NEU_MFA_History_AllPapers by Mahima Pushkarna

ARTG 5110: hierarchical structures

LITERARY ORGANISM S T E FA N I E P O S AV E C by Mahima Pushkarna

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Literary Organisms (�ig 1) is a visualization of the structural and thematic breakdowns of Jack Kerouac’s book “On The Road”1. Created by Stefanie Posavec2, a London- based designer with a focus on data-related design, data art and visualization, Literary Organisms was a part of a her masters thesis project at Central Saint Martins. Her personal work is highly stylized and a majority of it revolves around the visual representations of language, literature and numbers. “Writing Without Words” is an exploration of representing text visually, allowing readers to compare the writing styles of different authors.

Literary Organism breaks down “On the Road” into four quanit�iable sections: book parts (volumes), chapters, paragraphs & sentences. Aimed at creating a system that would enable the comparision of the writing styles of various authors, the visualization presents a pictorial representation of sentence lengths, themes, parts-of-speech, sentence rhythm, punctuation and text structure at a glance. 3 Knowingly or unknowingly, Posavec has used data collection and represention methods that borrow greatly from those used in history. She counted words and sentences, splitting them into key themes using markers and highlighters. She “physically” worked with the data in an analog and tactile fashion, instead of calling upon the use of technologies that were at her disposal. After sifting through the data with the aid of a basic calculator and simple arithmetic, Posavec drew the infographic by hand in a vector graphics software, rather than running it though a visualization application. Literary Organisms condenses a text that originally comprised of over ten thousand words into a poster 33.1 x48.7 inches in size. The �inal graphic takes on a plant-like form, allowing viewers to perceptually extrapolate information at various levels of engagement. As a graphic, Literary Organism uses lines of similar weights to create a sense of continuity, but different colors to create distinction. The length of the lines preemptively allow the viewer to group them, creating an immediate identity for those assigned to chapters, paragraphs and sentences. A small key introduces a framework to understand the visualization, guiding and encouraging viewers to make sense of the visualization over and above it’s aesthetic appeal. The branching nature of it juxtaposed with the varying density of lines marries the values of a cartesian-area map with the structure of a polar node-link diagram, resulting in an inbetween visual display system.

“Using the sentence as the basis for my information visualizations meant I would be able to measure and map quanti�iable information that could be found easily throughout the English language. So, gathering numerical data based on the structure of the novel and of a sentence seemed to be the most appropriate solution.”3

In Literary Organisms, colors denote theme and thematic variations. Numerical representations break what are otherwise long lines, elucidating referenced quotations in the book. Short lines that are nested together pan radially clock-wise, indicating word counts. The entire structure follows an organic pattern in which (a) the spatial arrangement is given prominence over a linear grid and (b) the radial degree of space between chapters depends on the sub-structuring of the infographic, with chapters with greater data sets (chapter 11, 13, 3 & 4) accquiring

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more space than others. Proximity to the root does not represent any information as such, and has been determined more on aesthetic value. Literary Organisms, as a whole, satis�ies the de�inition of a hierarchical structure as de�ined by Meirelles4: “Ordered sets where elements and/or subsets are organized in a given relationship to one another, both among themselves and within the whole”. Hierarchical structures have been adopted in the past to visualize information pertaining to subjects ranging from geneaology, phylogensis, natural and hard sciences to organizational sciences and the liberal arts. Linear as well as radial representations have helped determine comparative correlations. They have helped create top-down or bottom-up organizations. Structurally, literary organisms can be categorized as polar node-link system. The book volume acts as a well-de�ined root, and the entire system can be compared to Haeckel’s “Paleontological Tree of Vertebrates” in The Evolution of Man (�ig 2). Both visualizations not only show the branching of parent streams, but also provide insights into the size or volume, through the area occupied by sections of families.

“Banking In�luence in Large Corporations”(�ig 3) displays a more delibrate attempt at merging a polar node-link structure and an area-map system. The values of the end variables are displayed by manipulating the size of the circles that represent them. Thus, the values of end nodes are not derived from emerging patterns, but are rather represented by the encompassing visual element itself. The original illustration from which this �igure was adapted uses three colors of ink – a luxury in the early 1900’s. The constraints and the opportunities presented by printing technology and media plays a vital role in determining icons, colors and even layouts. In contemporary times, vector graphics softwares and a plethora of printing options allow the inclusion of a large number of colors. However, Stefanie Posavec uses 12 colors, each one determined by the subject matter and functionality. By activating gestalt principles of proximity (to create preemptive grouping), similarity (to enhance semantic relationships) and continuity (to inject dynamism), the visualization creates various layers of information. The use of proximity and continuity to engage content is prominent in Hienrich Gustav Adolf Engler’s “Tree of Relationships of Plants of the Cashew Family Anacardiacae” (�ig 4). In their respective visualizations, Engler and Posavec use radial proximity to show divergences and create distinct family trees, supported by the patterns that emerge from the data that they visualize. Engler’s diagram shows eight different degrees of depth, while Posavec chooses to work within three degrees of separation. One must note that even though Posavec‘s depth of divergence is infact shallow as compared to Engler’s, Literary Organisms is far more rich in data. Engler’s work on a single, strict radial grid with eight seperations makes for a more traditional approach where a grid is respected as the basis for a visualization. Posavec breaks this notion of using a radial grid by incorporating several “proxy” roots, allowing the nested word charts to �low all around the end-node of a paragraph, which in turn, allow the nested paragraph charts to �low around the end-node of a chapter.

Steering clear of a traditional clock-wise structure that starts at a 90º or a 180º angle, the polar system used in Literary Organisms �inds the base orientation a little off-center and rotated clock-wise. This makes space for the branches to expand, accurately visualizing the data. Similar undertones of unusual orientation choices can be seen in Haeckel’s Tree of Vertebrates (Fig 2), and the angular balance can be

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referenced to Engler’s work. Such organic layouts adopted in polar systems and radial sunbursts can be found in infographics emerging from scienti�ic work tracing back to the early 1900’s. Chart A of “The Genealogical Tree of the Nam Family” by Arthur Estabrook and Charles Davenport (�ig 5), published in 1912 is evidence of this. The Nam Family Chart makes for an interesting example. At each node, a new variable is introduced which results in a much larger sample space of combinations. Several pathways appear to overlap, but they do not intersect. The analysis of the large amount of data conveyed in the Nam Family Chart appears to be the primary reason behind choosing a polar-node link structure, even though the same data set was explored in a cartesian node-link system (�ig 6).

Stefanie Posavec refrained from using a visualization software, instead choosing to “sketch” the visualization.3 In doing so, she deemed the visualization truly organic – in process and in aesthetics. The use of a plant and fractal-like aesthetic has surfaced several times in the history of infographics, particularly in those emerging from taxonomical studies in botany and zoology. It can be inferred from the sheer aesthetic qualities that visualizations of a large number of schemes of classi�ications display, that they are primarily inspired by plants themselves. An array of visualizations, ranging from Max Fürbringer’s “Pylogenetic Tree of Birds” (�ig 7) to Augustine Augier’s “Arbre Botanique”(�ig 8) bolster the inclination towards this choice of form. “I was then convinced that plants formed different series united by their base, observing between them a gradation like those of the branches of a tree: I then worked to make different series, and to establish their gradations.” – P.F. Steven5

Of course, we cannot ignore the fact that a majority of Stefanie Posavec’s work consists of explorations of radial lines – it is unknown to me whether this visual style was developed as a result of an interest in an organic aesthetic juxtaposed with the industrial appeal of linear lines, or if it was derived from the process and is speci�ic to the data it encodes. An interesting documentation that shows a highly stylized use of this plant-like aesthetic, is in the �ield of logistical management. In the year 1855, David McAllum6, the general manager of Erie Railroad created an organizational tree chart that mapped the New York and Erie Railroads(�ig 9). This tree chart was created for the employees who worked on the �ield at different stations to reduce accidents due to confusion. Made by hand, this map uses clusters of circles to represent different classes and numbers of employees, which results in a visual reminescent of Mughal Art (�ig 10) from the late 18th century.

Language structures and the lexical analysis of texts have served as a popular theme for exploration – be it Egyptian heiroglyphics (�ig 11) or the analysis of alphanumeric content emerging from social media(�ig 12). Modern technology has allowed for various adaptations of linguistic and language enquiry. Algorithms have helped sift through large amounts of text very quickly, allowing us to �ind thematic patterns and linguistic connections that could have been hitherto overseen. In “Practical Cosmophonography”7, Francis Fauvel-Gouraurd uses a simple cartesian node-link layout to create a Genealogical Table of Alphabets (�ig 13), based on the origins and progress of writings by Thomas Astel. This could possibly be one of the earliest modern uses of infographics and categorization systems used to document the origins of language, speech and writing. Collin et al’s DocuBurst (�ig 14), is a visualization of document content which takes advantage of the human-created

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structure in lexical databases in the form of a radial icicle8. Jeff Clark9 uses relational structures to create a network of words(�ig 15) used in Les Miserables10, using area as a dynamic variable to represent the frequency of words as they occur in the original text. Like Stefanie Posavec, he uses proximity and the segregation of �igure & ground to create a multi-level, yet simple representation of his dataset. In contrast, explorations of the frequency of phrases occuring in the bible by Chrisoph Romhild and Chris Harrison (�ig 16) focuses on the complexity of the dataset.

While most visualizations will invariably maintain strong connections to the past, Literary Organisms succeeds in displaying a more pronounced relationship to vintage infographics. It is a perfect example of a multi-layered hierarchical visual system with engaging content that reveals itself through patterns and connections, with a style that lends itself to the curious eye.

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Bibliography 1 Kerouac, J On The Road, 1957, Viking Press, 1999 Penguin Books ISBN-‐10 9780140283297 2 Stefanie Posavec Writing Without Words, n.d. Available from <http://www.stefanieposavec.co.uk/-‐ everything-‐in-‐between/#/writing-‐without-‐words/> 3 Posavec, “Writing without Words”, Design Mind (Frog Design) Vol. 08: Numbers, n.d. Available at < http://designmind.frogdesign.com/articles/numbers/writing-‐without-‐ words.html#/images/dm/numbers/writingwithoutwords_01.gif> 4 Meirells, I 2013 “Hiearchical Structures: Trees” in Design for Information ISBN 978-‐1-‐59253-‐806-‐5 Rockport, Massachusetts pp 17 5 Stevens, P.F., Augier, A. Augustin Augier's "Arbre Botanique" (1801), a Remarkable Early Botanical Representation of the Natural System Taxon, Vol. 32, No. 2 (May, 1983), pp. 203-‐211, International Association for Plant Taxonomy (IAPT), http://www.jstor.org/stable/1221972 Accessed: 26/09/2014 6 Rosenthal, C., Big Data in the Age of the Telegraph, March 2013, McKinsey & Company, accesible at < http://www.mckinsey.com/insights/organization/big_data_in_the_age_of_the_telegraph > 7 Fauvel-‐Gouraud, F. Practical Cosmophonography, 1850, J.S.Redfield, Clinton Hall, New York, accessible at < https://archive.org/details/practicalcosmop00ballgoog > 8 C. Collins, S. Carpendale, and G. Penn, “DocuBurst: Visualizing Document Content Using Language Structure,” Computer Graphics Forum (Proc. of the Eurographics/IEEE-‐VGTC Symposium on Visualization (EuroVis)), vol. 28, iss. 3, pp. 1039-‐1046, 2009 9 Clark, Jeff 2013 “Les Miserables Word Graph” accessible at < http://www.neoformix.com/2013/LesMisWordGraph.html > 10 Hugo, Victor, 1887 Les Miserables, translated by Hapgood, I.F., Pub, Thomas Y Crowell & Co., New York accessible under The Project Gutenberg license at < http://www.gutenberg.org/files/135/135-‐h/135-‐ h.htm >

