author = {Few, Stephen},
  year = {2007},
  file = {/Users/cbbcbail/Zotero/storage/TQVKQFX2/Save the Pies for Dessert.pdf}
}

@article{francoisGaugesDesignDigital2019,
  title = {Gauges Design for a Digital Instrument Cluster: {{Efficiency}}, Visual Capture, and Satisfaction Assessment for Truck Driving},
  shorttitle = {Gauges Design for a Digital Instrument Cluster},
  author = {Fran{\c c}ois, Mathilde and Fort, Alexandra and Crave, Philippe and Osiurak, Fran{\c c}ois and Navarro, Jordan},
  year = {2019},
  month = jul,
  journal = {International Journal of Industrial Ergonomics},
  volume = {72},
  pages = {290--297},
  issn = {0169-8141},
  doi = {10.1016/j.ergon.2019.06.004},
  urldate = {2024-01-19},
  abstract = {This study aims at increasing knowledge of the best way to design trucks' gauges on digital instrument clusters. Trucks are equipped with many gauges that the driver has to monitor while driving. Digital instrument clusters offer new design possibilities and the human factors literature presents only limited answers on safe and efficient gauge designs. Eighteen truck drivers were presented with eight gauges with different shapes, orientation and indicators to perform three reading tasks (quantitative, qualitative and check reading). Results showed that gauge design impacted task completion times, eyes on-gauge duration and satisfaction. Horizontal gauges and pointer indicators were more efficient and less demanding visually. On the subjective side, circular and horizontal gauges were preferred by drivers. Specific gauge designs implied a gain in visual demand up to 250\,ms. For the design of gauges on digital instrument cluster, information processing can be facilitated thanks to basic design changes.},
  keywords = {Commercial vehicles dashboards,Display design principles,HMI design,Interface evaluation,Visual capture},
  file = {/Users/cbbcbail/Zotero/storage/CKUXYWIG/François et al. - 2019 - Gauges design for a digital instrument cluster Ef.pdf}
}

@inproceedings{heerCrowdsourcingGraphicalPerception2010,
  title = {Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design},
  shorttitle = {Crowdsourcing Graphical Perception},
  booktitle = {Proceedings of the {{SIGCHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  author = {Heer, Jeffrey and Bostock, Michael},
  year = {2010},
  month = apr,
  series = {{{CHI}} '10},
  pages = {203--212},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/1753326.1753357},
  urldate = {2024-03-12},
  abstract = {Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon's Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies.},
  isbn = {978-1-60558-929-9},
  keywords = {crowdsourcing,evaluation,experimentation,graphical perception,information visualization,mechanical turk,user study},
  file = {/Users/cbbcbail/Zotero/storage/5BZXXU5W/Heer and Bostock - 2010 - Crowdsourcing graphical perception using mechanic.pdf}
}

@article{hollandsBiasProportionJudgments2000,
  title = {Bias in {{Proportion Judgments}}: {{The Cyclical Power Model}}},
  shorttitle = {Bias in {{Proportion Judgments}}},
  author = {Hollands, Justin and Dyre, Brian},
  year = {2000},
  month = jul,
  journal = {Psychological Review},
  volume = {107},
  pages = {500--524},
  doi = {10.1037/0033-295X.107.3.500},
  abstract = {When participants make part--whole proportion judgments, systematic bias is commonly observed. In some studies, small proportions are overestimated and large proportions underestimated; in other studies, the reverse pattern occurs. Sometimes the bias pattern repeats cyclically with a higher frequency (e.g., overestimation of proportions less than .25 and between .5 and .75; underestimation otherwise). To account for the various bias patterns, a cyclical power model was derived from Stevens' power law. The model proposes that the amplitude of the bias pattern is determined by the Stevens exponent, {$\beta$} (i.e., the stimulus continuum being judged), and that the frequency of the pattern is determined by a choice of intermediate reference points in the stimulus. When {$\beta$} {$<$} 1, an over-then-under pattern is predicted; when {$\beta$} {$>$} 1, the under-then-over pattern is predicted. Two experiments confirming the model's assumptions are described. A mixed-cycle version of the model is also proposed that predicts observed asymmetries in bias patterns when the set of reference points varies across trials.},
  file = {/Users/cbbcbail/Zotero/storage/SZP3E6IQ/Hollands and Dyre - 2000 - Bias in Proportion Judgments The Cyclical Power M.pdf}
}

@article{hollandsJudgingProportionGraphs1998,
  title = {Judging {{Proportion}} with {{Graphs}}: {{The Summation Model}}},
  shorttitle = {Judging {{Proportion}} with {{Graphs}}},
  author = {Hollands, Justin and Spence, Ian},
  year = {1998},
  month = apr,
  journal = {Applied Cognitive Psychology},
  volume = {12},
  pages = {173--190},
  doi = {10.1002/(SICI)1099-0720(199804)12:23.0.CO;2-K},
  abstract = {People take longer to judge part-to-whole relationships with bar graphs than with pie charts or divided bar graphs. Subjects may perform summation operations to establish the whole with bar graphs, which would be unnecessary for other graph types depicting the whole with a single object. To test this summation model, the number of components forming the whole was varied with bars, divided bars, reference bars, and pies in three experiments. Response time increased with the number of components for bar graphs but there was little increase for other graph types in Experiment 1. An accuracy emphasis in Experiment 2 produced generally longer response times, but had little e€ect on the time per summation. The summation operation was not used when graphs were displayed brie{\textasciimacron}y in Experiment 3, although subjects still took longer with bars. The estimated time for a summation operation is consistent with estimates derived from other research. In general, the bar graph is not e€ective for proportion judgments, and its disadvantage becomes potentially greater as the number of components increases.},
  file = {/Users/cbbcbail/Zotero/storage/N4NEJFUB/Hollands and Spence - 1998 - Judging Proportion with Graphs The Summation Mode.pdf}
}

