Stop making cartograms! At least until permission is granted from the chap who holds the patent on them.

Karl Karsten’s “population projection” was published in his book Charts and Graphs (1923) and patented in 1925. As with the 1911 “Apportioinment Map” noted in an earlier post, the term “cartogram” was not used by Karsten to describe this creation.  He called it the “Population Projection.”

Curiously, it’s claimed that Karsten also invented the hedge fund.

But back to maps.

Karsten’s patent, (#1,556, 609, October 13, 1925) claimed rights to

…a map of a plurality of territories, having their boundary lines so distorted as to make their included areas represent graphically the relative importance of a given factor other than land area of one area with respect to another area, the boundaries being distorted without losing their familiar and significant features…

Karsten suggests using his “population projection” as a base upon which to map other data, such as truancy rates (below).  Thus it’s a bivariate cartogram (reproduced from p. 667 in Charts and Graphs):

The idea is good, but in practice it’s a bit wonky.  Several western US states are reduced to toothpick dimensions, and note the New York goiter (New York City). Also, Karsten seems to have some degree of difficulty maintaining the horizontal with the map and the legend. Could he have had an inner-ear infection?

But back to maps.

The illustration in Karsten’s patent reveals his methodology:

Details of the methodology can be found in the text of the patent.

Karsten, in Charts and Graphs, explains the justification for using the “population projection” which is, more or less, the same line of argument used in current discussions of cartograms:

We do not sell our goods to the mountains, bill them to the rivers, or credit the forests with payment. Probably from at least a subconscious appreciation of this circumstance, many national distributors, advertisers, and sales-managers have discarded maps on which the rivers, forests or mountains are shown when they are studying the geographic distribution of their sales. The up-to-date sales manager lots his distributing points and records his sales in a great many ways upon maps which carry only faint State outlines or a the most show the location of larger cities. But why stop here? Your sales manager does not sell to square miles, acres, or other units of land-area measurement. He sells to human beings. Why should he use maps which show, not human beings, but square miles, that is, maps in which the areas indicate not the population but the land surface? Why indeed!

The result of this projection of the map of the United Statues upon a population basis rather than a land-area basis will be most surprising even to the most hardened travelers.

Needless to say, the picture of sales conditions which such a map exhibits, will be far more valuable and useful than the picture upon the usual land-area basis. In short, the corrected areas of the States serve to give an excellent background or evaluation of the importance of the statistics plotted upon the map.

The number of ways in which the map can be altered and projected for special purposes upon special bases is unlimited, but all are alike in one respect – that their areas no longer show physical land areas in square miles but show the actual values more important for the special purposes in view.

In 2005 a series of cartogram patents (here here here) failed to cite Karsten’s patent.

]]> http://makingmaps.net/2009/06/22/making-psychogeography-maps/ Mon, 22 Jun 2009 15:27:50 +0000 John Krygier http://makingmaps.net/2009/06/22/making-psychogeography-maps/

Guide Psychogéographique de OWU (2009, med res jpg)

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During the week of June 15-19 (2009) five intrepid Ohio students and myself engaged in improvisational psychogeography, culminating in the map opening this post. A printable 11″ x 17″ (300dpi 1.4mb) PDF of the map is here.

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Map detail: The path taken through campus followed the outline of a wolfie hand-shadow cast on a campus map.

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Map detail: Stuff smelt, heard, and felt with its allure or disallure indicated with faces.

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The map was the product of a course – Mapping Weird Stuff – I offered at the OWjL (Ohio Wesleyan University Junior League of Columbus) summer camp for gifted and talented middle school students.

Based on the kid’s ideas and work collecting diverse data, I designed a layout and look for the map. The map itself was created in FreehandMX, now dead-tech thanks to Adobe (I still prefer Freehand even though I started with Illustrator back at version 1).

Making the map once again reminded me that it’s fun to make maps, if you have interesting stuff to map. The design and layout are certainly nothing one could generate with typical mapping software – thus the use of graphic illustration software. Diverse and interesting maps are not really the domain of web and pc-based map generation software. Maybe sometimes. Not usually.

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Map detail: An abstracted linear “map” sequencing smells, textures, and sounds from one end to the other of the path investigated.

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My vague intent was to do some kind of weird mapping project on campus – sensory mapping, psychogeography, etc. My search for resources for this age student (grades 6-8) resulted in a few finds, but not much. The materials I compiled on the course blog (here) served as the basis of our work, which developed as the students engaged the ideas. We met for 1.5 hours a day, for 5 days.

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Special glasses indicate how serious we were about this project.
The Hulk hand inspired confidence in our powers.

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The students, Django, Mallory, McKenna, Erica, and Ben, were great. They jumped into the project, came up with ideas that shaped our direction, and collected all of the data on the map. I had some ideas about what kind of psychogeography we would do, and what kind of map we would create, then it all transmogrified into something else which turned out great.

We did a dérive (“a technique of transient passage through varied ambiances”) to get a feel for the campus and its “resonances,” some blind-folded, ear-plugged tours through the campus (with me or one of the students leading the others along) collecting smells and sounds, as well as a few texture collection expeditions (inspired, in part, by Denis Wood’s Narrative Atlas of Boylan Heights project).

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Guiding much of our work was a single, inspiring Hulk hand.

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A bit of background on Psychogeography:

Psychogeography, according to its founder Guy Debord, is “the study of the precise laws and specific effects of the geographical environment, consciously organized or not, on the emotions and behavior of individuals.”

In practice, psychogeography inherently resists any narrow definitions. It encompasses diverse activities that raise awareness of the natural and cultural environment, is attentive to senses and emotions as they relate to place and environment, is often political and critical of the status quo, and must be both very serious and fun.

Psychogeography overlaps with Kevin Lynch’s work on mental maps, as nicely reviewed in Denis Wood’s article “Lynch Debord” as well as work on non-visual sensory-scapes (smellscape, soundscape, touchscape, tastescape, etc.).

The most famous psychogeography map is Debord’s Guide Pychogéographique de Paris:

Guy Debord, Guide Pychogéographique de Paris

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“In the morning they come out with queer-looking eyes…”

The above map represents one ward of New York City – the Eleventh.

The saloons as put upon this map were ascertained by the reporter of the Christian Union by actual count.

The saloons are largely beer saloons: for the base of the population is German, and a large intermingling of German sounds, German signs, German wares, and German smells generally, prevail.

Pretty much all the available space, after enough room has been taken out for houses and grown people and huckster’s stands, is filled by stout, chubby, healthy-looking children – with here and there a punier waif – of all ages and sizes, mostly young and small, and of all degrees of cleanliness, from comparatively clean to superlatively dirty.

The Ward is reported by the police to be as orderly as any in the city.

The German is peculiar.  Unlike his Irish and Yankee cousins, he does not make a great noise and hurrah over his cups, and wind up with a street brawl.  He gathers unto himself a few kindred spirits, and together they wend their way to the Trink-Halle, where, in a little back room, with closed doors and drawn curtains, they guzzle beer together till none of them can see.  In the morning they come out with queer-looking eyes, but there has been no disturbance in the place.

Said a clergyman to your reporter, “I came into the ward expecting to find nothing but filth and vice.  But I could take you into hundreds of homes where you would find ease and comfort and even culture.

Balance Sheet:

• 19 Churches and Sunday-Schools, 5 Industrial Schools, 1 Hospital
• 346 Saloons
• One saloon to every 200 population.

Christian Union, February 19, 1885.  PDF of entire article and map is here.

Denis Wood & John Fels’ new book The Natures of Maps is available now from the University of Chicago Press and many other sources. The lowest price I can find at this time is $29 (at Buy.com). Denis is, of course, co-author of the Making Maps book.

The book is big – almost a foot square – with color maps on almost every page.  The book had a harrowing path to publication.  Originally under contract to ESRI Press, the book was in final galleys (ready to print but for a handful of edits) when ESRI Press decided to cancel it and a dozen other books in process.  Given the expense of producing the book (and the cost of reproduction rights to the illustrations) this seemed to be a peculiar business decision.  The University of Chicago Press subsequently acquired the book, more or less ready to print.