Figures, Images & Diagrams 1 Posavec, Stefanie 2009 Literary Organisms Writing Without Words, accessible at <http://www.stefanieposavec.co.uk/-‐everything-‐in-‐between/#/writing-‐without-‐words/> 2 Haeckel, Ernst c1879 Paleontological Tree of Vertebrates 3 “Banking Influence in Large Corporations” from the New York Times Annualist as seen in Graphic Methods for Presenting Facts, Brinton, Williard Cope 1919, The Engineering Magazine Company, New York, Component Parts p. 13, accessible at < https://archive.org/details/graphicmethodsfo00brinrich >

4 Heinrich Gustav Adolf Engler, Top-‐down view of “Tree of Relationships of Plants of the Cashew Family Anacardiacae”, 1881 5 Estabrooke & Davenport , “Chart A, The Geneaology of the Nam Family”, The Nam Family: A study in Cacogenics, 1912, Eugenics Records Office, Memoir No.2, Long Island, New York 6 Estabrooke & Davenport , untitled, The Nam Family: A Study in Cacogenics, 1912, Eugenics Records Office, Memoir No.2, Long Island, New York 7 Max Furbringer, “Phylogenetic Tree of Birds” Bijdragen tot de Dierkune, tome XIII vol. XV, edited by J.Assezat, Amsterdam 1888 8 Augustin Augier 1801 “Arbre Botanique”, ), a Remarkable Early Botanical Representation of the Natural System Taxon, Vol. 32, No. 2 (May, 1983), pp. 203-‐211, International Association for Plant Taxonomy (IAPT), http://www.jstor.org/stable/1221972 Accessed: 26/09/2014 9 McAllum, D. “New York and Erie Railroad Organizational Chart”. Big Data in the Age of the Telegraph, March 2013, McKinsey & Company, accesible at < http://www.mckinsey.com/insights/organization/big_data_in_the_age_of_the_telegraph > 10 Unknown Mughal Tent Hanging, date unknown, origin: India, est. late 18th century, Museum number: IS.131-‐1950, Victoria and Albert Museum Collections, London 11 Papyrus of Ani, By en:user:Flembles (Bridgeman Art Library v. Corel Corp.) [Public domain], via Wikimedia Commons 12 Mood-‐map installation, E/B Office, Seoul, South Korea 2014, accessible at http://retaildesignblog.net/2014/01/17/mood-‐map-‐installation-‐by-‐eb-‐office-‐seoul-‐south-‐korea/ 13 Genealogical Table of The Alphabets based on the Origin & Progress of Writing by Thomas Astel, Fauvel -‐ Gouraud et al, “Practical Cosmophonography”, Published J. S. Redfield, 1850 p 28 14 Docuburst, C. Collins, S. Carpendale, and G. Penn, 2009, accessible at http://vialab.science.uoit.ca/portfolio/docuburst-‐visualizing-‐document-‐content-‐using-‐language-‐structure 15 Les Miserables Word Graph, Clarke, Jeff, 2013 accessible at http://www.neoformix.com/2013/LesMisWordGraph.html 16 Bible Cross-‐ References, Harrison, Chris, Römhild, Christoph n.d. accessible at http://www.chrisharrison.net/index.php/Visualizations/BibleViz

by Mahima Pushkarna | pushkarna.m@husky.neu.edu | NEU Fall 2014

ARTG 5110: spati0-temporal structures

TROPICAL DEPRESSION A95L

SCIENTIFIC VISUALIZATION STUDIO (NASA) by Mahima Pushkarna

[Above] Fig 1: A still from HS3â&#x20AC;&#x2122;s Tropical Depression A95L visualization [Left] Fig 2: Swedish Age Pyramid, Luigi Perozzo, 1880

Tropical Depression A95L Tropical Depression A95L is an animated visualization of wind flows from the tropical depression A95L (Fig 1) in the Gulf of Mexico on September 19 and 20, 2013. Created by Greg Shirah, Horace Mitchell and Lori Perkins at NASA’s scientific visualization studio, the visualization depicts data collected using wind vector data collected from the Hurricane and Severe Storm Sentinel Project [HS3]. The primary aim of this visualization is to study tropical depression systems and the factors that contribute towards the transformation of small storm systems into hurricanes 1. Tropical Depression A95L charts wind paths across time over a geographic region at different altitudes. There is a distribution element (wind vectors) in the visualization, accompanied by a temporal element (the period of time that the data has been collected across), and multiple spatial dimensions (geographic area and altitudes). Spatio-temporal structures can be defined as visualizations that use time-varying data found in diverse domains, occurring within a geographic or spatial boundary, in an attempt to understand natural or social phenomenon as well as to help make predictions 2. They can range from cartographic maps depicting complex movement patterns, to space-time cubes that depict data within multiple spatial and temporal dimensions. The HS3 animation is an example of a space-time cube that adopts animation to show the movement of wind currents across three dimensions of space and one temporal dimension. In physics, space-time is defined as any mathematical model that combines space and time into a single, interwoven continuum 3. According to Euclidean geometry, any point space can be assigned three dimensions and be defined by x, y, and z. By applying the concept of “space-time”, any point in space can be assigned an additional temporal dimension. Space-time cubes are information visualization techniques that display spatio-temporal data within a cube, commonly used to show geospatial data 4. Formally developed by Swedish geographer Torsten Hägerstrand 5 in the early 1970s along with the space-time prism, space time cubes can be said to be successors of Luigi Perozzo’s stereograms from the 1880s 6. It is interesting to note that Perozzo and Hägerstrand were both statisticians working with cultural data within a geographic context. Nearly 90 years prior to Hägerstrand’s seminal paper establishing the use of space-time cubes for geocoding cultural data, Perozzo used 3-D schematics to convey age distributions of the Swedish population across time (Fig 2). While the animation of Tropical Depression A95L does not traditionally fall into the category of a space-time cube, it can be considered as a modification of the space-time cube through inclusion of modern technological allowances. The use of dropsondes for data collection has allowed for richer datasets, and technological advances such as algorithmic

Fig 3: Temple of Time, Emma Willard, 1846

Fig 4: Light & Heat Diagram, ASA Smith, 1850

Fig 4 : Annual Probabilities of Dying

and data-driven animations in contemporary times provide an edge to create multi-dimensional visualizations. One could say that space-time cubes are the result of layering data across two or more dimensions and analyzing its movement within yet another dimension. It charts the movement of a data point on a two-dimensional graph at several different points in time, stacked one on top of another. The animation of the tropical depression portrays geospatial data for the x- and y- axes, and altitude (in kilometers) in the z- axis. The movement and speed of winds as recorded by dropsondes is depicted through animated strokes of different colors denoting the altitudes (which are also demarcated by translucent white planes). By making an animation out of data that could in fact be represented as a static output (using color to denote altitude and the z-axis to denote time), visualizers at the Scientiﬁc Visualization Studio intensiﬁed the efficiency of the visualization. By depicting time through animation, and changing camera angles they avoided occlusions of data-points and gave possible patterns greater clarity, allowing for a greater exploration of details. While using animation as a mode of visualization has overcome several traditional drawbacks of space-time cubes, such an animation is incomplete without an explanation. It suffices for those familiar with the data, but in order to convey inferences, we need textual explanations or supporting informational diagrams.

Modes of 3-d graphical representations have been used to show multi-dimensional data across a range of domains in the past. Emma Willard’s Temple of Time (Fig 3) uses 3D space quite literally to depict historical figures, biological charts, and chronological stream charts in a single diagram. Made in 1846, she emulates a memory theatre in a Greek temple- styled space. Columns on the left represent important historical figures in the old world, while columns on the right represent those in the new world. They are connected by the ceiling, on which a chart of biography is drawn. The floor depicts a chronological stream chart drawn perpendicular to the columns, adding to the perspective of the temple 7. This was perhaps a precursor to space-time cubes. Not soon after, "An Abridgment of Smith’s illustrated Astronomy”8 depicted the proportionality of light and heat on a planet to its distance from the sun using forms of three dimensional representation (Fig 4). This was probably because of the need to depict squares of distances, which could not be done as effectively when constrained by a two dimensional plane. Space-cubes have been used extensively to geocode cultural phenomenon and mark movements over time within geographic spaces. Thematically similar to Luigi Perozzo’s Swedish Age Pyramid is Nicolas Brouard’s “Annual Probabilities of Dying” (Fig 5). Brouard uses isoline frequencies within a stereogram to depict the annual probabilities of death in the French male

population according to age and period9. Though this was made in 1997, it is visually similar to Perozzo’s population schematic from the 1800s, despite being computer-generated. Kraak visualized Minard’s infamous sankey diagram, “Napolean's March to and from Russia”, in the form of an interactive space-time cube (Fig 6), allowing us to examine it layer-by-layer10. Today, space-time cubes are used extensively in geography, be it to visualize North Carolina’s Coastline evolution (Fig 7) or to plot the distribution of flu-like and stomach diseases within a fixed duration of time in a specified geographic area (Fig 8). A lot closer to the visualization of Tropical Depression A95L is Martin Schowenberg’s “Movement of Rain Clouds over Ethiopia” (Fig 9), both in terms Fig 6: Napoleon’s March To and From Russia, Kraak, 2002

of visual encoding, representation structure and content themes. If one were to compare Perozzo’s Demographic Stereograms (Fig 10), Brouard’s population diagrams and Thomas Pullum’s “Age-speciﬁc Fertility of White Women, 1980” (Fig 11), to recent space-time cubes, it is observed that a larger amount of visual evolution has occurred in the past twenty years. On a side note, other popular methods of using 3 dimensions in visualizations include, but are not limited to architectural (axonometric) drawings, pop-up books, orientation diagrams, and exploded diagrams.

Fig 7: North Carolina’s Costal Evolution, Mitasova & Thakur, u.d.

The visualization uses a combination of photo-realistic rendering and vector-styled graphics to distinguish between context and content. 3D renderings of land topography define the spatial region within which the storm system was observed. While there is no indication of timeframe of observations, four white vertical axes with ticks indicate different altitudes at which wind fields were observed. The altitudes were exaggerated ten times in the visualization to demarcate different layers in the atmosphere. To reinforce the altitude and wind field being showcased, translucent white planes are over-laid, giving us a sense of depth and area without interfering with the surrounding topographical depiction. The axes are shown using thick translucent cylindrical lines at the corners of the fields. A smaller cylindrical bar that appears metallic in nature moves up and down in tandem with the white planes bringing into focus a series of short strokes that depict particle movement through the flow field. These “moving” strokes are similar to meteor paths, with heavy stroke weights at the head and vanishing weights at the tails, providing a sense of movement. Accompanied with animations, they clearly present patterns within fields of wind. Presumably, the length of the strokes is related to the data gathered by the dropsondes for the altitude and period of observation. As the field altitude increases or decreases, the colors of the strokes change, leaving strokes below the field of observation with minimal opacity. Wind fields near ground-level are shown in white, and the highest wind fields observed at 15 km are shown in red. Ticks are placed at 5 km intervals: wind fields at 5 km are represented using yellow, and those at 10 km are orange in color. The gradual shifts in stroke colors, accompanied by opacity fade-outs create a smooth visual transition that makes perception of change

Fig 8: Spatio-temporal distribution of the lu-like and stomach disease, Andrienko, Andrienko et al, Springer, 2013

Fig 9: Movement of Rain Clouds over Ethiopia, Martin Schowenberg, 2011

Fig 10: Demographic Stereograms, Luigi Perozzo, 1881

Fig 11: Age-speci ic fertility of White Women, Thomas Pullum, 1980

and pattern-detection easier. The four axes at each corner of the horizontal moving window prevent data loss due to overlapping data points: reference scales are made constant. A lot of the visual elements are knowingly or unknowingly borrowed from the past. For example, the choice of color palettes is highly intuitive. "The Study of Sky and Trees” by John Constable (Fig 12) particularly comes to mind. Constable assigns greens and browns to land, and white to winds and clouds. The use of arrows to show wind drafts, temperature flows and water currents is a popular practice. The layering of wind flow fields in the visualization of the Tropical Depression A95L is an important visual-conceptual element to aid