@article{hollandsJudgmentsChangeProportion1992,
  title = {Judgments of {{Change}} and {{Proportion}} in {{Graphical Perception}}},
  author = {Hollands, J. G. and Spence, Ian},
  year = {1992},
  month = jun,
  journal = {Human Factors},
  volume = {34},
  number = {3},
  pages = {313--334},
  publisher = {SAGE Publications Inc},
  issn = {0018-7208},
  doi = {10.1177/001872089203400306},
  urldate = {2024-03-18},
  abstract = {Subjects judged change and proportion when viewing graphs in two experiments. Change was judged more quickly and accurately with line and bar graphs than with pie charts or tiered bar graphs, and this difference was larger when the rate of change was smaller. Without a graduated scale, proportion was judged more quickly and accurately with pie charts and divided bar graphs than with line or bar graphs. Perception is direct when it requires simpler or fewer mental operations; we propose that perception of change is direct with line and bar graphs, whereas perception of proportion is direct with pie charts and divided bar graphs. The results are also consistent with the proximity compatibility principle. Suggestions for improving the design of graphical displays are given.},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/76JDHFGZ/Hollands and Spence - 1992 - Judgments of Change and Proportion in Graphical Pe.pdf}
}

@article{hondaNumberBiasWisdom2022,
  title = {On the Round Number Bias and Wisdom of Crowds in Different Response Formats for Numerical Estimation},
  author = {Honda, Hidehito and Kagawa, Rina and Shirasuna, Masaru},
  year = {2022},
  month = may,
  journal = {Scientific Reports},
  volume = {12},
  number = {1},
  pages = {8167},
  publisher = {Nature Publishing Group},
  issn = {2045-2322},
  doi = {10.1038/s41598-022-11900-7},
  urldate = {2024-02-14},
  abstract = {When asked for numerical estimations, people can respond by stating their estimates (e.g., writing down a number) or indicating a number on a scale. Although these methods are logically the same, such differences may affect the responses to the numerical estimations. In this study, we examined how differences in response format affected responses to numerical estimations using two behavioral experiments. We found that participants showed a round number bias (i.e., people answered estimates with round numbers) when simply stating a number and the distribution of responses tended to be less diverse. In contrast, this tendency was not observed when the participants responded using a scale. Participants provided more diverse estimates when they answered using a scale. Furthermore, we analyzed how this difference in response distribution was related to the wisdom of crowds (the aggregated judgment is as accurate as, or sometimes better than, the best individual judgment in the group) using computer simulations. The results indicated that round number bias affected the achievement of the wisdom of crowds. Particularly, when the group size was small, biased responses resulted in less effective achievement. Our findings suggest that using an appropriate scale is a low-cost method for eliminating round number bias and efficiently achieving the wisdom of crowds.},
  copyright = {2022 The Author(s)},
  langid = {english},
  keywords = {Human behaviour,Psychology},
  file = {/Users/cbbcbail/Zotero/storage/MP8QJVVZ/Honda et al. - 2022 - On the round number bias and wisdom of crowds in d.pdf}
}

@article{isenbergStudyDualScaleData2011,
  title = {A {{Study}} on {{Dual-Scale Data Charts}}},
  author = {Isenberg, Petra and Bezerianos, Anastasia and Dragicevic, Pierre and Fekete, Jean-Daniel},
  year = {2011},
  month = dec,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {17},
  number = {12},
  pages = {2469--2478},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2011.160},
  urldate = {2024-03-17},
  abstract = {We present the results of a user study that compares different ways of representing Dual-Scale data charts. Dual-Scale charts incorporate two different data resolutions into one chart in order to emphasize data in regions of interest or to enable the comparison of data from distant regions. While some design guidelines exist for these types of charts, there is currently little empirical evidence on which to base their design. We fill this gap by discussing the design space of Dual-Scale cartesian-coordinate charts and by experimentally comparing the performance of different chart types with respect to elementary graphical perception tasks such as comparing lengths and distances. Our study suggests that cut-out charts which include collocated full context and focus are the best alternative, and that superimposed charts in which focus and context overlap on top of each other should be avoided.},
  keywords = {Data visualization,Dual-Scale Charts.,Focus+Context,Image color analysis,Quantitative Experiment,Quantization,Shape analysis,Terminology},
  file = {/Users/cbbcbail/Zotero/storage/S4YSSU4I/Isenberg et al. - 2011 - A Study on Dual-Scale Data Charts.pdf;/Users/cbbcbail/Zotero/storage/PJTUFHCV/6065014.html}
}