Here’s an “editorial” blurb I wrote for the book:

If Wood & Fels’ The Power of Maps showed that maps were powerful, The Natures of Maps reveals the source of that power. The Natures of Maps is about a simple but profound idea: maps are propositions, maps are arguments. The book confronts nature on maps – nature as threatened, nature as threatening, nature as grandeur, cornucopia, possessable, as a system, mystery, and park – with intense slow readings of exemplary historical and contemporary maps, which populate this full color, beautifully illustrated and designed book.

The careful interrogation of maps reveals that far from passively reflecting nature, they instead make sustained, carefully crafted, and precise arguments about nature. The Natures of Maps shows how maps establish nature, and how we establish maps. The power of maps extends not only from their ability to express the complexities of the natural world in an efficient and engaging manner, but in their ability to mask that they are an argument, a proposal about what they show.

The implications of the arguments in The Natures of Maps are significant, empowering map users and makers. The Natures of Maps shows that neither map users or map creators are passive, merely accepting or purveying reality; they are, instead, actively engaged in a vital process of shaping our understanding of nature in all its complexity. Map users have a critical responsibility, the power to accept, reject, or counter-argue with the maps they encounter. Map creators have creative responsibility, the power to build and finesse their arguments, marshalling data and design for broader goals of understanding and communicating truths about the world. Rethinking how maps work in terms of propositional logic, with its 2000-year history and vast methodological and theoretical foundation, promises to be one of the most profound advances in cartographic theory in decades, and The Natures of Maps shows the way in a captivating manner.

Considering maps from the perspective of propositional logic provides a rigorous foundation for a theory of the map that transcends disciplinary boundaries. Scholars from the humanities, social sciences, and natural sciences will find Wood and Fels’ The Natures of Maps intellectually sound, methodologically useful, and deeply engaging. But the beauty of The Natures of Maps is that it is not merely an academic book. Wood and Fels’ The Natures of Maps is a powerful, beautifully illustrated and engaged argument about maps as arguments that will appeal to map lovers, map makers, map users, and map scholars.

Making maps to counter prevailing assumptions and beliefs is a well established tradition.  Counter mapping, radical mapping, protest mapping … the map proposes an alternative.  Bolstered by its authoritative aura, the map can be quite convincing.

Geographers John Agnew, Thomas Gillespie, and Jorge Gonzalez, with Political Scientist Brian Min (all of UCLA) propose an alternative to the mantra – repeated by just about all on the political Right and Left – that the Iraq “Surge” has succeeded.

Agnew and his colleagues argue that the celebrated decline in violence in Baghdad is actually the result of inter-ethnic cleansing which began prior to the “Surge.”  And this counter-proposal about the “Surge” is bolstered by a garrison of maps.

Counter-mapping the “Surge” depends on a relatively mundane set of meteorological satellite data, ironically generated by the Defense Meteorological Satellite Program – Operation Linescan System (KMSP-OLS). Nighttime light is one kind of data collected by this program.

Nighttime light certainly suggests population patterns – we have all seen the global maps of nighttime light – and also access to electricity.

Agnew and his colleagues asked a relatively simple question that can be answered with a series of maps based on the KMSP-OLS data: how has emitted nighttime light in Baghdad changed as U.S. Military strategy in Iraq changed?

The study area consists of the ten security districts in Baghdad, here indicated on a Landsat ETM satellite image.

Nighttime light imagery was selected and analyzed for dates after the U.S. invasion of Iraq (November 16, 2003, 9pm), before the “Surge” (March 20, 2006, 9pm), and after the “Surge” (March 21 and December 16, 2007, both 9pm).

The results seem to contradict proclamations of the success of the “Surge.” In general, Baghdad’s nighttime light increased between the initial U.S. invasion and mid 2006, then begins a rapid decline prior to the implementation of the “Surge” strategy.

Even more interesting, the mid-2006 decrease in nighttime light is not evenly distributed in Baghdad.  The areas of declining nighttime light correspond with areas of ethnic violence and cleansing as documented in the Jones Report and its maps.

The greatest decline is in East and West Rashid – historically mixed Sunni and Shia – but also Adhamiya (Sunni), Kadamiya (Shia), Rusafa, and Karada (mixed and Sunni).  No change was observed in Sadr City (Shia), New Baghdad (Shia), Karkh (Green Zone), and Al Mansour (historically mixed but heavily Sunni by late 2007). This is certainly easier to see on a map:

Agnew and his colleagues conclude:

Our findings suggest that … the surge has had no observable effect, except insofar as it has helped to provide a seal of approval for a process of ethno-sectarian neighborhood homogenization that is now largely achieved but with a tremendous decline in the extent of residential intermixing between groups and a probable significant loss of population in some areas.

Furthermore, the nighttime light signature of Baghdad data when matched with ground data provided by the report to the US Congress by Marine Corps General Jones and various other sources, makes it clear that the diminished level of violence in Iraq since the onset of the surge owes much to a vicious process of inter-ethnic cleansing.

Disagree?  Raise your own army of data and maps to counter this counter-”Surge” proposition.

The text of Agnew, Gillespie, Gonzalez, and Min’s article “Baghdad Nights: Evaluating the U.S. Military ‘Surge’ Using Nighttime Light Signatures” is, for review and educational purposes, here.

Erwin Raisz is among the most creative cartographers of the 20th century, known in particular for his maps of landforms.

In 1931 Raisz outlined and illustrated the methods behind his landform maps, in an article in the Geographical Review (Vol. 21, No. 2, April 1931). Excerpts from the text and graphics in the article are included below.

Raisz’s approach is to create complex pictorial map symbols for specific landform types. Each specific application, of course, would have to modify the symbols to fit the configuration of particular landforms.

One of the limitations of Raisz’s work is that it is so personal and idiosyncratic that it virtually defies automation or application in the realm of computer mapping. Thus digital cartography has, in some cases, limited the kind of maps we can produce.

Raisz writes:

There is one problem in cartography which has not yet been solved: the depiction of the scenery of large areas on small-scale maps.

Most of our school maps show contour lines with or without color tints. Excellent as this method is on detailed topographic sheets … it fails when it has to be generalized for a small-scale map of a large area. Nor does the other common method, hachuring, serve better.

For the study of settlement, land utilization, or any other aspect of man’s occupation of the earth it is more important to have information about the ruggedness, trend, and character of mountains, ridges, plains, plateaus, canyons, and so on-in a word, the physiography of the region.

Our purpose here is to describe and define more closely a method already, in use, what we may call the physiographic method of showing scenery. This method is an outgrowth of the block diagram. [T]he method was fully developed by William Morris Davis. Professor Davis has used block diagrams more to illustrate physiographic principles than to represent actual scenery.

Professor A. K. Lobeck’s Physiographic Diagram of the United States and the one of Europe do away entirely with the block form, and the physiographic symbols are systematically applied to the vertical map. His book Block Diagrams is the most extended treatise on the subject.

It is probable that the mathematically-minded cartographer will abhor this method. It goes back to the primitive conceptions of the early maps, showing mountains obliquely on a map where everything should be seen vertically. We cannot measure off elevation or the angle of slope. Nevertheless, this method is based on as firm a scientific principle as a contour or hachure map: the underlying science is not mathematics but physiography.

If we regard the physiographic map as a systematic application of a set of symbols instead of a bird’s-eye view of a region, we do not violate cartographic principles even though the symbols are derived from oblique views instead of vertical views. It may be observed that our present swamp symbols are derived from a side view of water plants.

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Landform map symbols include: plains (sand & gravel, semiarid, grassland, savannah, forest, needle forest, forest swamp, swamp, tidal marsh, cultivated land), coastal plain, flood plain, alluvial fans, conoplain, cuesta land, plateau (subdued, young, dissected), folded mountains, dome mountains, block mountains, complex mountains (high, glaciated, medium, low, rejuvenated), peneplane, lava plateau (young, dissected), volcanoes, limestone region (with sinkholes, dissected, karst, tropical, mogotes), coral reefs, sand dunes, desert of gravel (serir), deflated stone surfaces (hamada), clay (takyr), loess region, glacial moraine, kames, drumlin region, fjords, glaciers, shoreline (sand, gravel, cliffed), and elevated shorelines & terraces.

