Visualizing time designing graphical representations for statistical data by will

Statistics and Computing Series Editors: J Chambers D Hand W Hăardle For further volumes: http://www.springer.com/series/3022 Graham Wills Visualizing Time Designing Graphical Representations for Statistical Data 123 Graham Wills Hidden Spring Dr 1128 60540-4112 Naperville, Illinois USA graham@spss.com Series Editors: J Chambers Department of Statistics Sequoia Hall 390 Serra Mall Stanford University Stanford, CA 94305-4065 D Hand Department of Mathematics Imperial College London, South Kensington Campus London SW7 2AZ United Kingdom W Hăardle C.A.S.E Centre for Applied Statistics and Economics School of Business and Economics Humboldt-Universităat zu Berlin Unter den Linden 10099 Berlin Germany ISSN 1431-8784 ISBN 978-0-387-77906-5 e-ISBN 978-0-387-77907-2 DOI 10.1007/978-0-387-77907-2 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011940977 © Springer Science+Business Media, LLC 2012 All rights reserved This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Although this book contains tributes to famous men and women who have invented unique and novel visualizations, and to experts who have distilled knowledge and advanced the science of information visualization, this book is dedicated to those people who designed reports, published charts, and created visualizations and were not content to use the defaults but instead took the extra effort to make their work more truthful, more beautiful, and more useful Thank you! Preface Art or science? Which of these is the right way to think of the field of visualization? This is not an easy question to answer, even for those who have many years of experience in making graphical depictions of data with a view to helping people understand them and take action When we look at beautiful hand-drawn pictures of data, carefully composed by talented individuals, we are drawn to the artistic side In some ways those charts are discouraging; their artistic elegance implies that the creation of good visualizations is not an option for most of us There are books that provide rules and advice on how to draw graphs Some give general advice, suggesting that such and such is good, but this other is bad Others give specific advice such as requiring all charts to have a title or all axes to go to zero, but these are often tied to specific visualizations and so are not general enough to qualify as scientific principles They are valuable for describing existing visualizations, but not general enough to provide guidance for future visualizations If you are designing something new, advice on a bar chart is not especially helpful In this book I want to bridge the gap and not simply give rules and advice but base these on general principles and provide a clear path between them, so that the rules and guidance fall into place naturally, due to knowledge of those principles In terms of the art/science split, I want to advance the scientific component There are excellent books describing artistically superb plots; however, my goal is not simply to be descriptive, but to be prescriptive – to allow people to start with a goal in mind and design a visualization that fulfills that goal clearly, truthfully, and actionably Because I have an essentially scientific direction in mind, I will concentrate on reproducibility A chart that is wonderful for exactly one data set is of little interest It can be appreciated and enjoyed, but the important question must always be: What can I learn from this graphic that I can apply to other data? With this in mind, the examples in this book have been chosen to be realistic rather than exemplary I have made a definite attempt not to choose data that make a picture look good, but rather to choose data for which a chart should be applicable If the result is not perfect, I prefer to present imperfection and explore remedies rather than look for a different data source vii viii Preface This book is concerned with the graphical representation of time data Time is special – it doesn’t behave quite like other variables It has an inherent direction and determines causality Time can be recorded in many ways: it can be linear or cyclic, categorical or continuous Even the written format of a piece of time data can be curiously baroque; mixtures of words, numbers, and special symbols make up the time “Monday the 13th of October, 11:45 am.” What other form of data might occur in so obscure a format? All data are recorded at a certain time, and so all data have a time component, even if it has been removed or deemed a priori as uninteresting This makes time data both unique and universal, so understanding how best to portray them not only is challenging but has wide applicability The portrayal of time data is ubiquitous Any newspaper will feature time-based plots; any company report will show historical data as charts Even the gas bill for my home invites me to compare a time series of the cost of heating my home against one of average monthly temperature Because of this generality, I have written this book to cover a range of different users A visualization expert designing tools for displaying time will find it valuable, but so also should a financier assembling a report in a spreadsheet or a medical researcher trying to display gene sequences using a commercial statistical package You have data, you have a goal in mind Now all you need are the tools to graph the data and so achieve the goal Read on! Graham Wills Acknowledgements The only way to know the effort needed to write