@article{kaleVisualReasoningStrategies2021,
  title = {Visual {{Reasoning Strategies}} for {{Effect Size Judgments}} and {{Decisions}}},
  author = {Kale, Alex and Kay, Matthew and Hullman, Jessica},
  year = {2021},
  month = feb,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {27},
  number = {2},
  pages = {272--282},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2020.3030335},
  urldate = {2024-03-05},
  abstract = {Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95\% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.},
  keywords = {data cognition,Data visualization,Decision making,Estimation,graphical perception,Task analysis,Uncertainty,Uncertainty visualization,Visualization},
  file = {/Users/cbbcbail/Zotero/storage/MP3RAW2B/Kale et al. - 2021 - Visual Reasoning Strategies for Effect Size Judgme.pdf;/Users/cbbcbail/Zotero/storage/RRMVNBA3/9222364.html}
}

@book{kosaraCircularParttoWholeCharts2019,
  title = {Circular {{Part-to-Whole Charts Using}} the {{Area Visual Cue}}},
  author = {Kosara, Robert},
  year = {2019},
  publisher = {The Eurographics Association},
  doi = {10.2312/evs.20191163},
  urldate = {2024-03-12},
  abstract = {Studies of chart types can reveal unexplored design spaces, like the circular diagrams used in recent studies on pie charts. In this paper, we explore several variations of part-to-whole charts that use area to represent a fraction within a circle. We find one chart that performs very similarly to the pie chart, even though it is visually more complex. Centered shapes turn out to lead to much worse accuracy than any other stimuli, even the same shape when not centered. These first results point to the need for more systematic explorations of the design spaces around existing charts.},
  isbn = {978-3-03868-090-1},
  langid = {english},
  annotation = {Accepted: 2019-06-02T18:14:24Z},
  file = {/Users/cbbcbail/Zotero/storage/8GWIPHJY/Kosara - 2019 - Circular Part-to-Whole Charts Using the Area Visua.pdf}
}

@inproceedings{kosaraEmpireBuiltSand2016,
  title = {An {{Empire Built On Sand}}: {{Reexamining What We Think We Know About Visualization}}},
  shorttitle = {An {{Empire Built On Sand}}},
  booktitle = {Proceedings of the {{Sixth Workshop}} on {{Beyond Time}} and {{Errors}} on {{Novel Evaluation Methods}} for {{Visualization}}},
  author = {Kosara, Robert},
  year = {2016},
  month = oct,
  series = {{{BELIV}} '16},
  pages = {162--168},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/2993901.2993909},
  urldate = {2024-03-12},
  abstract = {If we were to design Information Visualization from scratch, we would start with the basics: understand the principles of perception, test how they apply to different data encodings, build up those encodings to see if the principles still apply, etc. Instead, visualization was created from the other end: by building visual displays without an idea of how or if they worked, and then finding the relevant perceptual and other basics here and there. This approach has the problem that we end up with a very patchy understanding of the foundations of our field. More than that, there is a good amount of unproven assumptions, aesthetic judgments, etc. mixed in with the evidence. We often don't even realize how much we rely on the latter, and can't easily identify them because they have been so deeply incorporated into the fabric of our field. In this paper, I attempt to tease apart what we know and what we only think we know, using a few examples. The goal is to point out specific gaps in our knowledge, and to encourage researchers in the field to start questioning the underlying assumptions. Some of them are probably sound and will hold up to scrutiny. But some of them will not. We need to find out which is which and systematically build up a better foundation for our field. If we intend to develop ever more and better techniques and systems, we can't keep ignoring the base, or it will all come tumbling down sooner or later.},
  isbn = {978-1-4503-4818-8},
  file = {/Users/cbbcbail/Zotero/storage/EXL2LT37/Kosara - 2016 - An Empire Built On Sand Reexamining What We Think.pdf}
}

@inproceedings{kosaraEvidenceAreaPrimary2019,
  title = {Evidence for {{Area}} as the {{Primary Visual Cue}} in {{Pie Charts}}},
  booktitle = {2019 {{IEEE Visualization Conference}} ({{VIS}})},
  author = {Kosara, Robert},
  year = {2019},
  month = oct,
  pages = {101--105},
  doi = {10.1109/VISUAL.2019.8933547},
  urldate = {2024-03-15},
  abstract = {The long-standing assumption of angle as the primary visual cue used to read pie charts has recently been called into question. We conducted a controlled, preregistered study using parallel-projected 3D pie charts. Angle, area, and arc length differ dramatically when projected and change over a large range of values. Modeling these changes and comparing them to study participants' estimates allows us to rank the different visual cues by model fit. Area emerges as the most likely cue used to read pie charts.},
  keywords = {Analytical models,Bars,Computational modeling,Predictive models,Three-dimensional displays,Two dimensional displays,Visualization},
  file = {/Users/cbbcbail/Zotero/storage/FNG2NACQ/Kosara - 2019 - Evidence for Area as the Primary Visual Cue in Pie.pdf}
}