Quite a few years ago I wrote an overview article on the use of sound for representing geographic data, including a series of sound variables for mapping I developed. The article was titled “Sound and Geographic Visualization” and was published as a chapter in the now out-of-print book Visualization in Modern Cartography (MacEachren & Taylor eds., 1994).

Sound is used to convey information all the time, but less so in the realm of mapping where the visual dominates. The article explores the possibilities of making maps with sound, or using sound in tandem with a visual display to add additional layers of information.

Some work on tactile mapping had had occurred at the time the article was published, as well as a few dozen articles on sound for representing data in general (not geographic data). Subsequently, research on multi-sensory mapping has expanded but not as much as I expected. We still can’t hear data with Google Earth.

For an updated bibliography of related work, see the articles and books that cite “Sound and Geographic Visualization” at Google Scholar.

The article is below as originally published. It holds up ok, although technology has changed quite a bit.

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Sound and Geographic Visualization

“Who the hell wants to hear actors talk?”

Harry Warner on being confronted with the prospect of the sound movie.

Introduction

The issue of sound in the context of visualization may at first seem incongruous. There is, however, evidence to support the claim that sound is a viable means of representing and communicating information and can serve as a valuable addition to visual displays. Abstracted two-dimensional space and the visual variables – the traditional purview of cartography – may not always be adequate for meeting the visualization needs of geographers and other researchers interested in complex dynamic and multivariate phenomena. The current generation of computer hardware and software gives cartographers access to a broadened range of design options: three-dimensionality, time (animation), interactivity, and sound. Sound – used alone or in tandem with two-or three-dimensional abstract space, the visual variables, time, and interactivity – provides a means of expanding the representational repertoire of cartography and visualization.

This chapter discusses the use of realistic and abstract sound for geographic visualization applications. Examples of how and why sound may be useful are developed and discussed. Uses of sound in geographic visualization include sound as vocal narration, as a mimetic symbol, as a redundant variable, as a means of detecting anomalies, as a means of reducing visual distraction, as a cue to reordered data, as an alternative to visual patterns, as an alarm or monitor, as a means of adding non-visual data dimensions to interactive visual displays, and for representing locations in a sound space. The chapter concludes with research issues concerning sound and its use in geographic visualization.

Experiencing and Using Sound to Represent Data

Our sense of vision often seems much more dominant than our sense of hearing. Yet one only has to think about the everyday environment of sound surrounding us to realize that the sonic aspects of space have been undervalued in comparison to the visual (Ackerman 1990, Tuan 1993). Consider the experience of the visually impaired to appreciate the importance of sound and how it aids in understanding our environment. Also consider that human communication is primarily carried out via speech and that we commonly use audio cues in our day to day lives – from the honk of a car horn to the beep of a computer to the snarl of a angry dog as we approach it in the dark (Baecker and Buxton 1987).

There are several perspectives which can contribute to understanding the use of sound for representing data. Acoustic and psychological perspectives provide insights into the physiological and perceptual possibilities of hearing (Truax 1984, Handel 1989). An environmental or geographical perspective on sound can be used to examine our day to day experience with sound and to explore how such experiential sound can be applied to geographic visualization (Ohlson 1976, Schafer 1977, Schafer 1985, Porteous and Mastin 1985, Gaver 1988, Pocock 1989). Understanding how sound and music is used in non-western cultures may inform our understanding of communication with sound (Herzog 1945, Cowan 1948). Knowledge about music composition and perception provides a valuable perspective on the design and implementation of complicated, multivariate sound displays (Deutsch 1982). Many of these different perspectives have coalesced in the cross-disciplinary study of sound as a means of data representation, referred to as sonification, acoustic visualization, auditory display, and auditory data representation (Frysinger 1990). Within this context both realistic and abstract uses of sound are considered.

Using Realistic Sounds

Vocal narration is an obvious and important use of realistic sound. (note 2) Details about the physiological, perceptual, and cognitive aspects of speech are well known (Truax 1984, Handel 1989) and film studies offer insights into the nature and application of vocal narration (Stam, Burgoyne, and Flitterman-Lewis 1992).

Another use of realistic sounds is as mimetic sound icons, or “earcons” (Gaver 1986, Gaver 1988, Gaver 1989, Blattner et al. 1989, Mountfort and Gaver 1990). Earcons are sounds which resemble experiential sound. Gaver, for example, has developed an interface addition for the Macintosh computer which uses earcons. An example of an earcon is a “thunk” sound when a document is successfully dragged into the trash can in the computer interface.

Using Abstract Sounds

Abstract sounds can be used as cues to alert or direct the attention of users or can be mapped to actual data. Early experiments by Pollack and Ficks (1954) were successful in revealing the ability of sound to represent multivariate data. Yeung (1980) investigated sound as a means of representing the multivariate data common in chemistry after finding few graphic methods suitable for displaying his data. He designed an experiment in which seven chemical variables were matched with seven variables of sound: two with pitch, one each with loudness, damping, direction, duration, and rest (silence between sounds). His test subjects (professional chemists) were able to understand the different patterns of the sound representations and correctly classify the chemicals with a 90% accuracy rate before training and a 98% accuracy rate after training. Yeung’s study is important in that it reveals how motivated expert users can easily adapt to complex sonic displays.

Bly ran three discriminant analysis experiments using sound and graphics to represent multivariate, time-varying, and logarithmic data (Bly 1982a). In the first experiment she presented subjects with two sets of multivariate data represented with different variables of sound (pitch, volume, duration, attack, waveshape, and two harmonics) and asked subjects to classify a third, unknown set of data as being similar to either the first or second original data set. The test subjects were able to successfully classify the sound sets. In a second part of the experiment she tested three groups in a similar manner but compared the relative accuracy of classification among sound presentation only (64.5%), graphic presentation only (62%), and a combination of sound and graphic presentation (69%). She concluded that sound is a viable means of representing multivariate, time-varying, and logarithmic data – especially in tandem with graphic displays.

Mezrich, Frysinger, and Slivjanovski confronted the problem of representing multi-variable, time-series data by looking to sound and dynamic graphics (Mezrich et al. 1984). They had little success finding the graphic means to deal with eight-variable time series data. An experiment was performed where subjects were presented with separated static graphs, static graphs stacked atop each other (small multiples), overlaid static graphs, and redundant dynamic visual and sound (pitch) graphs. The combination of dynamic visual and sound representation was found to be the most successful of the four methods.

An ongoing project at the University of Massachusetts at Lowell seeks to expand the use of sound for representing multivariate and multidimensional data. The “Exvis” project uses a one-, two-, and three-dimensional sound space to represent data (Smith and Williams 1989, Smith et al. 1990, Williams et al. 1990, Smith et al. 1991). The project is based upon the idea of an icon: “an auditory and graphical unit that represents one record of a database” (Williams et al. 1990, 44). The visual attributes of the icon are “stick-figures” which can vary in “length, width, angle, and color” (Williams et al. 1990, 45). The sonic attributes of the icons are “pitch, attack rate, decay rate, volume, and depth of frequency modulation” (Williams et al. 1990, 45). An experimental Exvis workstation has been set up to run various human factors experiments, and initial tests of subjects have been completed. The results reveal that using visual and sonic textures together improves performance.

Two-dimensional sound displays which locate sounds up/down, right/left via stereo technology, and three-dimensional sound displays which add front/back to two dimensional displays are also being developed. A three-dimensional virtual sound environment has been developed at the NASA-Ames Research Center (Wenzel et al. 1988a, Wenzel et al. 1988b, Wenzel et al. 1990). The ability to locate sound in a multidimensional “sound space” will undoubtedly be important for representing spatial relationships.