a book is to so yourself, and only authors know the debt of gratitude they owe to others Warm thanks are due to many people, broadly classified as shown in the diagram below Any errors and mistakes within the book are entirely my own Fig A modified Venn diagram showing where acknowledgement is due; it shows the major sources but does not include everyone who has helped or influenced my thinking or who has taught me or argued with me over the years The sum total of those contributions would be large; as this book will indicate, it is often small contributions that make or break any major endeavor ix 10.2 Time Lines and Linked Events 221 Fig 10.9 An example timeline by Joseph Priestly, showing the time period leading up to AD Note the faceting into two sections, one on top for Men of Learning, and one below for Statesmen War in Achin Moslem Rebellion in Kansu Spanish Civil War Maji-Maji Rebellion Moslem Rebellions Franco-Prussian War Sequel to the Bolshevik Revolution Colombian Civil War First Chinese-Communist War North American Civil War Crimean War Russo-Turkish War Cuban Revolt Taiping Rebellion Russo-Turkish War 1825 1850 Great War in La Plata 1875 Chaco War Russo-Japanese War Communal Riots in the Indian Peninsula The Mexican Revolution World War II Spanish-American War World War I 1900 1925 1950 Fig 10.10 The timeline here shows the spans of major wars over the timeline covered by Richardson’s analysis of deadly quarrels The color of the interval element and, redundantly, the label color indicate the magnitude of the war as measured by the number of casualties designer to compensate for this problem In this book we are trying to establish automatic rules and so Fig 10.10 shows a more typical result, where an automatic label location algorithm has been used Figure 10.10 shows essentially the same timeline display, with the addition of a color aesthetic The data shown are the statistics of deadly quarrels used in Chap The use of color in this figure is worth a side note Because the elements may be of very short span (e.g., the Spanish-American War), the color of the element might 222 10 Topics In Time Fig 10.11 Timeline of Stonehenge exploration This figure is taken from the book Solving Stonehenge: The New Key to an Ancient Enigma [64], used with permission A detailed explanation of the figure is given in the text 10.2 Time Lines and Linked Events 223 not be visible Thus the label was used redundantly to encode the color An astute observer will then note that although the hues of the elements and their labels match, the labels are darker than the elements themselves This was done so as to ensure the text is legible on a white background (this page) Always be careful when designing visualizations to plan for extreme data, and ensure the chart will be useful in such circumstances Again, comparison with Fig 10.9 is informative The variation in lengths of wars is much greater than the variation in lengths of lives of important people It is rare to see a great man like Alexander, who lived less than 33 years; life spans for this chart have a min:max ratio of about 1:3 In contrast, some deadly quarrels are very quick (the Spanish-American war took months), whereas some are longer (the Achinese War lasted from 1873 to 1904), for a ratio of about 1:100 Different statistical properties of the data lead to different design decisions In the two and a half centuries since their invention, countless versions of the timeline have been created, with many levels of detail and organization The basic structure is of data ranges aligned on one dimension, labeled with information that allows an expert to apply their domain knowledge, and using aesthetics and faceting to add additional information Figure 10.11 shows an excellent example of a timeline showing a wealth of concentrated information in a timeline format The core data are coded as pastel-shaded rectangular ranges showing when various exploration activities took place, with the type of activity color coded and faceted to provide groupings In addition to these base ranges for activities, multiple other elements are present: Small red “subrange” elements These show critical events that are often point events, and almost always lie within a range of the base element One feature we can note is that the landmark events occur in the later half of the activity As we might expect, papers and books are published when the work is nearing completion Adding a second labeled element causes label layout issues, however, so the designer has mitigated this by using distinct typefaces for the two labelings, in much the same way a cartographer would use a different font for labeling regions as opposed to cities Low-information ranges Although most activities are well documented, a considerable amount of unorchestrated digging and robbing took place over the earlier years, and these have been displayed with ranges that are shown in a style that makes them more of a background element than a major feature A single label for a set of such low-information activities has been used, also deemphasizing their importance Icons Restoration activities are not shown as ranges of times, but as simple events Free from the constraints of showing a range, icons can be used to distinguish between simple stone straightening and trilithon fixing Color is used to show bad events in red and good events in green This figure is a complex, hand-designed chart intended for reflective study The design and layout invites us to make time comparisons, and help explain features of importance to