@book{kosaraImpactDistributionChart2019,
  title = {The {{Impact}} of {{Distribution}} and {{Chart Type}} on {{Part-to-Whole Comparisons}}},
  author = {Kosara, Robert},
  year = {2019},
  publisher = {The Eurographics Association},
  issn = {2019-1162},
  doi = {10.2312/evs20191162},
  urldate = {2024-03-12},
  abstract = {Pie charts and treemaps are commonly used in business settings to show part-to-whole relationships. In a study, we compare pie charts, treemaps, stacked bars, and two circular charts when answering part-to-whole questions with multiple slices and different distributions of values. We find that the circular charts, including the unusual variations, perform better than the treemap, and that their performance depends on whether participants are asked to judge the largest slice or a smaller one.},
  isbn = {978-3-03868-090-1},
  langid = {english},
  annotation = {Accepted: 2019-06-02T18:14:24Z},
  file = {/Users/cbbcbail/Zotero/storage/GYYPYDUA/Kosara - 2019 - The Impact of Distribution and Chart Type on Part-.pdf}
}

@book{kosaraJudgmentErrorPie2016,
  title = {Judgment {{Error}} in {{Pie Chart Variations}}},
  author = {Kosara, Robert and Skau, Drew},
  year = {2016},
  publisher = {The Eurographics Association},
  issn = {-},
  doi = {10.2312/eurovisshort.20161167},
  urldate = {2024-01-19},
  abstract = {Pie charts and their variants are prevalent in business settings and many other uses, even if they are not popular with the academic community. In a recent study, we found that contrary to general belief, there is no clear evidence that these charts are read based on the central angle. Instead, area and arc length appear to be at least equally important. In this paper, we build on that study to test several pie chart variations that are popular in information graphics: exploded pie chart, pie with larger slice, elliptical pie, and square pie (in addition to a regular pie chart used as the baseline). We find that even variants that do not distort central angle cause greater error than regular pie charts. Charts that distort the shape show the highest error. Many of our predictions based on the previous study's results are borne out by this study's findings.},
  isbn = {978-3-03868-014-7},
  langid = {english},
  annotation = {Accepted: 2016-06-09T09:42:27Z},
  file = {/Users/cbbcbail/Zotero/storage/Q9N3GB4D/Kosara and Skau - 2016 - Judgment Error in Pie Chart Variations.pdf}
}

@inproceedings{kosaraMechanicalTurksDream2010,
  title = {Do {{Mechanical Turks}} Dream of Square Pie Charts?},
  booktitle = {Proceedings of the 3rd {{BELIV}}'10 {{Workshop}}: {{BEyond}} Time and Errors: Novel {{evaLuation}} Methods for {{Information Visualization}}},
  author = {Kosara, Robert and Ziemkiewicz, Caroline},
  year = {2010},
  month = apr,
  series = {{{BELIV}} '10},
  pages = {63--70},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/2110192.2110202},
  urldate = {2024-03-18},
  abstract = {Online studies are an attractive alternative to the laborintensive lab study, and promise the possibility of reaching a larger variety and number of people than at a typical university. There are also a number of draw-backs, however, that have made these studies largely impractical so far. Amazon's Mechanical Turk is a web service that facilitates the assignment of small, web-based tasks to a large pool of anonymous workers. We used it to conduct several perception and cognition studies, one of which was identical to a previous study performed in our lab. We report on our experiences and present ways to avoid common problems by taking them into account in the study design, and taking advantage of Mechanical Turk's features.},
  isbn = {978-1-4503-0007-0},
  keywords = {empirical studies,Mechanical Turk},
  file = {/Users/cbbcbail/Zotero/storage/A2B8W6A3/Kosara and Ziemkiewicz - 2010 - Do Mechanical Turks dream of square pie charts.pdf}
}

@article{lewandowskyPerceptionStatisticalGraphs1989,
  title = {The {{Perception}} of {{Statistical Graphs}}},
  author = {Lewandowsky, Stephan and Spence, Ian},
  year = {1989},
  month = nov,
  journal = {Sociological Methods \& Research},
  volume = {18},
  number = {2-3},
  pages = {200--242},
  publisher = {SAGE Publications Inc},
  issn = {0049-1241},
  doi = {10.1177/0049124189018002002},
  urldate = {2024-03-06},
  abstract = {Graphs have been an essential tool for the analysis and communication of statistical data for about 200 years. Despite widespread use and their importance in science, business, and many other walks of life, relatively little is known about how people perceive and process statistical graphs. This article reviews several empirical studies designed to explore the suitability of various graphs for a variety of purposes, and discusses the relevant theoretical psychological literature. The role of traditional psychophysics is considered, especially in connection with the long-running dispute concerning the relative merits of pie and bar charts. The review also discusses experiments on the perception of scatterplots and the use of multivariate displays, and points out the need for more empirical work.},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/2Y3IB7Y6/LEWANDOWSKY and SPENCE - 1989 - The Perception of Statistical Graphs.pdf}
}