Almost all of the above studies and applications which use abstract sound to represent data rely upon a set of basic and distinct elements of sound – pitch, loudness, timbre, etc. These abstract elements can be called “sound variables” (figure 1). Most of these abstract sound variables naturally represent nominal and ordinal levels of measurement. (note 3) As such, a “variables” approach, analogous to that developed by Bertin (1983) for visual variables, can serve as a useful heuristic for incorporating sound in geographic visualization displays. This approach can be contrasted with one based on music theory and composition (Weber 1993a, Weber and Yuan 1993). Visual map symbolization and design have been approached from many different perspectives – psychophysics, cognitive psychology, Arnheim’s art theory, and Bertin’s semiotics to name a few – and all have added to our knowledge of cartographic design. The same multiplicity of approaches will undoubtedly underpin our approaches to the use of sound.

Using Abstract Sounds in Geographic Visualization: The Sound Variables

The following discussion reviews a basic set of abstract sound variables – not a complete taxonomy – which are viable for geographic visualization applications. This set of abstract sound variables can be used in tandem with voice narration and mimetic earcons as discussed above. The use of the term “variable” is used loosely and does not imply that the elements of sound are wholly separable from each other. Abstracted elements of sound, like those of vision, interact and effect each other (Lunney and Morrison 1990, Kramer and Ellison 1992). However, abstract sound variables, as with the visual variables, serve to clarify initial design choices and can serve as a viable starting point for incorporating sound into visual displays.

Data display applications using realistic and abstract sounds require a temporal dimension. This is in part due to the need to compare different sounds in order to glean information from the sounds. For example, the use of relative pitch – comparison with other pitches – is a key factor in using pitch to represent data (Kramer 1992). A tone of a certain pitch heard alone means less than when that same tone is heard in comparison to an array of varying pitches. In addition, a temporal dimension is required for certain variables of sound which must vary in some way over time for their character to be identified. Duration, for example, only exists when there is some beginning and end of a sound over time.

The Abstract Sound Variables

Location: the location of a sound in a two or three dimensional sound space. Location is analogous to location in the two dimensional plane of the map. As a sound variable location requires stereo or three-dimensional sound displays. Two- and three-dimensional sound allows for the mapping of left/right, up/down, (and in 3-D) forward/backward locations. Location can represent nominal and ordinal data. For example, a two-dimensional stereo sound map could use location to direct attention to a specific area of the graphic map display where the fastest change is occurring in a spatial data set over time.

Loudness: the magnitude of a sound. Loudness is measured in terms of the decibel and implies an ordinal difference. The average human can just detect a one decibel sound, can detect differences in loudness of about three decibels, and can tolerate up to approximately 100 decibels (the loudness of a jet taking off). We would like to avoid 100 decibel maps. Loudness is inherently ordered and thus seems appropriate for representing ordinal level data. Loudness may be used to imply direction and can be varied over time to represent ordinal change in data over time (eg., to alert one to important but infrequently occurring phenomena). It is known that humans usually become unconscious of constant sounds (Buxton 1990, 125). For example, although the hum of a computer’s fan in becomes inaudible soon after switching it on, even a slight variation in the fan will be instantly noticed. This effect can be used to represent information where a quiet tone represents a steady state and any variation represents change.

Pitch: the highness or lowness (frequency) of a sound. Pitch is highly distinguishable and is one of the most effective ways of differentiating order with sound. Judgements of pitch will vary somewhat from person to person. Western music has traditionally employed a scale of eight octaves comprised of twelve pitches each; extreme pitches, however, are hard to distinguish. On average, individuals can easily distinguish 48 to 60 pitches over at least four or five octaves, and this implies that pitch (divided up by octaves) can be used to represent more than a single variable in a sonic display (Yeung 1980, 1121). Mapping with pitch is appropriate for ordinal data. In addition, pitch may imply direction, where, for example, an increasing pitch represents upward movement. Tonal sharps and flats can be used to some effect also, possibly to represent a second variable such as variations in data quality. Every twelfth pitch has the same pitch color (chroma) and this may serve to represent nominal or ordinal data (Weber 1993b). Pitch, then, can represent quantitative data, primarily ordinal. Time can be added to pitch to create a sound graph which tracks ordinal change in data over time.

Register: the relative location of a pitch in a given range of pitches. Register describes the location of a pitch or set of pitches within the range of available pitches. Register is a more general case of pitch, where one can specify a high, medium and low register, each retaining a full set of chromatic pitches. (note 4) It can add to pitch as a broader ordinal distinction. An application which uses register and pitch is discussed later in this paper.

Timbre: the general prevailing quality or characteristic of a sound. Timbre describes the character of a sound and is best described by the sound of different instruments: the brassy sound of the trumpet, the warm sound of the cello, the bright sound of the flute, etc. Timbre, then, implies nominal differences (Risset and Wessel 1982, Kramer and Ellison 1992). For example, a brassy sound could be used to represent an urban phenomena while a warm or mellow sound could be used to represent a rural phenomena. Such an example draws attention to the evocative nature of sound.

Duration: the length of time a sound is (or isn’t) heard. Duration refers to the length of a single sound (or silence) and can represent some quantity mapped to that duration. Silence must be used in tandem with duration if one is to distinguish the duration of multiple sounds (Yeung 1980, 1122). Duration is naturally ordinal.

Rate of Change: the relation between the durations of sound and silence over time. Rate of change is primarily a function of the varying (or unvarying) durations of sounds/silences in a series of ordered sounds over time and can represent consistent or inconsistent change in the phenomena being represented.

Order: the sequence of sounds over time. The order in which sounds are presented over time can be “natural” – such as the progression from a low pitch to a high pitch – and this means that it should be easy to detect general trends (patterns) in data presented with sound variables such as pitch or loudness. The “natural order” of sounds can be manipulated to represent data “disorder” or different orders. For example, if a natural order of sounds (say pitch from low to high) is matched to chronological temporal order, any non-ordered sound will be recognizable as an indication that data are out of chronological order. An example will be discussed later in this paper.

Attack/Decay: the time it takes a sound to reach its maximum/minimum. The attack of a sound is the time it takes for a sound to reach a specific level of loudness; the decay is the time it takes to reach quiet. Attack has been found to be much more successful in conveying information than decay (Lunney and Morrison 1990, 144). Attack/decay could be used to represent the spread of a specific data variable in a given unit: for example, pitch may represent an average value for the income in a county and attack/decay the spread of values; a long attack and decay would represent, then, a wide range of incomes in that county. Attack/decay may also be used to represent rates of diffusion or recession.

Thus far this chapter has described the use of realistic sound (vocal narration and mimetic earcons) and the use of abstract sound (summarized as a basic set of abstract sound variables) for representing data. The next section describes a series of geographically-oriented applications of sound in geographic visualization.

Sound and Geographic Visualization: Applications

Animation and Sound

Sound is an inherently temporal phenomena. As a result, it is particularly suited to use with map animation. Recent work on cartographic animation has led to the derivation of a set of dynamic variables – duration, rate of change, and order – and some suggestions for their application (DiBiase et al. 1992). Sound can be closely linked to the dynamic variables and their applications and may be used to enhance the comprehension of information presented in a dynamic display. In addition, potential uses of the dynamic variables may be suggested by examining temporal issues in sound and music.

At least three distinct kinds of change can be visualized by a map animation. Spatial change, often called a “fly-by,” is visualized by changing the observer’s viewpoint of some static object. Computerized flight simulators provide an excellent example of visualized spatial change. Voice-over has been used with fly-by applications to provide an explanation of what is being seen (Jet Propulsion Laboratories 1987, DiBiase et al. 1991). Vocal narration is, then, an important way for using sound to enhance dynamic geographical visualizations. Mimetic sounds – earcons – can also be used to enhance dynamic geographic visualizations. Thus sound can be used as a mimetic symbol. The sound of fire and wind, for example, has been incorporated into an animation of forest growth to cue the viewer into what is happening in the animation (Krygier 1993). In this case sound serves as a redundant variable with which to enhance certain key events in the dynamic display.