Fig 12: Study of Sky and Trees, John Constable, 1821

contextual understanding. Similar attempts to isolate certain geo-spatial regions can be seen in several diagrams of a Historical Atlas (Fig 13) by Edward Quin in 1828. Visual marks such as arrows are similar to the strokes used by the Scientific Visualization Studio, with heavier heads and thinner tails that imply movement. The use of shorter strokes instead of long ones as seen in the map of “Pressure and Predominant Winds and Temperatures of the World” (H. Bayer, 1953) (Fig 14) encourages us to perceive each stroke independently. This form of depiction not only brings clarity to movement patterns, but it also makes diagrams visually more efficient. While it may have been far less feasible to create animations in the past, we can draw links between the reasoning behind Pullum's provision of different fields of vision in his diagrams on Age Specific Fertility in White Women, and the use of multiple camera angles in the Tropical Depression A95L visualization. Perhaps the use of three different perspectives in a single diagram provides similar inference-detection capabilities as animated visualizations. The Tropical Depression A95L visualization data was collected in an attempt to "better understand why some storms develop into hurricanes and others do not". According to the literature, "lower level winds show cyclonic circulation associated with tropical disturbance. But, just above those cyclonic winds, the storm is thwarted by wind shear, prohibiting further development into a tropical cyclone”. As humans we have sought new lands and explored vast seas to reach civilization’s present disposition. Wind data has ben crucial to the success of oceanic voyages and land expeditions. As early as the 1500’s, “wind roses” (graphic elements that depict wind speed and direction within a certain spatial range) showed up on navigation maps. In several cases, the Fleur de Lis and the Cross of Christianity was used to provide directions11, setting up the wind rose as the possible ancestor to present day compasses. Wind roses were in certain cases a combination of two diagrams, as seen in Fig 15, and in certain cases a single exquisitely ornate diagram. In Jean Rotz’s “View of North Atlantic Coast” (Fig 16), one can see several wind roses placed at different locations of the map, while Jacques de Vaulx applies different styles to different wind roses (Fig 17).

Fig 13 : Historical Atlas, Edward Quin, 1828

Fig 14 : Pressure and Predominant Winds and Temperatures of the World, H. Bayer, 1953

Fig 15 : Wind Roses, Unknown Author & Date, UCB Library Collection

Fig 16 : View of North Atlantic Coast, Jean Rotz, 1542 - British Library Collection

Fig 17: Wind Rose, Jacques De Vaulx 1582 - National Library of France

Fig 18: August and September Hurricane of 1848, M.F. Maury, 1850

Fig 19: Trade Wind Chart of the Atlantic Ocean, M.F. Maury, 1858 Reprint

M.F. Maury worked extensively with wind data around the 1850s. Back then, data was drastically different and less multi-dimensional than it is today, and the purposes of visualizing such data was more explanatory that exploratory. The graphic structures used to visualize wind data were also constrained by printing and visualization technologies. Most of them were created by hand, and optimized for printing methods like lithography. Maury visualized the August and September Hurricane of 1848 (Fig 18) on a two-dimensional map, using color and thickness of a sinusoidal graphic element to chart it's intensity and path. He also created another Trade Wind Chart of the Atlantic Ocean (Fig 19) in 1851 (reprinted in 1858), meant for sailors to geolocated different winds at different times of the year. He created a complex matrix that depicted latitude and longitude as spatial dimensions, months of the year as a temporal dimension, and differently colored “grounds” with markings, to chart wind paths and intensities. Due to the complexity of this matrix, the chart was printed with an explanation of the various marks on the map, as well as instructions on how to use it through the means of an example of an “imaginary ship” traversing through different grounds12. Nowadays, the use of satellites, buoys and other technological marvels allow us to receive weather related data in real-time. We are able to use predictive algorithms to chart out the path of impending weather catastrophes, and are able to issue states of emergency. Modern depictions of wind data range from the aesthetically appealing Wind Map from Hint.fm (Fig 20) to exploratory analysis of data as is in the visualization of Tropical Depression A95L. Space-time cubes are a visualization technique that we will see more of in the future, as data becomes increasingly dimension-rich.

Fig 20 : Hurricane Sandy Wind Map, hint.fm, 2013

Bibliography 1. Tropical Depression A95L, Shirah, G., et al., NASA’s Scientific Visualization Studio, 2014, “HS3 Global Hawk Observes winds from tropical depression A95L in September 2013”, accessible at http://svs.gsfc.nasa.gov/cgi-‐bin/details.cgi?aid=4210

2. Spatio-‐temporal Structures, Ch.5, p.159, “Design for Information”, Meirelles, I., Rockport Publishers, 2013 3. Space-‐time, accessible at http://en.wikipedia.org/wiki/Spacetime 4. Space Time Cube, “An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns”, Kristensson, P.O., Dahlback, N., et al, IEEE Transactions On Visualization and Computer Graphics, Vol. 15, no. 4, 2009 5. Hägerstrand, accessible at http://en.wikipedia.org/wiki/Torsten_H%C3%A4gerstrand 6. “Demographic Stereograms”, Perozzo, L.,1881, accessible at http://cirdis.stat.unipg.it/files/Sperimentazione/GraficiStorici.html 7. “Temple of Time”, Willard, E., 1846, p.201, “Cartographies of Time”, Rosenberg, D., Grafton, A., Princeton Architectural Press, 2010 8. “An Abridgement of Smith’s Illustrated Astronomy”, Smith, A., Cady & Burgess, 1850, accessible at archive.org 9. “Annual Probabilities of Dying, French Male Population”, Brouard, N., p.73 “Demography -‐ Analysis and Synthesis: A Treatise in Population”, Caselli, G., Academic Press, 2005 10. “Napolean’s March to and from Russia”, Kraak, 2002, accessible at http://www.itc.nl/personal/kraak/1812/3dnap.swf 11. Wind Rose, accessible at http://en.wikipedia.org/wiki/Wind_rose 12. From the explanation provided in “Trade Wind Chart of the Atlantic Ocean”, Maury, M.F., 1851, reprint 1858, David Rumsey Map Collection

List of Figures

1. Tropical Depression A95L, Shirah, G., et al., NASA’s Scientific Visualization Studio, 2014, “HS3 Global Hawk Observes winds from tropical depression A95L in September 2013”, accessible at http://svs.gsfc.nasa.gov/cgi-‐bin/details.cgi?aid=4210 2. “Swedish Age Pyramid”, Perozzo, L., p.70, “Demography -‐ Analysis and Synthesis: A Treatise in Population”, Caselli, G., Academic Press, 2005 3. “Temple of Time”, Willard, E., 1846, p.201, “Cartographies of Time”, Rosenberg, D., Grafton, A., Princeton Architectural Press, 2010 4. “Light and Heat on Planets”, p. 61, “An Abridgement of Smith’s Illustrated Astronomy”, Smith, A., Cady & Burgess, 1850, accessible at archive.org 5. “Annual Probabilities of Dying, French Male Population”, Brouard, N., p.73 “Demography -‐ Analysis and Synthesis: A Treatise in Population”, Caselli, G., Academic Press, 2005

6. “Napolean’s March to and from Russia”, Kraak, 2002, accessible at http://www.itc.nl/personal/kraak/1812/3dnap.swf 7. “North Carolina’s Coastline Evolution”, Mitasova, H., Thankur, S., date unknown, accessible at http://renci.org/research/nc-‐state-‐projects/ 8. “Spatio-‐temporal distribution of Flu-‐like and Stomach Disease”, Andrienko, Andrienko, et al., 2013, accessible at http://geoanalytics.net/vam/ 9. “Movement of Rain Clouds over Ethiopia”, Schouwenberg, M., 2011, accessible at http://blog.52north.org/2011/09/26/ilwis-‐and-‐3d/ 10. “Demographic Stereograms”, Perozzo, L.,1881, accessible at http://cirdis.stat.unipg.it/files/Sperimentazione/GraficiStorici.html 11. “Age-‐specific fertility of White Women”, Pullum, T., 1980, p.72 “Demography -‐ Analysis and Synthesis: A Treatise in Population”, Caselli, G., Academic Press, 2005 12. “Study of Sky and Trees”, Constable, C., 1821, V&A Museum Collections 13. “Historical Atlas”, Quin, E., p. 128-‐129, “Cartographies of Time”, Rosenberg, D., Grafton, A., Princeton Architectural Press, 2010 14. “Pressure and Predominant Winds and Temperatures of the World”, Bayer, H., 1953, David Rumsey Map Collection 15. “Wind Roses”, Artist Unknown, Date Unknown, UBC Library Digital Collection 16. “View of North Atlantic Coast”, Rotz, J., 1542, British Library Collection 17. “Wind Rose”, Vaulx, J. d. , 1582, National Library of France 18. “August and September Hurricane of 1848” Maury, M.F., 1850, David Rumsey Map Collection 19. “Trade Wind Chart of the Atlantic Ocean”, Maury, M.F., 1851, reprint 1858, David Rumsey Map Collection 20. “Hurricane Sandy”, Wind Maps, 2013, accessible at hint.fm

By Mahima Pushkarna | pushkarna.m@husky.neu.edu | NEU Fall 2014

ARTG 5110: spatial structures

THE GRAVITY OF DROUGHT

CHRIS POULSEN by Mahima Pushkarna

Fig 1: The Gravity of Drought

The Gravity Of Drought Made by Chris Poulsen, "the gravity of drought” [figure 1] is a scientific visualization adapted editorially for a web article of the same name. Chris Poulsen, a geospatial analyst at the National Drought Mitigation Centre, created this visualization using data from twin satellites from NASA’s GRACE Program 1. It is unique in the sense that it was not a designer’s commissioned or personal visualization, but made by someone who wanted to directly analyze the data and find patterns within it. The audience is more than just the layman who reads the article. It is also targeted at a range of scientists, geographers and cartographers, extending to ecology enthusiasts. Spatial maps have been defined as “a diagram or collection of data showing the spatial distribution of something or the relative positions of its components”2. A standard approach to visualize data is to use a color encoding the geographic area, resulting in a chloropleth map” 3. Based on these definitions, this map can be categorized as a traditional spatial map, though as a non-traditional chloropleth. This distribution map shows measurements of the water stored underground, in aquifers in the U.S. during the week of November 28, 2011 4.The maps has 3 visual layers of information: the first is a geographic outline of the United States of America, that gives us a sense of the overall space being represented. A fairly thick stroke of black tells us that the area in consideration is solely USA, freeing us from complications of area representation due to cartographic projection systems. The second layer is the demarcation of all the states, excluding Alaska and Hawaii. Lines of lighter weight keep them from being confused with the heavier outline of the country. This provides us with a conceptual sense of the estimated underground water per state, and seems to point towards a possible omission of the impact of political policy on water consumption trends and drought preparedness. The final visual layer is that of the hard data coded onto the geographic layers. It uses a divergent color scheme to represent the wetness percentile of the land, providing us with a quick-and-rough idea of the least and most drought prone regions in the United States.