@article{melodycarswellGraphingDepthPerspectives1991,
  title = {Graphing in Depth: Perspectives on the Use of Three-Dimensional Graphs to Represent Lower-Dimensional Data},
  shorttitle = {Graphing in Depth},
  author = {MELODY CARSWELL, C. and FRANKENBERGER, {\relax SYLVIA} and BERNHARD, {\relax DONALD}},
  year = {1991},
  month = nov,
  journal = {Behaviour \& Information Technology},
  volume = {10},
  number = {6},
  pages = {459--474},
  publisher = {Taylor \& Francis},
  issn = {0144-929X},
  doi = {10.1080/01449299108924304},
  urldate = {2024-03-18},
  abstract = {Embellishing simple graphs by adding perspective, 'the 3D look, has become increasingly commonplace with the ready availability of graphics software. However, the effect of adding such decorative depth on the comprehension and recall of the graph's message has received little attention. The present study evaluated performance on such common graphical formats as line graphs, bar charts and pie charts constructed with and without the 3D look. When subjects were asked to make relative magnitude estimations, only the 3D line graphs resulted in reliable performance decrements. Likewise, information presented in 3D line graphs was remembered less accurately than information presented in 2D line graphs. For the estimation of global trends, both 3D line graphs and bar charts were used more quickly than 2D formats, but this speed was obtained at the expense of accuracy. For a trend classification task involving more focused processing, 3D line graphs and bar charts were associated with an overall performance decrement when compared with their 2D counterparts. Finally, the use of 3D designs, in addition to modifying performance, may influence the attitudes formed by subjects toward the information presented in the graphs.}
}

@article{petersonHowAccuratelyAre1954,
  title = {How {{Accurately Are Different Kinds}} of {{Graphs Read}}?},
  author = {Peterson, Lewis V. and Schramm, Wilbur},
  year = {1954},
  journal = {Audio Visual Communication Review},
  volume = {2},
  number = {3},
  eprint = {30218399},
  eprinttype = {jstor},
  pages = {178--189},
  publisher = {Springer},
  issn = {0885-727X},
  urldate = {2024-03-18},
  file = {/Users/cbbcbail/Zotero/storage/VZNSGB7J/Peterson and Schramm - 1954 - How Accurately Are Different Kinds of Graphs Read.pdf}
}

@inproceedings{redmondVisualCuesEstimation2019,
  title = {Visual {{Cues}} in {{Estimation}} of {{Part-To-Whole Comparisons}}},
  booktitle = {2019 {{IEEE Visualization Conference}} ({{VIS}})},
  author = {Redmond, Stephen},
  year = {2019},
  month = oct,
  pages = {1--5},
  doi = {10.1109/VISUAL.2019.8933718},
  urldate = {2024-01-18},
  abstract = {Pie charts were first published in 1801 by William Playfair and have caused some controversy since. Despite the suggestions of many experts against their use, several empirical studies have shown that pie charts are at least as good as alternatives. From Brinton to Few on one side and Eells to Kosara on the other, there appears to have been a hundred-year war waged on the humble pie. In this paper a set of experiments are reported that compare the performance of pie charts and horizontal bar charts with various visual cues. Amazon's Mechanical Turk service was employed to perform the tasks of estimating segments in various part-to-whole charts. The results lead to recommendations for data visualization professionals in developing dashboards.},
  file = {/Users/cbbcbail/Zotero/storage/TLT5P9KU/Redmond - 2019 - Visual Cues in Estimation of Part-To-Whole Compari.pdf}
}

@article{rubio-sanchezAxisCalibrationImproving2014,
  title = {Axis {{Calibration}} for {{Improving Data Attribute Estimation}} in {{Star Coordinates Plots}}},
  author = {{Rubio-S{\'a}nchez}, Manuel and Sanchez, Alberto},
  year = {2014},
  month = dec,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {20},
  number = {12},
  pages = {2013--2022},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2014.2346258},
  urldate = {2024-03-04},
  abstract = {Star coordinates is a well-known multivariate visualization method that produces linear dimensionality reduction mappings through a set of radial axes defined by vectors in an observable space. One of its main drawbacks concerns the difficulty to recover attributes of data samples accurately, which typically lie in the [0], [1] interval, given the locations of the low-dimensional embeddings and the vectors. In this paper we show that centering the data can considerably increase attribute estimation accuracy, where data values can be read off approximately by projecting embedded points onto calibrated (i.e., labeled) axes, similarly to classical statistical biplots. In addition, this idea can be coupled with a recently developed orthonormalization process on the axis vectors that prevents unnecessary distortions. We demonstrate that the combination of both approaches not only enhances the estimates, but also provides more faithful representations of the data.},
  keywords = {Attribute value estimation,Axis calibration,Biplots,Calibration,Data centering,Data visualization,Estimation error,Linear systems,Multivariate regression,Orthographic projection,RadViz,Star Coordinates},
  file = {/Users/cbbcbail/Zotero/storage/PKFGT7HX/Rubio-Sánchez and Sanchez - 2014 - Axis Calibration for Improving Data Attribute Esti.pdf;/Users/cbbcbail/Zotero/storage/BH48CMQW/6875998.html}
}