Chronological change, or “time-series,” may be visualized by mapping chronologically-ordered phenomena onto an animated series. A map of the diffusion of AIDS over time and space is an example of visualized chronological change (Gould, Kabel, Gorr, and Golub 1991, Kabel 1992). Such spatial and chronological change is intuitive and minimal explanation is needed to make such representations understandable for most users. Sound has been used to add additional information to the chronological display of AIDS (Krygier 1993). Loudness is used to represent total cases of AIDS for each of the years displayed in the animation. The increasing loudness adds both a dimension of information (increasing number of AIDS cases) as well as a sense of impending disaster. Pitch is also used in the same AIDS animation to represent the percent increase of new cases for each year. The pitch can be heard “settling down” as the percent increase drops and steadies in the late 1980s. An anomaly can be heard in 1991 where the animation switches from actual AIDS cases to model predicted AIDS cases. Thus sound can be used to detect anomalies in data.

Initially less intuitive but valuable for expert users of visual displays is a third kind of change which can be visualized with map animation. Attribute change, or “reexpression,” is visualized by mapping attribute-ordered phenomena onto an animated series. Such a visualization of change in attribute may be very useful for enhancing or revealing patterns not evident in the original time-series. Graphic methods to alert the animation viewer to the fact that the animation is ordered in terms of attribute change have been used. For example, a time scale can be included at the bottom of the animation and a pointer can indicate the year of each animation scene. The problem with this graphic solution is that the viewer’s attention can be focused on the map or on the time bar and not both at the same time. This is obviously a situation where sound may provide a better solution since it is possible to watch the map and listen to it at the same time. Thus sound can be used to replace a distracting visual element on a map display. Pitch is used to replace the time bar in an animation of presidential election landslides (Krygier 1993). The animation is shown in chronological order with pitch mapped to years (increasing pitch = increasing years). This familiarizes the user with the meaning of pitch in this animation. The same data is then reexpressed in terms of an attribute – magnitude of the landslide – and shown again. The fact that the pitches are heard out of order cues the viewer that the visual sequence is out of chronological order. Patterns noticed in the visual or the sonic display – or both – may then be more carefully examined. Sound patterns may be more easily distinguished than visual patterns, and especially valuable for dealing with cyclic temporal data (Weber 1993b). Such an application of sound is more important as the amount of data being visualized increases and it becomes necessary to isolate the few interesting patterns from the many uninteresting ones.

Interactivity and Sound

Interactive multimedia displays have began to attract the attention of cartographers (Andrews and Tilton 1993, Armenakis 1993, Buttenfield 1993, DiBiase et al. 1993, Huffmann 1993, Shiffer 1993). Voice-over, realistic sounds, and abstract sound used as cues and mapped to data can be incorporated into such displays. A prototype interactive display which uses graphics and sound has been developed to display up to four data variables simultaneously (Krygier 1993). The prototype is based on 1990 U.S. Census data from Pennsylvania. A choropleth map is used to display percent population not in the labor force. A graduated circle map displaying median income is then added to the choropleth map. At this point one could add a third data variable to the display by changing the choropleth map into a bivariate choropleth map, by adding a data variable as a fill for the graduated circles, or by going to a second map. All of these have problems: bivariate maps are somewhat difficult to interpret and understand (Olson 1981); the third variable in the fill of the graduated circle will be hard to see in the small circles; and multiple maps may lead to comparison problems. Sound can provide an alternative to these visual methods. The prototype uses a single pitch in three different octaves (register) to display a “drive to work index.” The index is either high, medium, or low and refers to the relative distance workers have to drive to their places of work. When one points and clicks Pike County with the mouse a high octave pitch is heard representing a long drive to work. Thus two variables are seen and one is heard. A fourth data variable can be added by using the range of pitches within each of the three octaves. In the case of the prototype, this was done with another high/medium/low index, that relating to the percent poor in each county. For example, when one points and clicks Pike County a high octave pitch is heard followed by a low pitch within that octave representing a long drive to work and a low rate of poverty. After a short period of using such a “quad-variate” display it becomes relatively easy to extract the four data variables. Such a supposition will, of course, have to be more carefully evaluated but experience with the prototype suggests that sound is a viable way to add more data dimensions to visual displays.

Sound and Geographic Visualization: Some Research Issues

This chapter has thus far reviewed various ways that sound can be incorporated into visualization displays. These methods include the use of realistic and abstract sounds. A basic set of abstract sound variables have been defined and illustrated with some geographic examples and applications. Many issues, obviously, remain to be investigated. Learning and Sonic Legends

If sound maps are to work then the design of effective sound legends will have to be investigated. Because sound is not a traditional mapping variable, the user of a map display which incorporates sound will have to be acclimated to the idea of sound as a data presentation method as well as what the sound variables used in the display represent. How to best design a sonic legend is unclear: should it be “all sound” and set up as an interactive tutorial before the use of the display begins, or should it be akin to a traditional map legend, available if and when needed? The idea of sequencing may be useful in helping sound map users to understand the elements of a multivariate sound display. (Slocum et al. 1990).

Perceptual Issues

A solid body of knowledge exists (primarily in acoustics, psychology, and music) detailing the sound perception capabilities of the human physiological system. This knowledge can be used to underpin our understanding of the possibilities and limitations of the sound variables as visual design elements. We must also be aware of the problem of “sonic overload,” of barraging the user with too many different variables and dimensions of sound (Blattner et al. 1989, 12, O’Connor 1991). Attendants at the Three Mile Island Nuclear Power Plant were addled by more than sixty different auditory warning systems during the Plant’s 1979 crisis (Buxton 1990, 125).

Cognitive Issues

More difficult issues of identification, problem solving, judgment, remembering, and understanding of sound displays await attention. The sequential nature of sound raises questions of knowledge acquisition and memory. There are also questions of how much information people can deal with. A combined visual and sonic display may be one way to deal with the ever increasing complexities that geographers want to approach; such complex displays, however, have few precedents and may be more confusing than enlightening, especially for non-expert users. However, one of the goals of visualization is the construction of representations which can serve the needs of motivated expert users who are dealing with complex data and thus require sophisticated display methods. Evaluations of such methods must consider the capabilities of these users. One promising way to make the sonic display of complex information feasible is to adapt sound structures we are adept at dealing with – primarily those from music – to display design (Weber and Yuan 1993). Musical structures such as rhythm, melody, and harmony are consensual, defined elements of music and must be differentiated from arbitrary and abstract sound representations such as those discussed above in Yeung’s (1980) study (two pitches, loudness, damping, direction, duration, and silence). To what degree a familiarity with common musical structures will help people to distinguish and recognize sonic patterns is, however, unclear. Indeed, it should be expected that the sound variables – based on common musical structures or arbitrarily based on duration, rate of change, and order – will interact, interfere, and affect each other (Kramer and Ellison 1992, Lunney and Morrison 1990).

Location of Sound

The ability to locate sounds in a two- or three-dimensional “sound space,” analogous to the two- or three-dimensions of the map, is an important aspect of sound which is particularly applicable to the display of spatial data. The location of sound can be used in an abstract manner, as a cue to direct attention to a specific area of a visual display, or can be used to represent the actual location of phenomena in a display. Such applications of sound have been investigated (Blauert 1983, Wenzel et al 1987, Wenzel et al. 1988a, Wenzel et al. 1988b, Wenzel et al. 1990, Begault 1990, Smith et al. 1990) but not in terms of geographically referenced data. Questions concerning hardware and software requirements (for two- or three-dimensional sound generation) and issues of the human ability to adequately locate sounds in a sound space need to be investigated.

Sound Maps for the Visually Impaired

The use of sound displays have been explored in the context of communicating scientific data to visually impaired students. Lunney and Morrison have used high/low pitches and pitch duration to map out “sound graphs” and have found that visually impaired users are able to comprehend the graphs and understand the patterns with relative ease (Lunney and Morrison 1981, Lunney 1983). Mansur, Blattner, and Joy compared tactile graphs to sound graphs (created with continuously varying pitch) and evaluated subjects based upon judgements of line slopes, curve classification, monotonicity, convergence, and symmetry. They found comparable accuracy of information communication capabilities between tactile and sound graphs yet sound graphs were found to be a quicker way of communicating information and were easier to create (Mansur 1984, Mansur et al. 1985).