Top to Bottom Fig 2: Surface Soil Moisture Fig 3: Root Zone Soil Moisture Fig 4: Ground Water Storage

The visualization is part exploratory and part explanatory. Since it is a part of an animated time series, once could say that it is exploratory in nature. It provided scientists with a long-term average to compare immediate data against, revealing the long-term effects of drought. Hence, it can also be classified as explanatory. Chloropleths often need legends to support the actual visualization. Legends give a sense of the data represented by color: if the data was normalized or not, or if dark signifies greater intensity or lesser intensity, etc. However, the data represented in this particular static visualization, feels incomplete. Sans legend or animation, it coveys little to no meaningful information. Since the data depicted in this visualization has to do with underground water storage, visualizations of affecting factors such as Surface Soil Moisture [figure 2], Root Zone Soil Moisture [figure 3] and Ground Water Storage [figure 4] are needed in order to allow actual comparisons of the original visualization against the long-term average. This allows the proposed inferences to be validated against known yardsticks. For example, dark red regions represent dry conditions that should occur only 2 percent of the time

Fig 7 : Population Density Fig 5: Mouvement des Voyageurs et des Merchandises dans les Principales Stations de Chemins de fer en

Fig 6 : Population Problems, Atlas of Global Geography

Fig 8 : Plate 72, Foreign Born Population

or once every 50 years 5. If one were to follow Ben Shneiderman’s Task by Type Taxonomy 6, the data presented would be (multi-layered and) 2-dimensional in the static visualization, and (multi-layered and 2-dimensional and) temporal in the animated time-series. The visualization is multivariate since it shows wetness and dryness, classified by geography across time, but is a non-traditional chloropleth since the color encoding is not specific to administrative (or geo-political) units, and is instead specific to land area as a constant. In a way, one could say that it is a combination of a chloropleth distribution diagram and a geographic map. Using two or more diagrammatic styles is not a new practice, and has been often employed in the past. Visual elements like color, area and shape are used to show variance in different variables, and are placed against a common field - geographic or temporal, to provide grounds for comparison. Spatial maps have formed the basis of almost every kind of data representation, ranging from exploratory maps of ancient voyages, explanatory maps of the universe, all the way down to territorial maps and backdrops for statistical maps. The “Mouvement des voyageurs et des marchandises dans les principales stations de chemins de fer en” map from 1882 juxtaposes enclosure diagrams that use divergent color schemes against a spatial map to show the movement of passengers and freight [figure 5]. Chloropleths or color value-graduated maps have been used extensively in the past to map social phenomenon such as population statistics [figure 6,7], movement [figure 8], employment and illiteracy rates in political counties, as well as in geography to map regions of matching altitudes and physical features, and weather phenomenon such as humidity, pressure, etc. The idea of using graduated color values (as against distinct colors) can be credited to Pierre Charles Dupin, who created the first chloropleth map of France that showed statewide illiteracy rates in 1826 [figure 9]. Balbi and Guerry further adopted this style, and they created a series of Moral Chloropleths [figure 10] three years later. However, chloropleths often present their own limitations. According to Meirelles, "one of the problems with chloropleth maps is that the size of the area base for the encoding influences the perception of the quantity being represented" 7. Andrienko, Andrienko and Savinov, in their paper “Chloropleth Maps: Classification Revisited” 8, say "an analyst cannot be satisfied with a static chloropleth map with a fixed classification because such a map conveys just one of numerous spatial patterns possible.” This can be interpreted as a limitation of static chloropleths in that they tend to omit several important spatial or physical features that may contribute to the density of data counts of each region, adding to the increasing gap of between the perception of quantity and actual quantity presented. Using chloropleths in alliance with other pertinent visualization types, and in more recent times, the ability to present multi-layered interactive information or “details-on-demand” 9, are examples of solutions to overcome the cons of chloropleths. Water and water accessibility has been one of the primary factors influencing the colonization of mankind. Settlements were often created around river and

Fig 9: Illiteracy Rates in Paris (Charles Dupin)

Fig 10: Moral Chloropleths (Balbi & Guerry)

Fig 11 : Nansenbushu Bankoku Shoka No Zu

Fig 13: Map Illustrating the Extermination of the American Bison

lake banks, or other rich sources of water. This ensured fertility of land, abundance in vegetation, flora and fauna, which in turn ensured the availability of raw materials and fresh produce for human survival. The history of mapping often finds it’s roots in the spatial: the land and space around us is often a point of reference for exploration. This implies that a majority of historical documentation could be in the form of maps: physical, political, social and economical maps. Water features and land topographies is a well-explored subject in cartography. Maps showing water systems can be found dating well back into history. What is considered to be the first Japanese printed map of all the continents, the “Nansenbushu Bankoku Shoka No Zu” or “Outline Map of All Countries of the Universe” [figure 11] focuses greatly on the Mansarovar lake in India, and the depiction of four great rivers: the Indus, the Ganges, the Satluj and the Bhramaputra 10. One must note that the symbology in such maps, often created by monks, represents space spiritually and symbolically rather than realistically 11. The Nansenbushu Bankoku Shoka No Zu was printed using woodcut blocks in 1710, by the Buddhist monk Rokashi Hotan, basing it on the pilgrimage narrative of another monk. “There is a close relationship between pattern discovery and superstition since humans and animals alike excel at finding structures where there are none.” 12

Fig 13: Geological Map of England and Wales and Part of Wales (William Smith)

Humans have an intrinsic inclination towards finding patterns and documenting them in order to progress. We understand that our actions have consequences, and in order to understand these consequences better, we keep records of polarities. This visualization was created to aid the understanding of groundwater recharging speeds as a drought indicator. “Groundwater takes a long time to be depleted, but it takes a long time to be recharged as well” 13. In showcasing the wetness percentile, the visualization shows regions that are drought-prone as well as those that are water-rich. Thematically speaking, it is similar to the Map illustrating the extermination of the American Bison, prepared by W.T. Hornaday [figure 12]. The maps uses what are canonically considered as opposite colors - blue and red, with the addition of varying stroke styles, alphanumeric notations and an addition of green to show ten different variables (see image). While the gravity of drought series of visualizations may not necessarily show such a wide range of inter-related variables, it expresses the notion of polarity documentation. Chris Poulsen’s visualization can be considered to be a thematic map since it plots quantifiable data against spatial territories. This is not to far from physical maps, such as William Smith’s “Geological Map of England and Wales and Part of Scotland” in 1815 [figure 13]. Thematically closer maps can be found in the Pergamon World Atlas, published in 1967 by the Polish Army Topography Service. A subset map of the Australian continent titled “Underground Water” [figure 14] uses a combination of three textures and four colors to encode the different qualities of water available across three different types of basins. Less than a decade after the Pergamon World Atlas was published, The California Governor’s Office of Planning and Research commissioned Bowen, Brand, et al. to map the drought and groundwater during the water years of 1976 and 77 in California. This was done in conjunction with the California Department of Water Resources and several other agencies. Published in a chapter entitled

Fig 15 : Drought & Groundwater. Water Years 1976-77. Chapter 7: The Operation of Modern Water System

“The Operation of Modern Water System” [figure 15], the presence of these maps indicates a strong correlation between waterworks-related policies and strategies, and the occurrence of droughts and availability of groundwater. Methods of data collection and the nature of the data collected often have a great impact on the visualization. Chris Poulsen’s visualization series uses data from NASA’s Gravity Recovery and Climate Experiment (or GRACE) satellites. Traditionally, hydrologists use wells to measure underground water. They were skeptical about GRACE’s ability to measure water from space using principles of gravity. After verifying the credibility of data acquired from the satellites against parallel measurements from known aquifers, the satellites set out to measure groundwater across the United States 14. This data was normalized and used to populate the visualization. Due to this unique collection method, the dataset is free from geo-political boundaries, and lacks the impact of any other physical or social elements. However, this method also affects the resolution and nature of data, causing the elements of representation to resemble blown up pixels that depict raster elements. This is visually similar to single-layer maps created by Howard Fisher using the Synagraphic Mapping System back in 1963, where layers of grid cells were overlaid to created smoother [figure 16] transitions that low raster and basic computing of GIS data could allow 15. This was previously possible only by the use of transparencies. The visualization of GRACE’s data can also be compared to the results of computer generated rule-based designed spatial maps from the Harvard Laboratory for Computer Graphics and Spatial Analysis in 1968 [figure 17]. The gravity of drought uses points as primary elements of representation, but the resolution of data converts these points into planes. This can be attributed to the fact that the twin satellites are constantly separated by an average of 200 kilometers 16, and therefore the grid on which they operate places the resultant data on a plane representation system, rather than a point representation system. This gives the data two dimensions, providing viewers with a sense of space and scale. The data presented ranges from 0 to 100, with graduated color values and hues stepping up at normalized intervals. Values of red step at 2, 5, 10, 20 and 30 towards white, and transition towards blue at 70, 80, 90, 95 and 98. A long-term average of groundwater between August 2002 and 2012 has been used as a baseline to compare this data against. By using the continuous standard normal probability distribution, the data presented is more symmetrical, and allows Poulsen to accommodate outliers and otherwise unlikely extreme measurements 17. This a technique often used when dealing with large, quantitative datasets. It gained popularity particularly in the 1970s, though De Moivre discovered it in 1738 and refined and formalized by Gauss in 1809 18. Color graduation has been used extensively in the past, especially in the generation of topographic maps. A stunning example is Marie Tharp’s mapping of the ocean floor, called the World Ocean Floor Panorama [figure 18], published in 1977 and painted by Heinrich Berann, an Austrian painter. Marie Tharp hand-plotted and drew the entire ocean floor using SONAR readings [figure 19], which led to invaluable contributions in understanding underwater topography 19. Once could say that the data collection method presented here is similar to what the GRACE satellites did, since both datasets emerged from underground phenomenon

Fig 16: Conformant & Contour Maps (SYMAP)

Fig 17: Computer Generated Rule Based Design

Fig 148: Manuscript of the Heezen-Tharp Workd Ocean Floor Panorama

Fig 19: Marie Tharp at Work in the Early 1960â&#x20AC;&#x2122;s.

Fig 20 : Bunsen Burner Flame Types

that cannot be physically measured with the precision required yet. Heinrich Bern’s choice of divergent color schemes that employ the use of blue and yellow is easily explained: Humans associate blue to water, and yellow to land. Chris Poulsen’s choice of similarly pre-emptive color schemes can be explained just as easily: as humans, we relate red to hot and dry, and blue to wet and cool (even though this association may be thermodynamically flawed blue flames are higher in core temperature that red flames) [figure 20]. Additionally, natural laws dictate that blue and red are on far other ends of the visible spectrum, as is evidenced by several spectrum analyses [figure 21]. It is important to note that without the presence of a legend and disruptive text, these series of spatial thematic maps could be incorrectly linked to popular (binary and multistep) vote maps in bipartisan United States [figure 22], with red symbolizing votes inclined towards Republicans, and blue towards Democrats 20. With the proliferation of big data across a range of subjects and the limited availability of colors, the lack of descriptions can often times result in incorrect interpretations of spatial maps. As designers, we should acknowledge the public tendency to misinterpret data and create our visualizations accordingly. Chloropleths are a highly engaging system of data visualization, and offer us the facility of combination. They can be used to create multivariate maps that embody multidimensional, if created unerringly, to aid exploration and explanationn

Fig 21: Spectroscopy of the Sun

Fig 22: Maps of the 2012 US Presidential Election Results by County (Mark Newman)

Bibliography 1. Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 2. Map n. Oxford English Dictionary Online, 3rd Edition, September 2000 3. Heer,J., Bostock, M., Ogievetsky, V., “A Tour Through the Visualization Zoo”, Communications of the ACM, 53(6), pp. 59-67, Jun 2010. 4. Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 5. Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 6. Shneiderman, B. “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization”, In Visual Languages, 1996. Proceedings, IEEE Symposium on, 336-43 7. Meirelles, I. “Design for Information” p 142, Rockport 2013 8. Andrienko, G., Andrienko, N., Savinov, A., “Chloropleth Maps: Classification Revisited” p2, GMD – German National Research Center for Information Technology 9. Shneiderman, B. “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization”, In Visual Languages, 1996. Proceedings, IEEE Symposium on, 336-43 10. “Nansenbushu Bankoku Shoka No Zu”, Author Unknown, 2010, accessible at: http://www.geographicus.com/blog/rare-and-antique-maps/antique-map-of-the-week1710-nansenbushu-map-of-the-world/ 11. Laffon,C., Laffon, M. “Mapping the World: Stories of Geography”, 2009, Firefly