@article{siegristUseMisuseThreedimensional1996,
  title = {The Use or Misuse of Three-Dimensional Graphs to Represent Lower-Dimensional Data},
  author = {Siegrist, Michael},
  year = {1996},
  month = jan,
  journal = {Behaviour \& Information Technology},
  volume = {15},
  number = {2},
  pages = {96--100},
  publisher = {Taylor \& Francis},
  issn = {0144-929X},
  doi = {10.1080/014492996120300},
  urldate = {2024-03-18},
  abstract = {Some statisticians hold strong opinions regarding graphs with a 3-D look. However, in experiments little attention has been paid to the effects of adding decorative depth. The performance of subjects on pie charts and bar charts with and without 3-D was evaluated in the present experiment. Subjects were asked to make relative magnitude estimates for different graphs. For pie charts, better performance was observed for 2-D than for 3-D charts. For the bar charts, a more differentiated picture emerged: performance was dependent on the position, height and dimension of the bars. However, 3-D bar charts had the one disadvantage that subjects needed more time to evaluate this type of graph.}
}

@article{simkinInformationProcessingAnalysisGraph1987,
  title = {An {{Information-Processing Analysis}} of {{Graph Perception}}},
  author = {Simkin, David and Hastie, Reid},
  year = {1987},
  month = jun,
  journal = {Journal of the American Statistical Association},
  volume = {82},
  number = {398},
  pages = {454--465},
  issn = {0162-1459, 1537-274X},
  doi = {10.1080/01621459.1987.10478448},
  urldate = {2024-03-06},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/DJNFVKRP/Simkin and Hastie - 1987 - An Information-Processing Analysis of Graph Percep.pdf}
}

@article{skauArcsAnglesAreas2016,
  title = {Arcs, {{Angles}}, or {{Areas}}: {{Individual Data Encodings}} in {{Pie}} and {{Donut Charts}}},
  shorttitle = {Arcs, {{Angles}}, or {{Areas}}},
  author = {Skau, Drew and Kosara, Robert},
  year = {2016},
  journal = {Computer Graphics Forum},
  volume = {35},
  number = {3},
  pages = {121--130},
  issn = {1467-8659},
  doi = {10.1111/cgf.12888},
  urldate = {2024-03-06},
  abstract = {Pie and donut charts have been a hotly debated topic in the visualization community for some time now. Even though pie charts have been around for over 200 years, our understanding of the perceptual factors used to read data in them is still limited. Data is encoded in pie and donut charts in three ways: arc length, center angle, and segment area. For our first study, we designed variations of pie charts to test the importance of individual encodings for reading accuracy. In our second study, we varied the inner radius of a donut chart from a filled pie to a thin outline to test the impact of removing the central angle. Both studies point to angle being the least important visual cue for both charts, and the donut chart being as accurate as the traditional pie chart.},
  copyright = {{\copyright} 2016 The Author(s) Computer Graphics Forum {\copyright} 2016 The Eurographics Association and John Wiley \& Sons Ltd. Published by John Wiley \& Sons Ltd.},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/Z2CWENQ2/Skau and Kosara - 2016 - Arcs, Angles, or Areas Individual Data Encodings .pdf}
}

@article{spenceDisplayingProportionsPercentages1991a,
  title = {Displaying Proportions and Percentages},
  author = {Spence, Ian and Lewandowsky, Stephan},
  year = {1991},
  journal = {Applied Cognitive Psychology},
  volume = {5},
  number = {1},
  pages = {61--77},
  issn = {1099-0720},
  doi = {10.1002/acp.2350050106},
  urldate = {2024-03-06},
  abstract = {Pie and bar charts are commonly used to display percentage or proportional data, but professional data analysts have frowned on the use of the pie chart on the grounds that judgements of area are less accurate than judgements of lenth. Thus the bar chart has been favoured. When the amount of data to be communicated is small, some authorities have advocated the use of properly constructed tables, as another option. The series of experiments reported here suggests that there is little to choose between the pie and the bar chart, with the former enjoying a slight advantage if the required judgement is a complicated one, but that both forms of chart are superior to the table. Thus our results do not support the commonly expressed opinion that pie charts are inferior. An analysis of the nature of the task and a review of the psychophysical literature suggest that the traditional prejudice against the pie chart is misguided.},
  copyright = {Copyright {\copyright} 1991 John Wiley \& Sons, Ltd},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/BLDKBWH9/Spence and Lewandowsky - 1991 - Displaying proportions and percentages.pdf}
}

@article{spenceNoHumblePie2005,
  title = {No {{Humble Pie}}: {{The Origins}} and {{Usage}} of a {{Statistical Chart}}},
  shorttitle = {No {{Humble Pie}}},
  author = {Spence, Ian},
  year = {2005},
  journal = {Journal of Educational and Behavioral Statistics},
  volume = {30},
  number = {4},
  eprint = {3701294},
  eprinttype = {jstor},
  pages = {353--368},
  publisher = {[American Educational Research Association, Sage Publications, Inc., American Statistical Association]},
  issn = {1076-9986},
  urldate = {2024-03-12},
  abstract = {William Playfair's pie chart is more than 200 years old and yet its intellectual origins remain obscure. The inspiration likely derived from the logic diagrams of Llull, Bruno, Leibniz, and Euler, which were familiar to William because of the instruction of his mathematician brother John. The pie chart is broadly popular but-despite its common appeal-most experts have not been seduced, and the academy has advised avoidance; nonetheless, the masses have chosen to ignore this advice. This commentary discusses the origins of the pie chart and the appropriate uses of the form.},
  file = {/Users/cbbcbail/Zotero/storage/N9SLTN7Y/Spence - 2005 - No Humble Pie The Origins and Usage of a Statisti.pdf}
}