One can speculate on the use of sound as a means of representation for the visually impaired. While there is a body of cartographic research on tactile maps (Andrews 1988), there is no cartographic research on sound maps for the visually impaired. The nature of the map – with its two graphic dimensions and one or more data variables – complicates the matter and makes sound maps more difficult to create than simple sound graphs. Is there any way to construct spatial representations using a one dimensional sound? If a high/low pitch is used to represent high/low location can this (or other similar sonic metaphors) be used to map with one dimension of sound? Or will we have to look to stereo (two dimensions) and three dimensional sound? If we can create a two or three dimensional sound space how will maps be represented in that space? How finely can the sound variable location be specified? Can both dimensions of the plane and a data variable be represented? How easy is it to comprehend, remember, and use a sonic spatial display? The ability to create and locate a sound in two or three dimensions remains a major problem hindering the use of sound for spatial data representation.

A hybrid of tactile materials and sound may prove more useful than either alone: the research carried out by Yeung, Bly, and Williams has shown that complex multi-variate single dimension sounds can be detected and understood. Thus a map display for the visually impaired could use tactile display for base map information and could use sound to represent single or multivariate data located at points or areas on the map. The sonic representations could be roughly located in a two (or three) dimensional sound space or they could be selected by an interactive tactile display. These approaches would allow the communication of complex, multivariate data to the visually impaired – something to which tactile maps are not particularly well suited. It would use the tactile display for base locational attributes which may be more difficult to create and interpret with sound.

Sound Maps of Data Uncertainty and Quality

Maps often impose strict points, lines, and areas where no strict structures actually exist or where the certainty of their location or magnitude is low. In addition, maps are often compiled from multiple data sources, and these data vary in quality and reliability. Maps tend to be “totalizing” creatures: variations in uncertainty and quality are smoothed over to create an orderly, homogeneous graphic. On one hand, this is why maps are so useful, and it is obvious that maps enable us to deal with our uncertain and messy world by making it look more certain and tidy. Yet it seems important that some sense of the uncertainty or quality of the represented data be available. The cartographer’s reflex is to conceive of uncertainty as a statistical surface and to represent it graphically. There is a rich history of graphical presentations of uncertainty – many historical atlases for example, show past migration of peoples in a manner which stresses that what is know about the migration is “fuzzy” and not well established. On the other hand, taken to its logical extreme, a map which visually displays uncertainty may become a blurred mess. The purpose of maps, remember, is to impose order, not to accurately represent chaos. Further, there is only so much visual “headroom” on a display: using visual variables to display uncertainty may have the effect of limiting the display of other data variables. A final problem with visual representations of uncertainty is that it is difficult to model visually the composite uncertainty of two or more map overlays – the realm of multivariate data displays.

An alternative approach to “visualizing” uncertainty takes advantage of sound (Fisher 1994). A “sound map” can be created which underlies the visual map and can be accessed if and when necessary. This sound map may be multivariate: register and pitch could be used to distinguish different layers of information. The click of a mouse at any position on the visual map would cause a specific sound mapped to that specific point, line, or area to be heard; dragging the mouse would reveal a variation of sound as the sound-mapped data varied. A variable pitch (low to high) can represent the level of uncertainty. By dragging the mouse – the sonic equivalent to Monmonier’s “brushing” (Monmonier 1989) – one begins to move toward a representation of a two dimensional space; but the effect would be like having a small “window” with which you could only see a small portion of a map at one time. Can people build up an sound “image” of the entire sound map from these small glimpses? The creation of a two dimensional sound space would allow a fuller representation of the uncertainty surface. However, if the entire uncertainty surface needs to be known, a visual representation may be more appropriate. Using sound to represent data quality or uncertainty has the advantage of preserving the sharp image of the map while allowing for the extraction of data quality or uncertainty data if and when it is needed or if it passes a predetermined threshold. Sound, in this case, serves as an invisible source of information and may be one solution to the problem of representing the quality and uncertainty of data in an already crowded visual display.

Applicability and Viability

Sound has been only minimally used for data display to date in part because of the limitations and costs of producing and using sound. Such limitations are rapidly diminishing as computers incorporate sophisticated sound capabilities. The Musical Instrument Device Interface (MIDI) standard for sound and music generation in computers is well accepted across all computer platforms and MIDI compatible software is currently available which can create and manipulate all of the sound variables this paper has discussed. It is possible to incorporate sound into visual displays with commonly available hardware and off-the-shelf software. (note 5)

Finally, it seems reasonable to approach the use of sound in visual displays with a critical sense of its viability and value in terms of actual applications which have real (expert and/or motivated and interested) users. Evaluations can be made using traditional quantitative methods as well as qualitative methods such as focus groups (Monmonier and Gluck 1993). While interesting in and of itself as a possible addition to visual display, it is important to avoid using sound just for the sake of its novelty.

Conclusions

This chapter reviews the possibilities of using of sound as a design variable for geographic visualization. It describes how and why sound may be used as vocal narration, as a mimetic symbol, as a redundant variable, as a means of detecting anomalies, as a means of reducing visual distraction, as a cue to reordered data, as an alternative to visual patterns, as an alarm or monitor, as a means of adding non-visual data dimensions to interactive visual displays, and for locating sounds in a “sound space.”

In general the exploration of sound as a visualization design method for geographic visualization is important for two interrelated reasons. It is necessary to explore the ways in which we can take full advantage of human perceptual and cognitive capabilities in our visualization designs. Much of the inspiration behind surging interest in visualization lies in the desire to exploit the tremendous and often unappreciated visual capabilities of humans in order to cope with increasing amounts of data about our physical and human worlds. Our sense of hearing, which has until recently been unappreciated as a means of representing data, can be used to expand the representational repertoire of cartographic design. At the same time, it is important to realize that the ideas and phenomena geographers wish to represent may not always be best represented by static, two-dimensional visual displays. Sound offers a way to represent information for map users who lack the sense of vision. Sound, in tandem with time, offers a way to enhance the comprehension of non-chronological uses of time. Sound offers a way to expand the limited possibilities of representing multivariate data with graphics. Sound, in other words, provides us with more choices for representing ideas and phenomena and thus more ways in which to explore and understand the complex physical and human worlds we inhabit.

Notes

1. Harry Warner (of Warner Brothers fame) on being confronted with the prospect of the sound movie. Quoted in A. Walker, (1979) The Shattered Silents: How the Talkies Came to Stay, William Morrow and Co., New York.

2. This assumes the content and meaning of the language used in a narration is unproblematical, which is, of course, an oversimplification. The use of vocal narration in visualization displays opens up interesting possibilities for investigating the relations between spoken and visual languages.

3. I have collapsed interval and ratio levels of measurement into the category of ordinal pending further research on the capacity of sound to represent these finer distinctions.

4. In music, distinctions between soprano, alto, tenor, and bass are more commonly used.

5. The applications created for this paper were constructed on a Macintosh. Sounds were generated or digitized with MacroMind™ SoundEdit Pro and were combined with animations in MacroMind™ Director.

Acknowledgements: For constructive criticism and ideas thanks to Sona Andrews, Mark Detweiler, David DiBiase, Roger Downs, Gregory Kramer, Alan MacEachren, Mark Monmonier, David Tilton, and Chris Weber.

Bibliography

Ackerman, D. (1990) “Hearing,” In: Ackerman, D, A Natural History of the Senses, Random House, New York, pp. 173-226.

Ammer, C. (1987) The Harper Collins Dictionary of Music, Harper Collins, New York.

Andrews, S., and D. Tilton. (1993) “How Multimedia and Hypermedia are Changing the Look of Maps,” Proceedings: Auto-Carto 11, Minneapolis. pp. 348-366.

Armenakis, C. (1993) “Hypermedia: An Information Management Approach for Geographic Data,” Proceedings: GIS/LIS 1993 Annual Conference, Minneapolis, pp. 19-28.

Baecker, R. and W. Buxton (1987) “The Audio Channel,” In: Baecker, R. and W. Buxton, Readings in Human-Computer Interaction, Morgan Kaufman Publishers, Los Altos CA, pp. 393-99.