Books, Cataloging-in-Publication (U.S.), p82 12. Riegler, Alexander. 2007. “Superstition in the Machine.” In Anticipatory Behavior in Adaptive Learning Systems, edited by Martin V. Butz, Olivier Sigaud, Giovanni Pezzulo, and Gianluca Baldassarre, 4520:57–72. Berlin, Heidelberg: Springer. 13 Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 14. Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 15. Chrisman, N., “Charting the Unknown”, p5, date unknown, accessible at: http://www.gsd.harvard.edu/gis/manual/lcgsa/HarvardBLAD_screen.pdf 16.Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 17. Hayes, A. “Statistical Methods for Communication Science”, p98, Ohio State University, Lawrence Erlbaum Associates, NJ 2005 18. History of Normal Distribution, Development. Accessible at http://en.wikipedia.org/wiki/Normal_distribution#History 19. Maxwell, R., “Marie Tharp and Mapping the Ocean Floor”, 2013, accessible at: http://www.gislounge.com/marie-tharp-and-mapping-ocean-floor/ 20. Newman, M. “Maps of the 2012 US presidential election results” 2012, accessible at http://www-personal.umich.edu/~mejn/election/2012/

List of Figures 1. Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 2. Surface Soil Moisture, Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 3. Root Zone Soil Moisture, Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 4. Ground Water Storage, Riebeek, H., visualizations by Poulsen, C., ”The Gravity of Drought”, 2012, accessible at: http://earthobservatory.nasa.gov/Features/GRACEGroundwater/page1.php 5. Cheysson, E., “Movement des Voyageurs et des Merchandises dans les Principales Stations de Chemins de fer en”, 1884–1886, “Albums de Statistique Graphique”, French Ministry of Public Works. Friendly, M., p19, “A Brief History of Data Visualization”,2006, Springer-Verlag 6. Raisz, E. “Population Problems”, Atlas of Global Greography, 1944, Harper & Brothers, New York. From the David Rumsey Map Collection. 7. Polish Army Topography Service, “Population Density” 1967, Pergamon World Atlas. Pergamon Press, Sluzba Topograficzna W.P. from the David Rumsey Map Collection 8. Paulin, C., Wright,J. “Plate 72, Foreign born Population”, 1932, Atlas of the Historical Geography of the United States, New York, from the David Rumsey Map Collection

9. Illiteracy Rates in France, Dupin, Charles (1826). Carte figurative de l'instruction populaire de la France. Jobard, accessible at: http://euclid.psych.yorku.ca/SCS/Gallery/milestone/sec5.html 10. Guerry, Balbi “Moral Chloropleths”, 1833, Statistique Morale de La France, Paris, accessible at: http://libweb5.princeton.edu/visual_materials/maps/websites/thematicmaps/quantitative/sociology-economics/sociology-economics.html 11. Hotan, R., “Nansenbushu Bankoku Shoka No Zu”, 1710, from The David Rumsey Map Collection 12. Hornaday, W.T., “Map Illustrating the Extermination of the American Bison”, 1887, Library of Congress, accessible at: http://lcweb2.loc.gov/cgibin/map_item.pl?data=/service/gmd/gmd3/g3301/g3301d/ct000308.jp2&itemLink=D?g md:15:./temp/~ammem_Ajnw::&title=Map+illustrating+the+extermination+of+the+Ame rican+bison+/+prepared+by+W.T.+Hornaday+;+compiled+under+the+direction+of+Hen ry+Gannett,+E.M.&style=cnsvmap&legend= 13. Smith, W., “Geological Map of England and Wales and Part of Scotland”, 1817, accessible at: http://www.unh.edu/esci/WilliamSmiths-StrataIdentified/i/scaledmap.html 14. “Underground Water, Australia”, Pergamon World Atlas, 1967, p367-368, Pergamon Press, Sluzba Topograficzna W.P. from the David Rumsey Map Collection 15. Bowen, Brand, et al., “Groundwater” “Drought. Water Years 1976/77. Chapter 7. The Operation of Modern Water System” 1979, The California Water Atlas, Sacramento, State of California. From the David Rumsey Map Collection. 16. Fisher, H., Conformant & Contour Maps Synagraphic Mapping System (SYMAP), 1963, Harvard GSD, accessible at: http://www.esri.com/esrinews/arcnews/summer13articles/beginnings-of-geodesign-a-personal-historical-

perspective 17. Stienitz, C., “Computer Generated Rule Based Design”, 1968, Harvard GSD, The Harvard Laboratory for Computer Graphics and Spatial Analysis. accessible at: http://www.esri.com/esri-news/arcnews/summer13articles/beginnings-of-geodesign-apersonal-historical-perspective 18. Berann, H., “Manuscript of the Heezen-Tharp World Ocean Floor Panorama”, 1977, Library of Congress, accessible at: http://lcweb2.loc.gov/cgibin/map_item.pl?data=/service/gmd/gmd9/g9096/g9096c/ct003148.jp2&style=cnsvmap &itemLink=D?gmd:17:./temp/~ammem_Ajnw:&title=[Manuscript%20painting%20of%2 0Heezen-Tharp%20%22World%20ocean%20floor%22%20map%20by%20Berann] 19. Marie Tharp at Work in the Early 1960’s, “Marie Tharp and Mapping the Ocean Floor”, 2013, accessible at: http://www.gislounge.com/marie-tharp-and-mapping-oceanfloor/ 20. Fijałkowski , A., “Bunsen Burner Flame Types”, Creative Commons ShareAlike Unported 3.0, accessible at http://en.wikipedia.org/wiki/File:Bunsen_burner_flame_types.jpg 21. Spectroscopy of the Sun using a high resolution Echell Spectroscope, Nigel Sharp, National Optical Astronomical Observatories/National Solar Observatory at Kitt Peak/Association of Universities for Research in Astronomy, and the National Science Foundation. Copyright Association of Universities for Research in Astronomy Inc. (AURA), all rights reserved, accessible at: http://sunearthday.nasa.gov/2006/multimedia/gal_030.php 22. Newman, M. “Maps of the 2012 US presidential election results” 2012, accessible at http://www-personal.umich.edu/~mejn/election/2012/

By Mahima Pushkarna | pushkarna.m@husky.neu.edu | NEU Fall 2014

ARTG 5110: relational structures

THE MIDDLE EAST DAV I D M c C A N D L E S S , E T A L by Mahima Pushkarna

Fig 1 : The Middle East, Key and Notable Players (screenshots)

The Middle East “The Middle East”1 is an interactive infographic, designed by David McCandless2, a UK-based journalist turned information designer and UniversLab3 in 2014. It satisfies the basic requirements that are needed to classify it as a relational network: conceptually and structurally. The Middle East is a global-level sociogram, examining inter-national and inter-communal relationships between various communities and countries in the Middle East. The infographic follows a traditional node-link structure that examines four kinds of relationships between 5 types of communities, which results in a multipartite or multimodal network. The Middle East, by the use of an interactive, multipartite relational network, gives the users the opportunity to understand the relationships between countries, communities, religious groups and affiliations in Middle-East Asia. It broadly categorizes the relationships amongst these “players” as Love, Hate, Good & Strained, and the players themselves as countries, groups, Shias, Sunnis and Non-Muslim. Made from data4 sourced from New York Times5, BBC6, Guardian7, Christian Science Monitor8, CIA World FactBook9, Wikipedia10 and the Middle East Friendship Chart11. David McCandless was a journalist before becoming a self-taught information designer. Information is Beautiful12 usually displays infographics of the latest data sets, which invariably have current affairs at the center of their subject matter. The intended users of this infographic would presumably be other journalists, information design aficionados, designers and of course, anyone interested in understanding a topic such as the Middle East, that trends heavily.

A Deeper Examination The infographic allows us to peruse the data to quite an extent. It allows us to examine the four relationships individually, or juxtaposed against one another. We can choose between examining 1, 2, 3 or even all the four kinds of relationships, leaving us with 13 distinct possibilities of juxtaposed relationships. Further, the infographic allows us to individually pick a player, and shows us all of the selected player’s relationships, regardless of our filtering of relationship type, with the player at the center of the relational network and other player’s relationships hidden. If we need to quickly examine, say, a certain country’s relationships without completely hiding the relationships between other countries and communities, all the user needs to do is hover over the name of the country. This will reduce the opacity of the relationships between other players, highlighting only the country’s relationships over which the mouse is hovering.

Fig 1: Clippings from “The Middle East”

The Middle East, at first glance, looks similar in shape to a map of the United States. However, on deeper engagement, the user realizes that the shape is in face, arbitrary. Interaction and animation generates diverse dimensions and layers in the network, and provides a platform where many traditional problems of relational network representation in print are omitted. The force-diagram network could somewhat signify the tension between countries, and with all four relationship types visible, the visualization is messy and noisy, with several

Fig 2 : The Middle Eastern Friendship Chart

Fig 3 : Byrthferth De Ramseyâ&#x20AC;&#x2122;s depiction of the universe

Fig 4 : The Solstices and Equinoxes

Fig 5 : Half Page Illustration of Planetary Courses in the Zodiac Signs

overlaps that cannot be distinguished from one another. Of course, this has been solved by the use of filters, and allowing users to change the centrality or point of view of the network, by clicking on a player. The colors and treatment of lines are preemptively symbolic: solid pink signifies love, dashed pink signifies good ties, and solid and dashed grey represent hate and strained relationships respectively. This is also carried over into groups: Green represents the Sunni Muslim’s and Orange is associated with the Shias. Grey is used for non-Muslims, other groups and countries with low Muslim numbers.

Encoding and Information Loss This simple encoding system does not strain the working memory of users much, and a key at the bottom ensures that a ready reference is available at hand. Decoding is easy despite the high in-degree and out-degree of node elements. There is no active clustering, apart from the color-coding. One will often find non-middle east powers such as USA, UK, India, China and Russia, but their presence indicates the importance of their ties to the Middle East and vice-versa. This brings us to the hard data conveyed by the infographic. As it often happens and has happened in the past, minimalism results in massive loss of information and possibly presenting biased and skewed information. Even Slate.com’s “The Middle East Friendship Chart”11 experiences losses in information – but not as much as the visualization in question. Slate.com uses a matrix structure to examine it’s data, and uses blocks of text to summarize the present status of the relationship. This visualization has been acknowledged by David McCandless as one of the sources for his data set4.

Relational Structures in History Relational structures have been used extensively in representations of abstracts concepts in religion and mankind’s idea of the universe through time. Byrthferth de Ramsey serendipitously employed the use of a node-link relational network diagram in depicting the universe in 108013. In England in the 12th Century CE, St. Bede the Venerable, St. Isidore of Seville, and St. Abbo of Fleury used relational diagrams and matrices to document the solstices and eqinoxes14, and planetary courses in the zodiac signs15. While such diagrams cannot be used to look for patterns or establish direct connections, they do show inter-element relationships. They can be interpreted as simplified graphs and therefore, can be classified as networks (Newman16).