@article{spenceVisualPsychophysicsSimple1990,
  title = {Visual {{Psychophysics}} of {{Simple Graphical Elements}}},
  author = {Spence, Ian},
  year = {1990},
  month = dec,
  journal = {Journal of experimental psychology. Human perception and performance},
  volume = {16},
  pages = {683--92},
  doi = {10.1037//0096-1523.16.4.683},
  abstract = {The accuracy with which graphical elements are judged was assessed in a psychophysical task that parallels the real-life use of graphs. The task is a variant of the Metfessel-Comrey constant-sum method, and an associated model based on Stevens's law is proposed. The stimuli were horizontal and vertical lines, bars, pie and disk slices, cylinders, boxes, and table entries (numbers). Stevens's law exponents were near unity for numbers and 1-dimensional elements but were also close to 1 for elements possessing 2 or 3 apparent dimensions--subjects accommodate extraneous dimensions that do not carry variation, changing the effective dimensionality of the stimulus. Judgment errors were small, with numbers yielding the best performance; elements such as bars and pie slices were judged almost as accurately; disk elements were judged least accurately, but the magnitude of the errors was not large.},
  file = {/Users/cbbcbail/Zotero/storage/2EN8GKWJ/Spence - 1990 - Visual Psychophysics of Simple Graphical Elements.pdf}
}

@inproceedings{suzukiScatterplotBasedVisualizationTool2016,
  title = {A {{Scatterplot-Based Visualization Tool}} for {{Regression Analysis}}},
  booktitle = {2016 20th {{International Conference Information Visualisation}} ({{IV}})},
  author = {Suzuki, Chie and Itoh, Takayuki and Umezu, Keisuke and Motohashi, Yousuke},
  year = {2016},
  month = jul,
  pages = {75--80},
  issn = {2375-0138},
  doi = {10.1109/IV.2016.63},
  urldate = {2024-03-04},
  abstract = {Regression analysis has been widely applied to various academic and industrial fields. Applications of regression analysis include medical problems such as health estimation, environmental problems such as disaster prediction and energy consumption estimation, and business/economic analytics. Here, accuracy and quality of regression analysis strongly depend on relevancy between input explanatory variables and actual objective functions. It often happens that several explanatory variables are well correlated with objective functions while others do not well correlated, and therefore accuracy of regression analysis may improve by removing unnecessary explanatory variables. This paper presents a scatterplot-based regression analysis tool. This tool visualizes the distribution of errors between actual and estimated values of objective functions, and provides user interfaces to explore the relationships between explanatory variables and the errors. This paper introduces examples of the visualization results using the presented tool with actual and estimated revenues at a store.},
  keywords = {Data visualization,Estimation,Linear programming,Mathematical model,Regression analysis,scatterplots,Three-dimensional displays,Visualization},
  file = {/Users/cbbcbail/Zotero/storage/5DVR7Y2N/Suzuki et al. - 2016 - A Scatterplot-Based Visualization Tool for Regress.pdf;/Users/cbbcbail/Zotero/storage/3SQCVHVH/7557907.html}
}

@book{tufteVisualDisplayQuantitative1983,
  title = {The {{Visual Display}} of {{Quantitative Information}}},
  author = {Tufte, Edward},
  year = {1983},
  file = {/Users/cbbcbail/Zotero/storage/CMAMWHAN/The Visual Display of Quantitatve Information.pdf}
}

@article{ventocillaComparativeUserStudy2020,
  title = {A Comparative User Study of Visualization Techniques for Cluster Analysis of Multidimensional Data Sets},
  author = {Ventocilla, Elio and Riveiro, Maria},
  year = {2020},
  month = oct,
  journal = {Information Visualization},
  volume = {19},
  number = {4},
  pages = {318--338},
  publisher = {SAGE Publications},
  issn = {1473-8716},
  doi = {10.1177/1473871620922166},
  urldate = {2024-03-04},
  abstract = {This article presents an empirical user study that compares eight multidimensional projection techniques for supporting the estimation of the number of clusters,  k , embedded in six multidimensional data sets. The selection of the techniques was based on their intended design, or use, for visually encoding data structures, that is, neighborhood relations between data points or groups of data points in a data set. Concretely, we study: the difference between the estimates of  k  as given by participants when using different multidimensional projections; the accuracy of user estimations with respect to the number of labels in the data sets; the perceived usability of each multidimensional projection; whether user estimates disagree with  k  values given by a set of cluster quality measures; and whether there is a difference between experienced and novice users in terms of estimates and perceived usability. The results show that: dendrograms (from Ward's hierarchical clustering) are likely to lead to estimates of  k  that are different from those given with other multidimensional projections, while Star Coordinates and Radial Visualizations are likely to lead to similar estimates; t-Stochastic Neighbor Embedding is likely to lead to estimates which are closer to the number of labels in a data set; cluster quality measures are likely to produce estimates which are different from those given by users using Ward and t-Stochastic Neighbor Embedding; U-Matrices and reachability plots will likely have a low perceived usability; and there is no statistically significant difference between the answers of experienced and novice users. Moreover, as data dimensionality increases, cluster quality measures are likely to produce estimates which are different from those perceived by users using any of the assessed multidimensional projections. It is also apparent that the inherent complexity of a data set, as well as the capability of each visual technique to disclose such complexity, has an influence on the perceived usability.},
  langid = {english},
  file = {/Users/cbbcbail/Zotero/storage/LFULNBAK/Ventocilla and Riveiro - 2020 - A comparative user study of visualization techniqu.pdf}
}