Begault, D. (1990) “The Composition of Auditory Space: Recent Developments in Headphone Music,” Leonardo, Vol. 23 (1), pp. 45-52.

Bertin, J. (1983) Semiology of Graphics, University of Wisconsin Press, Madison.

Blattner, M., D. Sumikawa, and R. Greenberg (1989) “Earcons and Icons: Their Structure and Common Design Principles,” Human-Computer Interaction, Vol. 4(4), pp. 11-44.

Blauert, J. (1983) Spatial Hearing: The Psychophysics of Human Sound Localization, MIT Press, Cambridge.

Bly, S. (1982a). Sound and Computer Information Presentation, Unpublished PhD, University of California – Davis.

Bly, S. (1982b) “Presenting Information in Sound,” CHI `82 Conference on Human Factors in Computer Systems, pp. 371-375.

Buttenfield, B. (1993) “Proactive Graphics and GIS: Prototype Tools for Query, Modeling, and Display,” Proceedings: Auto-Carto 11, Minneapolis. pp. 377-385.

Buxton, W. (1985) “Communicating With Sound,” Proceedings of CHI `85, pp. 115-119.

Buxton, W. (1989) “Introduction to This Special Issue on Nonspeech Audio,” Human-Computer Interaction, Vol. 4(4), pp. 1-9.

Buxton, W. (1990) “Using our Ears: An Introduction to the Use of Nonspeech Audio Cues,” In: Farrell, E., (ed.), op cit., pp. 124-127.

Cohen, M. and L. Ludwig. (1991) “Multidimensional Audio Window Management,” International Journal of Man-Machine Studies, Vol. 34(3), pp. 319-336.

Cowan, G. (1948) “Mazateco Whistled Speech,” Language, Vol. 24, pp. 280-286.

Deutsch, D. (ed.) (1982) The Psychology of Music, The Academic Press, New York.

DiBiase, D., A. MacEachren, J. Krygier, C. Reeves, and A. Brenner. (1991) “Uses of the Temporal Dimension in Cartographic Animation + Visual and Dynamic Variables,” Computer Animated Videotape, Deasy Geographics Lab, Penn State University, University Park, PA.

DiBiase, D., A. MacEachren, J. Krygier, C. Reeves. (1992) “Animation and the Role of Map Design in Scientific Visualization,” Cartography and GIS, Vol. 19 (4), pp. 201-14.

DiBiase, D., C. Reeves, A. MacEachren, J. Krygier, M. von Wyss, J. Sloan, and M. Detweiler. (1993) “A Map Interface for Exploring Multivariate Paleoclimate Data,” Proceedings: Auto-Carto 11, Minneapolis. pp. 43-52.

Evans, B. (1990) “Correlating Sonic and Graphic Materials in Scientific Visualization,” In: Farrell, E., (ed.), op cit., pp. 154-162.

Farrell, E., (ed) (1990) Extracting Meaning from Complex Data: Processing, Display, Interaction, Proceedings of The International Society for Optical Engineering, Vol. 1259, SPIE, Bellingham WA.

Farrell, E., (ed.) (1991) Extracting Meaning from Complex Data: Processing, Display, Interaction II. Proceedings of The International Society for Optical Engineering, Vol. 1459, SPIE, Bellingham WA.

Fisher, P. (1994) “Hearing the Reliability in Classified Remotely Sensed Images,” Cartography and Geographical Information Systems, vol. 21, no. 1, pp. 31-36.

Frysinger, S. (1990) “Applied Research in Auditory Data Representation,” In: Farrell, E., (ed.), op cit., pp. 130-139.

Gaver, W. (1986) “Auditory Icons: Using Sound in Computer Interfaces,” Human-Computer Interaction, Vol. 2 (2), pp. 167-77.

Gaver, W. (1988) Everyday Listening and Auditory Icons, Unpublished PhD, University of California – San Diego.

Gaver, W. (1989) “The Sonic Finder: An Interface that uses Auditory Icons,” Human-Computer Interaction Vol. 4 (4), pp. 67-94.

Gould, P., J. Kabel, W. Gorr, and A. Golub. (1991) “AIDS: Predicting the Next Map.” Interfaces Vol. 21(3), pp. 80-92.

Handel, S. (1989) Listening: An Introduction to the Perception of Auditory Events, The MIT Press, Cambridge.

Herzog, G. (1945) “Drum Signaling in a West African Tribe,” Word, Vol. 1, pp. 217-38.

Huffmann, N. (1993) “Hyperchina: Adventures in Hypermapping,” Proceedings: 16th International Cartographic Conference, Cologne, pp. 26-45.

Jet Propulsion Laboratories. (1987) “LA: The Movie,” Computer animated videotape, Jet Propulsion Laboratories, Pasadena.

Kabel, J. (1992) “AIDS in the United States,” Computer Animated Videotape, Deasy Geographics Lab, Penn State University, University Park, PA.

Kendall, G. (1991) “Visualization by Ear: Auditory Imagery for Scientific Visualization and Virtual Reality,” Computer Music Journal, Vol. 15 (4), pp. 70-73.

Kramer, G. (1992) Personal communication, April 13.

Kramer, G. and S. Ellison. (1992) “Audification: The Use of Sound to Display Multivariate Data,” Unpublished manuscript.

Krygier, J. (1993) “Sound and Cartographic Design,” Manuscript Videotape, Deasy Geographics Lab, Penn State University, University Park, PA.

Iverson, W. (1992) “The Sound of Science,” Computer Graphics World, January, pp. 54-62.

Lunney, D. (1983) “A Microcomputer-based Laboratory Aid for Visually Impaired Students,” IEEE Micro, Vol 3 (4), pp. 19-31.

Lunney, D. and R. Morrison. (1981) “High Technology Laboratory Aids for Visually Handicapped Chemistry Students,” Journal of Chemical Education, Vol. 8 (3), pp. 228-231.

Lunney, D. and R. Morrison. (1990) “Auditory Presentation of Experimental Data,” In: Farrell, E., (ed.), op cit., pp. 140-146.

McCormick, B., T. DeFanti, and M. Brown. (1987) “Visualization in Scientific Computing,” Computer Graphics, Vol. 21(6).

Mansur, D. (1984) Graphs in Sound: A Numerical Data Analysis Method for the Blind, Lawrence Livermore National Laboratory report UCRL-53548.

Mansur, D., M. Blattner, and K. Joy. (1985) “Sound Graphs: A Numerical Data Analysis Method for the Blind.” Journal of Medical Systems, Vol. 9 (3), pp. 163-174. Mezrich, J., S. Frysinger, and R. Slivjanovski. (1984) “Dynamic Representation of Multivariate Time Series Data,” Journal of the American Statistical Association, Vol. 79 (385), pp. 34-40.

Moellering, H. (1991) “The Background and Concepts of Dynamic Cartography,” Paper presented at the meeting of the Association of American Geographers, Miami FL.

Monmonier, M. (1989) “Geographic Brushing: Enhancing Exploratory Analysis of the Scatterplot Matrix,” Geographical Analysis, Vol. 21, pp. 81-84.

Monmonier, M. (1991) “Ethics and Map Design – Six Strategies for Confronting the Traditional One-Map Solution,” Cartographic Perspectives No. 10, pp. 3-8.

Monmonier, M. and M. Gluck. (1993) “Focus Groups for Design Improvement in Dynamic Cartography,” Cartography and Geographical Information Systems, vol. 21, no. 1. pp. 37-47..

Mountford, S., and W. Gaver. (1990). “Talking and Listening to Computers.” In: B. Laurel, B. The Art of Human Computer Interface Design. Addison-Wesley, Reading, pp. 319-334.

O’Connor, R. (1991) “Workers in Close Quarters May Not Be Ready for Noisy Computers,” Centre Daily Times, Monday, March 18, State College, PA, p. 9E.

Ohlson, B. (1976) “Sound Fields and Sonic Landscapes in Rural Environments,” Fennia, Vol. 148, pp. 33-45.

Olson, J. (1981). “Spectrally Encoded Two-Variable Maps,” Annals of the Association of American Geographers, Vol 71(2), pp. 259-276.