Fig 6 : Apollonian Space Packing

Much in the way that Euler17 decoded mathematical problems using relational structures, Apollonius of Perga proposed a model of space-filling by packing spheres, which was interpreted in the form of a relational diagram in Apollonian Networks18. Relational networks were used widely in religious and semi-religious diagrams, as seen in cosmological depictions of Mount Meru in Jainism19, ancient India, and the Tree of Life in Judaism20. Relational networks, particularly node-link diagrams can convey multi-layered information. The United States Army Service Forces mapped Iron and Steel trade in 192721, and used a combination of visual elements such as squares, colors and lines of different weights to convey different kinds of information. By

Fig 7 : Mount Meru in Jain Cosmology

Fig 8 : The Tree of Life in Judaism

Fig 9 : Iron and Steel Trade Map

Fig 10 : The Post Ofďż˝ice Radio-Telephone Services Map

placing it on a map and using multiple centralities with varying indegrees and outdegrees, they created a relational network on a geographic base for their purposes. In 1935, Leslie McDonald created a colorful world map of international Radio Telephone Services22. Spanning across five land masses and originating from London, the map shows “long waves and short wave receiver and transfer. On lower margin: five vignettes showing Rugby Radio Station, R.R.S. Main power house, Faraday Building, London Post Office, R. R. S. Aerial tuning inductance, and R.R.S. Demountable valve. Includes mail ships, GPO logo, notes and quotes from Tennyson, Shakespeare and Ovid.” This is an example of a dimension rich relational map that has its roots in geography, comparable to “The Middle East”. With the advent of mass production technologies and manufacturing lines, relational networks have also bee used extensively in fields like Project Management, because of their ability to serve as computational tools. Action on Node and Action on Arrow diagrams are favoured tools in calculating project durations, dependencies and even optimising project structures.

Vintage Comparisons “The Middle East” is structurally incoherent, but this incoherence does not interfere with structure itself. There are no directions or indegrees indicated, unless one clicks on a specific country, in which case directionality is implied by the outdegree. A parallel can be drawn in the “Expanded Arts Diagram”23 in which movement is suggested by two elements: a top-to-bottom flow, and the orientation of text blocks.

Fig 17: Project Management AON Diagrams

Human patterns in social relationships has persistently been a topic of interest through history. “The Middle East” and “Friendship Choices amongst Fourth Graders”24 are similar and different in many way. A very similar network diagram with analogous subject matter, though in a different context and scale, is “Friendship Choices amongst Fourth Graders” by Jacob Moreno. He used sociograms extensively to study the role of connections amongst individuals. In “Friendship Choices amongst Fourth Graders”, we observe three distinct relational networks: two with nodes represented by circles, which indicate a similarly in elements represents, and the third with triangles, intimating a different category of node elements. The triangle node-link network is related to the larger circular node-link network. The smaller circular node-link network is completely disconnected from the other two networks. The absence of a relationship can provide much needed insights to understand the data we are dealing with, as is prominent from this example. On the other hand, in “The Middle East”, colors are the only distinguishing element amongst the nodes. Three colors are used to categorize five types of elements, which is not a very efficient attempt at encoding. Different elements which are not related are bunched together in a key, which can be misleading. However, the encoding of links is relatively clear, and is the primary objective of this infographic.

Fig 12: Friendship Choices amongst Fourth Graders

Fig 11 : The Expanded Arts Diagram

Interactivity through the Ages Interactive media often took on a more tactile form in the past, when access to solid user interfaces was limited and at time, negligible. Undeterred by unavailable or missing technologies, we have found ways to create and represent layers within information through tangible data visualization and the manipulation of visual elements. If one was to ask, how would have “The Middle East” been represented in the past, arrows could possibly turn to the Target Sociogram25 by McKenzie. The “Target Sociogram” visualizes relationships much in the way The Middle East does. The depth of field allows for a layering of information, and there is no “central node”, despite being placed on target board. Upon examining the outdegrees of one node element, centrality is created. Rubber bands used as links between nodes are a tangible representation of the force-directed links used in “The Middle East”. In more recent times, “WV 2009 - 094 Symphonie Studie Var. XI/3”26 is an example of layering information by manipulating line structure and value in a 2-dimensional, non-interactive space. Made by Jorinde Voight, a counterpart of Mark Lombardi in 2009, Symphonie Studie is an ink and pencil study of music on paper. Voight uses textures created by fine overlapping, organic lines contrasted against heavy weights of short strokes in ink to give a sense of the symphony under scrutiny. The artwork is itself abstract, as abstract as the representation on sound waves on paper can be, but is preemptively efficient.

Fig13 : Target Sociogram by McKenzie

Another solutions that formal and informal information visualizers turned to in the past were volvelles. According to Wikipedia, “a volvelle or wheel chart is a type of slide chart, a paper construction with rotating parts. It is considered an early example of a paper analog computer.” Some of them, like the modern day color-wheels available at Amazon.com27 are widely popular amongst designers even today. The “Discus Chronologicus”28 is an earlier example demonstrating the use of this paper-computer to observe the relationships between kingdoms and the events that occurred in them over time. A moveable arm allows us to match kingdoms to events in different centuries, represented by circular wedges. The book, Cartographies of Time29 notes that one Princeton University copy, “a reader has inscribed events from contemporary history in the blank spaces of the eighteenth century wedge, at one point carrying over into the contiguous space of the first century CE.” Other common interactive relational networks that took the form of volvelles were used to compute train schedules, travelling time and distance, and even weather30. Of course, the use of these methods for such functions now seems obsolete in the light of our smart phones that come pre loaded with applications that give us real-time updates of time, duration and temperature. Interactive information visualization are highly efficient modes of conveying complex information, and can be both explanatory and exploratory. But the limitless options that are enabled often times edge us to make choices which result in massive losses of highly contextual and vital information. The subject of gate-keeping information and therefore offering a skewed or biased visualization to users is highly debated. “The Middle East”, in much of its graphic minimalism, is evidence to this debaten

With inputs from Dietmar Offenhuber and Esat Karaman

Fig 14 : WV 2009 - 094 Symphonie Studie Var. XI/3,z

Fig 15 : Discus chronologicus

Fig 16 : WOWO Weather Wheel

Bibliography 1. McCandless, D., UniversLab, “The Middle East”, 2014 accessible at: http://www.informationisbeautiful.net/visualizations/the-‐middle-‐east-‐key-‐ players-‐notable-‐relationships/ 2. McCandless, David accessible at: www.informationisbeautiful.net 3. UniversLab accessible at: https://www.universlabs.co.uk/ 4. The Middle East Dataset accessible at: https://docs.google.com/spreadsheet/ccc?key=0Aqe2P9sYhZ2ncFliSmVvb2 dwRk44bUotOFZLM2NPUkE&usp=sharing 5. New York Times accessible at: http://www.nytimes.com/ 6. BBC accessible at: www.bbc.com 7. The Guardian accessible at http://www.theguardian.com/uk 8. Christian Science Monitor accessible at: http://www.csmonitor.com/ 9. CIA World FactBook accessible at: https://www.cia.gov/library/publications/the-‐world-‐factbook/geos/le.html 10. Wikipedia several pages accessible at: http://en.wikipedia.org/ 11. The Middle East Friendship Chart, Keating, J., Kirk, C., 2014, accessible at: http://www.slate.com/blogs/the_world_/2014/07/17/the_middle_east_frie ndship_chart.html 12. Information is Beautiful accessible at: www.informationisbeautiful.net

13. Byrthferth de Ramsey, Meirelles,I., “Design for Information”, 2013, ISBN-‐13: 978-‐1592538065, Rockport Publishers, Massachusetts,p 48 14. Solstices and Equinoxes, “Compendium of computistical texts”, Bede, the Venerable, Saint, 673-‐735; Isidore, of Seville, Saint, d. 636; Abbo, of Fleury, Saint, ca. 945-‐1004, Late 12th Century CE, accessible at: http://www.thedigitalwalters.org/Data/WaltersManuscripts/html/W73/de scription.html 15. Planetary Courses in the Zodiac Signs, “Compendium of computistical texts”, Bede, the Venerable, Saint, 673-‐735; Isidore, of Seville, Saint, d. 636; Abbo, of Fleury, Saint, ca. 945-‐1004, Late 12th Century CE, accessible at: http://www.thedigitalwalters.org/Data/WaltersManuscripts/html/W73/de scription.html 16. Newman, “Design for Information”, 2013, ISBN-‐13: 978-‐1592538065, Rockport Publishers, Massachusetts,p 49 17. Euler, “Design for Information”, 2013, ISBN-‐13: 978-‐1592538065, Rockport Publishers, Massachusetts,p 49 18. Apollonian Packing, Jose S. Andrade Jr.; Hans J. Herrmann; Roberto F. S. Andrade; Luciano R. da Silva, “Apollonian Networks”, 2004, accessible at: https://archive.org/details/arxiv-‐cond-‐mat0406295 19. Mount Meru/ Jain Cosmology 19th Century accessible at: http://en.wikipedia.org/wiki/File:Adhaidvipa.jpg and http://en.wikipedia.org/wiki/Jain_cosmology 20. Tree of Life: Based on Fig. 10, page 155,”The Bahir: An ancient Kabbalistic text attributed to Rabbi Nehuniah ben HaKana, first century, C. E.”, Kaplan. A., 1979, S Weiser, ISBN-‐10: 0877283435, accessible at: http://www.amazon.com/The-‐Bahir-‐Kabbalistic-‐attributed-‐ Nehuniah/dp/0877283435 and en.wikipedia.org/wiki/bahir 21. Iron and Steel Trade, 1937, United States Army Service Forces, from the David Rumsey Map Collection, accessible at: http://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~244339

~5513822:Iron-‐and-‐Steel-‐Trade,-‐1937,-‐20-‐?sort=Pub_List_No_InitialSort# 22. Post office radio -‐ telephone services. MacDonald Gill, 1935. P.R.D. 98, from the David Rumsey Map Collection, accessible at: http://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~268345 ~90042624:Post-‐office-‐radio-‐-‐-‐telephone-‐ servi?sort=Pub_List_No_InitialSort# 23. “Expanded Arts Diagram,” from Film Culture—Expanded Arts, no. 43, 1966 [V.B.16], accessible at: http://www.moma.org/interactives/exhibitions/2013/charting_fluxus/ 24. Moreno,J., “Friendship Choices amongst Fourth Graders”, Who will Survive?, Meirelles,I., “Design for Information”, 2013, ISBN-‐13: 978-‐1592538065, Rockport Publishers, Massachusetts,p 48 25. Target Sociogram, 1954, Northway, accessible at: http://www.cmu.edu/joss/content/articles/volume1/Freeman.html 26. WV 2009 -‐ 094 Symphonie Studie Var. XI/3,, Jorinde Voigt | 2009, Ink, pencil on paper,Accessible at: http://jorindevoigt.com/blog/?p=1181 27. Color wheels on Amazon.com, accessible at: http://www.amazon.com/Color-‐Wheel-‐9-‐1-‐4-‐-‐ /dp/B000I1TFMK/ref=sr_1_3?ie=UTF8&qid=1413038381&sr=8-‐ 3&keywords=colorwheel 28. “Discus Chronologicus”, Christoph Weigl, Early 1720s, The Cartographies of Time, Rosenberg, D., Grafton, A., Princeton Architectural Press, 2012, ISBN-‐ 10: 1616890584, p105 29. The Cartographies of Time, Rosenberg, D., Grafton, A., Princeton Architectural Press, 2012, ISBN-‐10: 1616890584 30. “WOWO Weather Wheel ”, Jessica Hefland | Reinventing the Wheel, Princeton Architectural Press; 1 edition (June 1, 2002), ISBN-‐13: 978-‐1568983387