@article{vonhuhnFurtherStudiesGraphic1927,
  title = {Further {{Studies}} in the {{Graphic Use}} of {{Circles}} and {{Bars}}: {{A Discussion}} of the {{Eell}}'s {{Experiment}}},
  shorttitle = {Further {{Studies}} in the {{Graphic Use}} of {{Circles}} and {{Bars}}},
  author = {{von Huhn}, R. and Croxton, Frederick E.},
  year = {1927},
  journal = {Journal of the American Statistical Association},
  volume = {22},
  number = {157},
  eprint = {2277346},
  eprinttype = {jstor},
  pages = {31--39},
  publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
  issn = {0162-1459},
  doi = {10.2307/2277346},
  urldate = {2024-03-18},
  file = {/Users/cbbcbail/Zotero/storage/WVRUQQGA/von Huhn - 1927 - Further Studies in the Graphic Use of Circles and .pdf}
}

@article{xiongReasoningAffordancesTables2022,
  title = {Reasoning {{Affordances}} with {{Tables}} and {{Bar Charts}}},
  author = {Xiong, Cindy and {Lee-Robbins}, Elsie and Zhang, Icy and Gaba, Aimen and Franconeri, Steven},
  year = {2022},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  pages = {1--13},
  issn = {1941-0506},
  doi = {10.1109/TVCG.2022.3232959},
  urldate = {2023-12-08},
  abstract = {A viewer's existing beliefs can prevent accurate reasoning with data visualizations. In particular, confirmation bias can cause people to overweigh information that confirms their beliefs, and dismiss information that disconfirms them. We tested whether confirmation bias exists when people reason with visualized data and whether certain visualization designs can elicit less biased reasoning strategies. We asked crowdworkers to solve reasoning problems that had the potential to evoke both poor reasoning strategies and confirmation bias. We created two scenarios, one in which we primed people with a belief before asking them to make a decision, and another in which people held pre-existing beliefs. The data was presented as either a table, a bar table, or a bar chart. To correctly solve the problem, participants should use a complex reasoning strategy to compare two ratios, each between two pairs of values. But participants could also be tempted to use simpler, superficial heuristics, shortcuts, or biased strategies to reason about the problem. Presenting the data in a table format helped participants reason with the correct ratio strategy while showing the data as a bar table or a bar chart led participants towards incorrect heuristics. Confirmation bias was not significantly present when beliefs were primed, but it was present when beliefs were pre-existing. Additionally, the table presentation format was more likely to afford the ratio reasoning strategy, and the use of ratio strategy was more likely to lead to the correct answer. These findings suggest that data presentation formats can affect affordances for reasoning.},
  file = {/Users/cbbcbail/Zotero/storage/MS5ZPHSD/Xiong et al. - 2022 - Reasoning Affordances with Tables and Bar Charts.pdf}
}

@inproceedings{zengReviewCollationGraphical2023,
  title = {A {{Review}} and {{Collation}} of {{Graphical Perception Knowledge}} for {{Visualization Recommendation}}},
  booktitle = {Proceedings of the 2023 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  author = {Zeng, Zehua and Battle, Leilani},
  year = {2023},
  month = apr,
  series = {{{CHI}} '23},
  pages = {1--16},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3544548.3581349},
  urldate = {2024-01-19},
  abstract = {Selecting appropriate visual encodings is critical to designing effective visualization recommendation systems, yet few findings from graphical perception are typically applied within these systems. We observe two significant limitations in translating graphical perception knowledge into actionable visualization recommendation rules/constraints: inconsistent reporting of findings and a lack of shared data across studies. How can we translate the graphical perception literature into a knowledge base for visualization recommendation? We present a review of 59 papers that study user perception and performance across ten visual analysis tasks. Through this study, we contribute a JSON dataset that collates existing theoretical and experimental knowledge and summarizes key study outcomes in graphical perception. We illustrate how this dataset can inform automated encoding decisions with three representative visualization recommendation systems. Based on our findings, we highlight open challenges and opportunities for the community in collating graphical perception knowledge for a range of visualization recommendation scenarios.},
  isbn = {978-1-4503-9421-5},
  keywords = {Human Perception,Literature Review,Visualization Design},
  file = {/Users/cbbcbail/Zotero/storage/QXIFYYB5/Zeng and Battle - 2023 - A Review and Collation of Graphical Perception Kno.pdf}
}