researchers in this domain, such as answering questions like: What differences would we expect between Loggan’s engravings and Sir Henry James’ 224 10 Topics In Time photographs? As well as pattern discovery, this visualization has an important use as a reference: When we need to understand one person’s contribution (such as Gibson’s Brittanica article), we can see his contemporaries and parallel activities This chart is valuable for both discovery and search goals 10.3 Summary Most of this book is targeted at presenting general data In this chapter we have examined a couple of specific areas that need visualizations designed to highlight important features of this data The examples were chosen to show the extremes in terms of data quantity The section on large data shows how to apply the principles described in this book to data of arbitrarily large volume; the focus being on aggregation, filtering and showing features within the aggregation The section on timelines shows a different extreme – very few items of data, but rich information on each item For large data, general aggregation techniques were shown, as well as techniques for augmenting standard displays to cope with large data volumes One important lesson is that graphs and tables are not necessarily competing techniques for displaying summary data, but comprise different points along a continuum of possible visualizations The Grammar of Graphics [135] provides building blocks for defining this continuum, as discussed in the framework Chap For linked events and timelines very different techniques are required The details are too important to be aggregated over, and the design goals become focused on maximizing the available information on single charts for reflective study Issues of labeling and graphic design become less of an individual style preference and more important for enhancing clarity These charts are not easy to design and may take many design iterations to get useful enough to use 10.4 Further Exploration The American Statistical Association site for this Airline Data Expo [1] contains a number of posters showing visualizations crafted to explore this data set As with any such competition, it is valuable to look through them all and see what works and try and draw out the successful principles It is also interesting to see how details of presentation make a big difference to clarity of results Which axes drawing algorithms work best? Which fonts are better for labeling? Is it better to have a dark or a light background for a chart? By exploring a set of graphs for the same data set, we can learn more about what makes visualizations successful It is interesting to compare the Stonehenge exploration (Fig 10.11) to those created by the LifeLines project [58, 86] The latter figures are for a very different 10.4 Further Exploration 225 domain, showing events in the treatment of health issues for medical patients, but the goals are similar and the end results show a remarkable similarity of design Linked event diagrams and timelines have a free dimension (in our examples and also typically, the vertical dimension) Items can be moved round within their faceting and should be placed so as to make best use of the space available, avoid overlap and, with linked events, avoid too many crossings This is an important constrained layout problem, and the field of graph layout provides help on how to design algorithms to solve this problem The Web site http://graphdrawing.org provides references to a number of books at various levels of complexity Index A acknowledgement diagram, ix aesthetics brightness, 157 color, 156, 221 combinations, 46 hue, 157 on text labels, 161, 221 other, 161 saturation, 157 shape, 158 size, 157 transparency, 157 aggregation, 207, 208 Agincourt, battle of, 158 airports in the USA, 215 always show zero, 30 annotation, 57 aspect ratio, 124 axis, 129 B Bach, Johann Sebastian, 117 balance of trade, baseball, 41 designated hitter, 48 bible, biblical time, big-endian, 102 binning, 176, 178 bottom-up design, 89, 90 boxplot, 33, 75 C calendars, 2, cartesian coordinates, 50 cascading style sheets, 59 categorical sequences, 116, 118 chart complexity, 140 chartlike table, 211 Chernoff faces, 33 choice of y-axis minimum, 30 Clock of Ages, closely related variables, 77 clustering hierarchical, 200 self-organizing map, 41 color, 156 color, use of, 41 comic books, 15 common patterns, 66 complexity aesthetics, 46 grammatical breakdown, 227 subjective evaluation, 231 complexity experiment, 228 composite aesthetics, 46 conditional relationships, 79 consensus ordering, 230 consistency of mapping, 89 converting time into ranges, 178 converting time ranges to time points, 176 coordinate chain, 52 coordinate transformations, 127, 169 correlations, 75 count data representation, 152 CPI data for the UK, 188 CSS see cascading style sheets, 59 cyclical order, 97 cylindrical coordinates, 50 G Wills, Visualizing Time: Designing Graphical Representations for Statistical Data, Statistics and Computing, DOI 10.1007/978-0-387-77907-2, © Springer Science+Business Media, LLC 2012 253 254 D data–ink ratio, 58, 152 date formats, 102 date transformation formulas, 179 decision trees, 80 density estimation, 115 use in ThemeRiver, 115 dependent and independent variables, 106 discrete time series, 102 display pipeline, 182 distances between orderings, 230 distorting time, 169 distortion techniques, 188 distributions, 71 dividing time, 176 DNA sequence, 117 document analysis, 161 dodging, 112 Dojo, 62 domain-specific display, 199 drill-down, 204 dynamic graphics