List of Figures 1. McCandless, D., UniversLab, “The Middle East”, 2014 accessible at: http://www.informationisbeautiful.net/visualizations/the-‐middle-‐east-‐key-‐ players-‐notable-‐relationships/ 2. “The Middle East Friendship Chart”, updated 2013, Kirk,C., Keating, J. Accessible at http://www.slate.com/blogs/the_world_/2014/07/17/the_middle_east_frie ndship_chart.html 3. Byrthferth de Ramsey, Meirelles,I., “Design for Information”, 2013, ISBN-‐13: 978-‐1592538065, Rockport Publishers, Massachusetts,p 48 4. Solstices and Equinoxes, “Compendium of computistical texts”, Bede, the Venerable, Saint, 673-‐735; Isidore, of Seville, Saint, d. 636; Abbo, of Fleury, Saint, ca. 945-‐1004, Late 12th Century CE, accessible at: http://www.thedigitalwalters.org/Data/WaltersManuscripts/html/W73/de scription.html 5. Planetary Courses in the Zodiac Signs, “Compendium of computistical texts”, Bede, the Venerable, Saint, 673-‐735; Isidore, of Seville, Saint, d. 636; Abbo, of Fleury, Saint, ca. 945-‐1004, Late 12th Century CE, accessible at: http://www.thedigitalwalters.org/Data/WaltersManuscripts/html/W73/de scription.html 6. Apollonian Packing, Jose S. Andrade Jr.; Hans J. Herrmann; Roberto F. S. Andrade; Luciano R. da Silva, “Apollonian Networks”, 2004, accessible at: https://archive.org/details/arxiv-‐cond-‐mat0406295 7. Mount Meru/ Jain Cosmology 19th Century accessible at: http://en.wikipedia.org/wiki/File:Adhaidvipa.jpg and http://en.wikipedia.org/wiki/Jain_cosmology 8. Tree of Life: Based on Fig. 10, page 155,”The Bahir: An ancient Kabbalistic text attributed to Rabbi Nehuniah ben HaKana, first century, C. E.”, Kaplan. A., 1979, S Weiser, ISBN-‐10: 0877283435, accessible at: http://www.amazon.com/The-‐Bahir-‐Kabbalistic-‐attributed-‐ Nehuniah/dp/0877283435 and en.wikipedia.org/wiki/bahir

9. Iron and Steel Trade, 1937, United States Army Service Forces, from the David Rumsey Map Collection, accessible at: http://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~244339 ~5513822:Iron-‐and-‐Steel-‐Trade,-‐1937,-‐20-‐?sort=Pub_List_No_InitialSort# 10. Post office radio -‐ telephone services. MacDonald Gill, 1935. P.R.D. 98, from the David Rumsey Map Collection, accessible at: http://www.davidrumsey.com/luna/servlet/detail/RUMSEY~8~1~268345 ~90042624:Post-‐office-‐radio-‐-‐-‐telephone-‐ servi?sort=Pub_List_No_InitialSort# 11. “Expanded Arts Diagram,” from Film Culture—Expanded Arts, no. 43, 1966 [V.B.16], accessible at: http://www.moma.org/interactives/exhibitions/2013/charting_fluxus/ 12. Moreno,J., “Friendship Choices amongst Fourth Graders”, Who will Survive?, Meirelles,I., “Design for Information”, 2013, ISBN-‐13: 978-‐1592538065, Rockport Publishers, Massachusetts,p 48 13. Target Sociogram, 1954, Northway, accessible at: http://www.cmu.edu/joss/content/articles/volume1/Freeman.html 14. WV 2009 -‐ 094 Symphonie Studie Var. XI/3,, Jorinde Voigt | 2009, Ink, pencil on paper,Accessible at: http://jorindevoigt.com/blog/?p=1181 15. “Discus Chronologicus”, Christoph Weigl, Early 1720s, The Cartographies of Time, Rosenberg, D., Grafton, A., Princeton Architectural Press, 2012, ISBN-‐ 10: 1616890584, p105 16. “WOWO Weather Wheel ”, Jessica Hefland | Reinventing the Wheel, Princeton Architectural Press; 1 edition (June 1, 2002), ISBN-‐13: 978-‐ 1568983387 17. Simple activity-‐on-‐node logic diagram, by Wikiuser NuggetKiwi, accessible at en.wikipedia.org/wiki/File:SimpleAONwDrag3.png | Creative Commons A_ribution Share-‐Alike 3.0 Unported

By Mahima Pushkarna | pushkarna.m@husky.neu.edu | NEU Fall 2014

ARTG 5110: final paper

A CIRCLE OF REPRESENTATIONS

ABSTRACT â&#x20AC;&#x153;A Circle of Representationsâ&#x20AC;? attempts to bring various kinds of radial graphs, charts, diagrams and plots together on a single ield, by analyzing their occurrences in history and noting details that add to or remove from their ef iciency. The primary goal of this paper is to document the ef iciency enabled by the use of circles in information design and visualization through the study of select vintage and historical examples. The secondary goal is to de ine various basic information design and visualization techniques stemming from circular graphic devices and examine them from a critical standpoint. The tertiary pursuit is to tabulate a list of factors to take into consideration while deciding on the best circular layout to represent data of a speci ic type.

1) INTRODUCTION Circles are universal shapes that have been known to mankind since time immemorial. Their numerous occurences in nature are not bound by scale or matter, and have given direction to our understanding of the world. The movement of planetary spheres and celestial bodies in the dome of the night sky in luenced our perception of our living spaces, which in turn in luenced our models of the philosophical, religious, physical and social universe. Circles have aided documentation, pattern deduction and event prediction. Tangible adaptations of circles, like the wheel, have escorted us from the bronze age into the scienti ic era.

2) SCOPE AND BACKGROUND Throughout history, we ind that radial layouts have been used to visualize data ranging from dates and numbers to places and paths. Clocks and calendars used angles and rings in circular layouts as the primary organizing principle to keep track of time and predict synchronous and repetitive events. Concentric circles allowed cartographers to integrate timelines of varying ranges, and locate equidistant spatial points from a central reference points. Spheres were dissected, fashioned and re-fashioned into a range of mapping projecting to ind the best possible depiction of earth in two dimensions. When lattened into circles, they were also used to set forth three-dimensional models of the solar system. The direct proportionality between the radius and the circumference makes the circle a prime choice for displaying hierarchical datasets with a high volume of leaf nodes. A complete range of 360Âş can be exploited to create multiple axes and plot multivariate data, as seen in radar charts and spiral diagrams. However, certain visualizations that try to use the areal capacities of circles or leverage the scope of arc degrees in radial graphs have not seen much success, making circles as controversial a choice, as they are prime. When choosing a radial layout, the nature of the dataset and the scope of the visualization primarily dictates the selection. Each visualization technique offers speci ic features that can enhance or take away from the dataset in question, as is

elaborated further. As a result of the study, the paper describes the primary features of different radial layouts that determine their ef iciency at a pre-emptive stage and a conscious, exploratory stage of cognition. 3) METHODS For the purpose of this paper, radial or circular layouts have been generally de ined as “any information visualization layout in which circles (or several derivative elements and shapes such as central angles, radii, arcs, or spirals) are used as the basis of plotting data and serve the purpose of an axis”. The layouts themselves facilitate insight deduction through areal or angular comparisons and distribution mapping. The layouts include, but are not limited to, radial line graphs, pie charts, donut charts, radar charts and rose diagrams, coxcomb charts, radial tree maps, hyperbolic tree diagrams, venn diagrams, polar diagrams, and packed bubbles. This paper presents a examples of the use of circular layouts in history in the following categories of information visualization structures (Meirelles 2013). Temporal Structures (time dependent) Temporal structures depict events and actions that are correlated with bounded time spans, making them ‘container objects’ to depict the abstract concept of time. They include systems used to measure time and stories of sequential, linked events and moments in a temporal series Spatial Structures (location speci ic) A diagram or collection of data showing the spatial distribution of something or the relative positions of its components Spatio-temporal Structures (time & location speci ic) Spatio-temporal structures trepresent phenomenon and processes inherent to the dimensions of space and time, and aid the understanding of patterns in natural and social phenomena as well as help making predictions. Data belonging to both space and time are found in diverse domains and include mobility, dispersion, proliferation and differsion, to mention a few Hierarchical Structures (leaf/node, parent/children) Hierarchical systems are ordered sets where elements and/or subsets are organized in a given relationship to one another, both amongst themselves and within the whole Relational Structures (networks) Relational Structures organize data for which relationships are key to the system being visualized, containing collections of nodes and links within a particular structure or topology The data represented by these diagrams can themselves be categorized into seven different classes: Statistical Data: Highly quantitative data that containes the frequency of a certain event or set of related events, such as censuses, sales reports, exports, etc

Temporal Data: Data that is relevant to a ix period of time, or that documents the prevalence of an event at a given point in time or is contained within a span of time Spatial Data: A set of locations or co-ordinates that exist in the tangible world (earth, solar system, etc.) that can be measured from a point of reference in a standard denomination of location (co-ordinates) or distance Spatio-temporal Data: datasets that are bound to time and space, such data emerging from a given event within a ixed window of time at a particular location Multivariate Data: Datasets that record the values of a single or a group of identical events across several distinct yet related variables based on the nature of the dataset. This kind of data enables several computational tasks. Hierarchical Data: A hierarchical dataset is one in which the data is organized into a tree-like structure. The data is stored as records which are connected to one another through links, and each record is a collection of ields, with each ield containing only one value (Wikipedia Contributors 2014a) Relational Data: Data encapsulating relationships and networks between several different elements in a larger system

4) ANALYSIS & FINDINGS Pie charts are one of the most basic forms of expressing data as a statistical graph that allows us to depict proportions of a dataset (proportions to a whole). A circle is split into wedges that depict quantitative data, usually in the form of percentages or fractions that add up to one. The arc length, and therefore, the central angle determine the size of each wedge. This, effectively, creates a graphic state where areal comparison plays a pre-emptive role. Areal comparisons at a preemptive level are highly effective to give the viewer an overview of the data, though that may not hold true at a deeper investigation of the graph. Since the area of a circle is exponentially proportional to its radius, and its circumference is directly proportional to the radius, this can cause the largest wedge to be perceived as a lot larger that what is true. Additionally, the orientation of the “pie slices” of a piechart can cause distortions in perception. Thus, while pie charts are good to compare proportions to a whole, they pose several perceptual problems when used to compare proportions of wedges (Few 2007). This problem extends to situations where pie charts are compared to other forms of information graphics, as is seen in William Playfair’s attempts to visualize census data in 1801 in “The Statistical Breviary” (Image 1). Playfair is credited with the irst use of pie charts in this graphic: Here, he tried to compare the area of different countries by representing them using the area contained within a circle. He juxtaposed them against two line graphs for on either side of the circle of each country, with the line on the left showing population and that on the right showing taxes collected. He further tried to connect these two lines using a dotted line, whose ascent would determine a positive or negative relation between the population and taxes (Tufte 2001). This graphic, however, poses two hindrances: the irst being that comparing areas of

[Image 1]

[Image 2] [Image 3]

[Image 4]

[Image 5]

[Image 3]

[Image 6]

circles is not intuitive to our perceptional abilities, and the second that the diameter of the circle has a direct impact on the slope of the dotted line, leaving the relationship presented by the slope “skewed”. From this, one can infer that pie charts are best suited to providing proportional comparisons for data that amounts to a whole; particularly when the elements of the dataset are limited in number. We see Minard’s more successful use of pie charts in the form of small multiples rooted in spatial data in 1858, in his graphic “Carte igurative et approximative des quantités de viandes de boucherie envoyées sur pied par les départements et consommateurs à Paris”. [Minard's map using pie charts to represent the cattle sent from all around France for consumption in Paris] (Image 2) (Wikipedia Contributors 2014b). Due to it’s strong geo-spatial grounding, and limited variables for comparison, this visualization is easier to comprehend, though it poses the same issues as presented in the areal comparison of differently-sized circles. This is further evidenced in history by Playfair’s “Statistical Chart of Capital Cities of Europe based on Population”(1909) (Image 3). Donut charts are derivatives of pie charts, with “a hole” in the middle. This eliminates the ability to present an angular comparison, restricting the ef iciency of a donut chart tot a purely pre-emptive level of cognition. It continues to function on the basis of proportional comparisons, since the hole further prevents sophisticated areal comparisons, as seen in “Brazil: Graphicos Economies Estatisticas” (Image 4). Coxcomb charts are another information visualization technique that utilize radial distance as well as angles to display information. Florence Nightingale created these “polar area diagrams”, which are equivalent to column graphs, to create a compelling case for the causes of deaths of soliders in the east across months of a year (Image 5). Andre-Michel Guerry, the French statistician, is also known for the use of polar charts to show cyclical phenomenon in the form of small multiples (Image 6), but also provided bar charts to enable comparison (“Milestones: Section 5. 1800-1849” 2014). Guerry combined the “hole” from donut charts with polar diagrams, to add a secondary layer of information, thus enhancing the tasks enabled by the overall infographic as a whole. While Nightingale found great success in the use of these diagrams, the wedges of a coxcomb chart use varying radii the way a column graph would use the y-axis (Skau 2014). This results in wedges of larger radii being associated with larger areas, leaving the diagram open to misinterpretations.

[Image 7]

Originally created in 1877 by Georg von Mayr, radar plots (then known as star plots) were proposed as a way to display multivariate observations for an arbitrary number of variables for a single event (Friendly 1991). Radar charts follow a similar principle to organize information, though one can use several axes that pass through the center to match different, often unrelated, ields of comparison. The area enclosed by the inal shape is used for comparisons. Radar charts present a high case of arti iciality: the presence of unrelated axes next to one another in a radial layout give the impression that the data is cyclical, which may not be the case. Radar diagrams may also result in deceptive connections between quantitatively and qualitatively incomparable axes. The 1979 Automobile Analysis (Image 7) compares 16 cars across 10 parameters. These parameters cannot be related

amongst themselves, but when plotted distinctly 16 times over, it allows immediate comparisons of different cars. Keeping in mind Guerry’s success with small multiples as well, it can be said areal comparisons are best seen in small multiples. Venn diagrams and Euler diagrams are another way of using area to compare data. Venn diagrams (Image 8), conceived in 1880 by John Venn, are derivatives of eulerian diagrams, based in logic and set theory. They use overlapping circles to convey commonalities and differences between different sets (a set being a group of datum) using basic operations (Venn 1880). Overlapping areas and colors, when used in certain ways, depicts intersections, unions, symmetric differences, and relative and absolute components of the two sets. Venn diagrams were extended into functions involving a higher number of sets, by visually skewing circles representing the fourth set using a solid arc, and any set henceforth as a solid stroke for this arc, to ensure that all sets overlap and are represented consistently. Concentric circles reinforce different levels of power, with the datapoint at the center being at the top of the tree, and those at the peripheral circle being at the bottom of the tree. Concentric circles activate visual movement, and echo an outward “rippled effect like” movement, making them ideal for the depiction of top-down structures. This makes radial treemaps a highly ef icient way of organizing hierarchical data. The direct proportionality of the circumference of a circle to its radius is employed to the advantage of these treemaps, seen in as early as seventh century when Bishop Isodore of Seville used a rota to depict a consanguinity tree (Image 9). As the size of the circumference increases, so does the space to contain a growing number of children nodes of a treemap or polar diagram. This is clearly shown in Chart A (Image 10) of “The Genealogical Tree of the Nam Family” (Estabrook and Davenport 1912), where increasing levels of complexity and data is treated by using a polar diagram. Modern, interactive versions of radial hierarchical treemaps use a donut-chart like aesthetic to show statistical data that has been parsed into a hierarchical organization system. These work purely on the basis of angular comparisons, and though the central angles are themselves in visible, multiple colors and areas fortify comparisons made on the basis of angular divisions, the base concept of polar charts. Since the beginning of his kind, man has sought patterns and references. The concept of night and day, months, seasons and years are all cyclical patterns in nature that have a direct impact on our lives. The continuous nature of the circle has made this shape an ideal choice for depicting periodic and repetitive data, including time. The Aztecs carved out an entire calendar (Image 11) in stone on the basis of a concentric, radial grid. Though the data encrypted in the symbols carved on the stone are not known per se, it con irms the existence of the idea of representing time as a cyclical event, rather than a linear one. Luke Howard uses a radial bar graph to plot seasons over a year, around a circular x-axis in “The Climate of London” (Image 12). This is one of few examples in which radial graphs have successfully communicated data without skewing it beyond consideration. This is because Howard uses the circle itself as an axis, very innovatively. Against the background of a repetitive temporal unit (months and days of a year), he is able to show variation in seasons very effectively. Representing statistical data on a radial bar graph often clashes with our instincts in decoding the visual system: the graph itself requires that we make angular comparisons, while our instincts make us want

[Image 8]

[Image 9]

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[Image 12]

to compare the “bars” in terms of arc length. With the proportionality factor in play once again, we are faced with a situation where data is skewed in perception.

[Image 13]

[Above: Image 14, Below: Image 15]

However, circular graphs are very well suited for different kinds of spatial and spatio-temporal data. Concentric circles provide us with an inherent point of reference, the center, and thus, plotting equidistant points on the circumference becomes a visualization technique that is user-friendly. This is seen in our usage of latitudes and longitudes to depict geo-spatial locations on earth, in which we “slice” the earth in equal segments around the axis of rotation (resulting in longitudes) and parallel to tropics and equator (resulting in latitudes). This gives us a co-ordinate system to map out any physical location on earth, and represent the earth’s sphere in two dimensions. The “North Pole, Star Projection” (Image 13) by Guillaume Le Testu in 1555 attempts to show points in space that fall on the same latitude by means of a star projection. Another starkly different visualization that plots equidistant locations of parking ticket issued to diplomats of the United Nations from a point of reference (the United Nations Headquarters) is “Flocking Diplomats 6” (Image 14). It uses short, tangential lines emitting from concentric circles placed at ixed radii steps, to virtually create a map of New York centered in Manhattan through plotting unique geocoded locations. As such, the very de inition of a circle as “a closed plane curve consisting of all points at a given distance from a point within it called the center” (“Circle | De ine Circle at Dictionary.com” 2014), proves that it is suitable for such uses. The idea of circles and ellipses combining to seamlessly represent spheres has ensured the shape’s abundant usage in the representation of the universe. Spatio temporal paths of celestial bodies have been etched into our understanding of orbits and space, from the times of the “Ptolemic Solar System” model (Image 15). Petrus Apianus created the astronomical computer (Image 16) in 1540, using revolving paper devices to determine planetary positions. Similar to volvelles, this is one of the irst few examples that saw the birth of concept of interactivity and computation of repetitive spatio-temporal events.

Derivative shapes of circles, such as arcs and spirals, have also played an important role in information visualization techniques. Circles and ellipses give us one great advantage that squares and rectangles fail to offer: the ability to break down a circle in terms of 360º degrees or 2π radians, and spiral graphs let us leverage that repeatedly. Jacob Bloch’s “Chronological Skeleton Chart” (Image 17) from 1632 presents us with a template that allows us to integrate several timescales of varying ranges. A spiral graph also depicts growth, as seen in “10 years of Wikipedia” (Image 18), by Grefor Aisch, 2011. This particular example plots a constant around a varying radius (the y-axis), going over 12 segments or axes, each depicting a month, and number of revolutions around the center implying the year. Tufte credits this system to “the shrink principle”(Tufte 2001, 169), and its repeated application resulting in small multiples. These small multiples are combined over a common variable (in this case, temporal), resulting in the spiral graph. It is highly compact and ef icient in depicting detailed data, even though the comparison of growth to another such chart is much harder to make, owing to the organic shape of the spiral. Spiral graphs are often preferred when comparing quarter or month-speci ic data shifts, by judging the centrality of the spiral.

[Image 16]

[Image 18]

[Image 17]

[Image 19]

[Image 21]

[Image 20]

Relational networks use centrality to mark grouping and show density and distribution within a network in many different ways. Mark Lombardi used radially emitting lines to show links, and differently sized circles to show nodes in his network diagram of “World Finance Corporation and Associates.”(Image 19). Mary Northway, instead, used the area encompassed between concentric circles to represent distribution of student networks (with key players in the network being at the center), categorized across four “umbrella” quadrants in an XY co-ordinate system in a target sociogram of a nursery school in “A Primer of Sociometry” (Image 20) in 1953 (Lima 2013). She uses a combination of concentric circles and a bar graph to successfully show relational networks. Both these examples show that relational networks are best associated in a circular form, since it gives a pre-emptive sense of grouping, while enabling tangential connections that emit radially. The hyperbolic tree is another (recent) information visualization technique that is best suited to show relationships of different kinds within a closed group. The group elements are represented as arcs on a circle, the length of the arc being relevant to either the importance of the element within the group, or the weight of the relationships of that group. The relationships are shown using multi-colored arcs within the circle, lowing from one group element to another. The “looping” nature of the circle is advantageous to the orientation of these relationships, i.e. they can consume the entire 360º offered by the circle, and each of these links can be either reciprocal or non-reciprocal. Arc diagrams have been used extensively in history. They present links between different points on a single, linear scale, using arcs that rise above or below that scale. One of the earlier examples of an arc diagram to show relational data is the “Apocalyptic Chart” (Image 21), made in 1632 by Josef Mede, depicting the opening of the seven seals of the apocalypse based on the Key of Revelation. This diagram had not only the ability to in luence political and social visions of the English revolution (Rosenberg and Grafton 2012, 158), but also became a yardstick for textual analyses. CONCLUSION As seen from the study of select examples in history, circles can be a highly ef icient form of comparison, but when we put the appropriate dataset to the best-suited form of representation. As many say, there is no correct or incorrect way of doing things. There are active supporters of radial layouts, constantly innovating new ways of using circles in information visualization, and there are those that ind circles “controversial”. Each different radial form of information visualization has its pros and cons, which must be considered carefully before usage. After all, the best design gets out of the way between the viewer’s brain and the content (Edward Tufte on “Presenting Data and Information”).

BIBLIOGRAPHY “Circle | De ine Circle at Dictionary.com.” 2014. Accessed December 11. http://dictionary.reference.com/browse/circle?s=t.

Estabrook, Arthur H. (Arthur Howard), and Charles Benedict Davenport. 1912. The Nam Family; a Study in Cacogenics. Cold Spring Harbor, N.Y. [The New era printing company]. http://archive.org/details/namfamilystudyi00esta. Few, Stephen. 2007. “Save the Pies for Dessert.” Visual Business Intelligence Newsletter, 1–14. Friendly, Michael. 1991. “Statistical Graphics for Multivariate Data.” In Statistical Graphics for Multivariate Data, 1745. Cary, NC: SAS Institute Inc. http://www.math.yorku.ca/SCS/sugi/sugi16-paper.html#H1_5:Star. Lima, Manuel. 2013. Visual Complexity: Mapping Patterns of Information. Reprint edition. New York: Princeton Architectural Press. Meirelles, Isabel. 2013. Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Rockport Publishers. “Milestones: Section 5. 1800-1849.” 2014. Accessed December 11. http://euclid.psych.yorku.ca/SCS/Gallery/milestone/sec5.html. Rosenberg, Daniel, and Anthony Grafton. 2012. Cartographies of Time: A History of the Timeline. New York; London: Princeton Architectural Press. Skau, Drew. 2014. “Battle of the Charts: Why Cartesian Wins Against Radial.” Visually Blog. Accessed December 6. http://blog.visual.ly/cartesian-vs-radial-charts/. Tufte, Edward R. 2001. The Visual Display of Quantitative Information. 2nd edition. Cheshire, Conn: Graphics Pr. Venn, John. 1880. “On the Diagrammatic and Mechanical Representation of Propositions and Reasonings, 1.” The London, Edinburgh and Dublin Philosophical Magazine and Journal of Science, 5, X: 508. Wikipedia Contributors. 2014a. “Hierarchical Database Model.” Wikipedia, the Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Hierarchical_database_model&oldid=6 22034139. ———. 2014b. “Charles Joseph Minard.” Wikipedia, the Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Charles_Joseph_Minard&oldid=632517 701

ARTG 5110 | Information Design History | Fall â&#x20AC;&#x2DC;14 | IDV | NEU