see interactivity, 181 E earthquakes, 107 Easter, Ebbinghaus Illusion, 153 El Ni˜no, 49, 53 elapsed time, 97 email data, 184 English dialects, 21 epoch, 103 Excel, 103 UNIX, 103 epoch failure, 103 event data, 99, 101, 108, 114 examples airline delay data, 208 baby names, 78 balance of trade, baseball, 140 baseball players, 41 beatles songs, 86 consumer price index, 172, 188 crime, 51, 58 deadly quarrels, 108, 220 El Ni˜no, 49, 53 email, 184 human genome, 116, 117, 119 IBM Stock, 176 mass layoffs, 124, 127 medieval soldiers, 151 Index migration paths, movie ratings, 169 movies, 71, 114, 129 passenger arrivals, 138 population changes in US states, 17 rainfall in the UK, 144 roleplaying games, 218 seismic activity, 106 software bugs and feature requests, 74 software features, 80 star magnitude / color, 68 stock trades, 23, 30, 77 The Jungle Book, 161 Twitter, 111 US Population, 18 wind speeds, 35, 38 ExcelTM date functions, 179 exploratory graphics, 63 F faceting, 17, 136 complexity, 140 faceting by time, 138 time within a faceting, 144 filtering, 207 fisheye coordinate transformation, 52 fisheye coordinate transformations, 188 focus+context, 184, 188 formats, 102 output, 135 fourier analysis, 172 fragile visualizations, xi, 119, 208, 223 frequency space transformations, 172 G gallery, 236 Gantt chart, 217 generalized correlations, 75 geo-temporal data, 144 geography and nationality in the British Isles, 144 Goldberg Variations, 119 GQM (Goal, Question, Metric), 64 Grammar of Graphics, 22 aesthetics, 41 coordinate transformations, 123 coordinates, 50, 105, 123 elements, 23 faceting, 49, 55, 123 guides, 56 interactivity, 58 statistics, 35 styles, 58 Index grammar of graphics complexity analysis, 227 grammatical summary of charts, 229 Grand Canyon, granularity of data, 178 graph comprehension, 85 graph layouts for variable associations, 82 graphical perception tasks, 105 guide axis, 129 time axis, 132, 135 H heatmap, 74 Hertzsprung-Russell Diagram, 68 high-dimensional data, 67 histogram, 178 bin width automatic choice, 178 history of visualization of time, I identifier variable, 111 immersive learning, 65 information seeking, 91 interactive model fitting, 192 interactive parameter manipulation, 184 interactivity, 181 linked views, 198, 216 international date format, see ISO 8601 interval data, 97 ISO 8601, 102 J Japanese calendar, jittering, 112 John Harrison, K kernel, Epanechnikov, 37 Kohonen map, see self-organizing map Kolmogorov–Smirnov test, 71 L labeling, 220 large data sets, 207 legends, 57 linked events, 218 little-endian, see big-endian longitude, 2, 95 lunar time, 255 M ManyEyes, 91 map, 215 map projections, 52 mapping data to graphical features, 85 measurement levels, 95 measures of calendar time, medieval soldiers, 151 Minard, Charles Joseph, model fitting, 192 moving average, 37 multidimensional scaling, 145 multimodal distributions, 72 multivariate time series techniques, 145 musical notation as visualization, 117 N Napoleon, narrative structure, 88 narrative visualization, 86 nominal data, 96 nonlinear transformations of time, 169 O oblique projection, 52 occlusion problem, 24 ordered data, 233 ordinal, 208 ordinal data, 96 outliers, 70 overplotting, 121 overview+detail see focus+context, 184 P paneling, 17, 148 parallel coordinates, 50, 67 parameters, 184 perceptual tasks, 105 periodogram, 174 petroglyphs, phrase net, 91 pipeline see display pipeline, 182 Playfair, William, point processes, 67, 101 polar coordinates, 5, 50, 127 pop-up, 187 position modifiers, 112 preattentive visual processing, 48 presentation graphics, 63 PRIM-9, 182 256 principles of design, 63 Python, xi Q questions charts answer, 69 R random charts, 228 random forest, 80 rank data, 233 ratio data, 97 real-time data, 69 recoding data, 208 rectangular coordinates, 50 reflective learning, 65 regular data, 101 relationships, 73 S scale divergent, 157, 209 double-ended, 157 interactive scale manipulation, 194 scatterplot matrix, 59 schema, 33 search engines, 90, 91 seasonality, 192 SeeNet, 216 seismograph, 106 selection calculus, 203 self-organizing map, 40 semantic map, 40 September 11, 209 shape, 158 Shape of Song, 118 shape of song, 118 shingling, 148 showing importance, 66 sidereal time, SimCity, 58 size, 157 small multiples, 55 smooth local, 36 loess, 36 moving average, 37 social networking, 111 Solomom, SOM, see self-organizing map space–time processes, 67 space-filling layout, 199 spatial data, 144 spectral analysis, 172 Index spherical coordinates, 50 splitting aesthetic, 154 SQL GROUP BY and splitting aesthetics, 156 stability in animation, 89 stacking, 112 standard date format, see ISO 8601 statistics interactive parameter manipulation, 191 step line, 176 stereotypes, 89 stock trades, 23 storytelling visualization, 86 Strasbourg Cathedral clock, streaming data, 69 summarizing aesthetic, 154 sunflower plot, 98 T tablelike chart, 211 tag cloud, 162 taxonomies 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Chambers D Hand W Hăardle For further volumes: http://www.springer.com/series/3022 Graham Wills Visualizing Time Designing Graphical Representations for Statistical Data 123 Graham Wills Hidden Spring... different calendar systems that have been employed across G Wills, Visualizing Time: Designing Graphical Representations for Statistical Data, Statistics and Computing, DOI 10.1007/978-0-387-77907-2... portrayal of time data is ubiquitous Any newspaper will feature time- based plots; any company report will show historical data as charts Even the gas bill for my home invites me to compare a time series