Peterson, I. (1985) “Some Labs are Alive with the Sound of Data,” Science News, Vol. 27(June 1), pp. 348-350.

Pocock, D. (1989) “Sound and the Geographer,” Geography, Vol. 74 (3), pp. 193-200.

Pollack, I. and L. Ficks. (1954) “Information of Elementary Multidimensional Auditory Displays,” Journal of the Acoustical Society of America, Vol. 6, pp. 155-158.

Porteous, J. and J. Mastin. (1985) “SoundScape.” Journal of Architectural Planning Research, Vol. 2, pp. 169-186.

Rabenhorst, D. (1990) “Complementary Visualization and Sonification of Multi-Dimensional Data,” In: Farrell, E., (ed.), op cit., pp. 147-153.

Risset, J. and D. Wessel. (1982) “Exploration of Timbre by Analysis and Synthesis,” In: Deutsch D., (ed.), op cit.

Scaletti, C. and A. Craig,. (1991) “Using Sound to Extract Meaning from Complex Data,” In: Farrell, E., (ed.), op cit., pp. 207-219.

Schafer, R. (1977) The Tuning of the World, Knopf, New York.

Schafer, R. (1985) “Acoustic Space,” In: Seamon, D. and R. Mugerauer, (eds.) Dwelling, Place, and Environment Martinus Nijhoff, Dordrecht, pp. 87-98.

Shiffer, M. (1993) “Augmenting Geographic Information with Collaborative Multimedia Technologies,” Proceedings: Auto-Carto 11, Minneapolis. pp. 367-376.

Slocum, T., and S. Egbert. (1991) “Cartographic Data Display,” In: Taylor, D. (ed) Geographic Information Systems: The Microcomputer and Modern Cartography, Pergamon Press, Oxford, pp. 167-199.

Slocum, T., W. Roberson, and S. Egbert. (1990) “Traditional versus Sequenced Choropleth Maps: An Experimental Investigation,” Cartographica, Vol. 27 (1), pp. 67-88.

Smith, S. and M. Williams. (1989) “The Use of Sound in an Exploratory Visualization Environment,” Department of Computer Science University of Lowell Technical Report No. R-89-002, Computer Science Department, University of Massachusetts at Lowell, Lowell MA.

Smith, S., R. Bergeron, and G. Grinstein. (1990) “Stereophonic and Surface Sound Generation for Exploratory Data Analysis,” Proceedings of the Association for Computing Machinery Special Interest Group on Computer Human Interfaces, pp. 125-32.

Smith, S., G. Grinstein, and R. Pickett. (1991) “Global Geometric, Sound, and Color Controls for Iconographic Displays of Scientific Data.” In: Farrell, E., (ed.), op cit., pp. 192-206.

Stam, R., R. Burgoyne and S. Flitterman-Lewis. (1992). New Vocabularies in Film Semiotics: Structuralism, Post-Structuralism and Beyond, Routledge, New York.

Szlichcinski, K. (1979) “The Art of Describing Sounds,” Applied Ergonomics, Vol. 10 (3), pp. 131-138.

Truax, B. (1984) Acoustic Communication, Ablex Publishing Co, Norwood, NJ.

Tuan, Y. (1993) “Voices, Sounds and Heavenly Music,” In: Passing Strange and Wonderful: Aesthetics, Nature, and Culture, Island Press, Washington D.C., pp. 70-95.

Tukey, J. (1977). Exploratory Data Analysis, Addison-Wesley, Reading MA.

Weber, C. (1993a). “Sonic Enhancement of Map Information: Experiments Using Harmonic Intervals.” Unpublished PhD dissertation, State University of New York at Buffalo, Department of Geography.

Weber, C. (1993b). Personal communication.

Weber, C. and M. Yuan. (1993) “A Statistical Analysis of Various Adjectives Predicting Consonance/Dissonance and Intertonal Distance in Harmonic Intervals,” Technical Papers: ACSM/ASPRS Annual Meeting, New Orleans, Vol. 1, pp. 391-400.

Wenzel, E., F. Wightman, and S. Foster. (1987) “Development of a Three-Dimensional Auditory Display System.” In: Yost, W. and G. Gourevitch, (eds.) Directional Hearing, Springer-Verlag, New York.

Wenzel, E., F. Wightman, and S. Foster. (1988a) “Development of a Three-Dimensional Auditory Display System,” SIGCHI Bulletin, Vol. 20 (2), pp. 52-57.

Wenzel, E., F. Wightman, and S. Foster. (1988b) “A Virtual Display System for Conveying Three-Dimensional Acoustic Information,” Prceedings of the Human Factors Society, Vol. 32, pp. 86-90.

Wenzel, E., S. Fisher, P. Stone, and S. Foster. (1990) “A System for Three-Dimensional Acoustic `Visualization’ in a Virtual Environment Workstation,” Visualization `90: First IEEE Conference on Visualization, IEEE Computer Society Press, Washington, pp. 329-337.

Williams, M. (1989) “The Architecture of the Exvis Kernel,” Department of Computer Science University of Lowell Technical Report No. R-89-004, Computer Science Department, University of Massachusetts at Lowell, Lowell MA.

Williams, M., S. Smith, and G. Pecelli. (1990) “Computer-Human Interface Issues in the Design of an Intelligent Workstation for Scientific Visualization,” SIGCHI Bulletin, Vol. 21(4), pp. 44-49.

Yeung, E. (1980) “Pattern Recognition by Audio Representation of Multivariate Analytical Data,” Analytical Chemistry, Vol. 52 (7), pp. 1120-1123.

A cartogram varies the size of geographic areas based on the data values associated with each area. Typical cartograms scale geographic areas to population, GNP, electoral votes, etc.

This “apportionment map,” as creator William B. Bailey (Professor of Political Economy, Yale University) calls it, scales the size of U.S. states to the size of their population (in 1910). Note that New York has colonized much of the upper Midwest.

The map, published April 6, 1911 in The Independent is one of the earliest cartograms I have seen.

Apportionment means “allotment in proper shares.” Thus, each state size is allotted based on population, not actual geographical area. Is a curious term to use, possibly more meaningful than the somewhat vague term “cartogram” (a “map diagram”).

Bailey writes:

The map shown on this page is drawn on the principle that the population is evenly distributed thruout the whole United States, and that the area of the States varies directly with their population. With the map constructed on this principle some curious changes become apparent. On the ordinary map the four States, Montana, Wyoming, Colorado, and New Mexico, together with the seven States which lie to the west of them, comprise more than one-third of the territory of the United States, and the area of each one of them is considerably larger than that of New York State; yet the population of New York State alone is nearly one-fourth larger than the combined population of these eleven Western States. In fact, the entire territory to the west of the Mississippi River contains only about 5 per cent. more people than are to be found in the New England States, together with New York, New Jersey, and Pennsylvania. Yet the territory at present covered by these nine Eastern States is only about two-thirds as large as the State of Texas. If we should add to these nine Eastern States the population of Ohio, the total would be greater by about three millions than the entire population west of the Mississippi. The State of Rhode Island, hardly visible to the naked eye on the ordinary map, now becomes almost as large as the territory of Utah and Arizona combined.

Were Texas as densely populated as is the State of Rhode Island, it would contain a population of nearly eighty-five millions, leaving only six millions of our people to be scattered thruout the rest of the country. Were the population of the United Stats as a whole as dense of that of Rhode Island this country would have more than a billion inhabitants.

Cartograms can show derived data (suicide rate in U.S. states, left) or totals (electoral votes in U.S. states, right):

Cartograms can be contiguous or non-contiguous:

Illustrations taken from Making Maps book, p. 218-19.

According to Waldo Tobler (PDF article: “Thirty Five Years of Computer Cartograms”) the term cartogram has been used since at least 1851, but it seems like the kind of cartograms shown above are rare until the first few decades of the 20th century.

Confusingly, the term cartogram was commonly used to describe various kinds of graphs and statistical maps (also called thematic maps), making it difficult to trace the genealogy of the modern cartogram.

A “cartogram” (bar graph) from Vocational Education by John Morris Gillette (1910):

A “cartogram” (graduated circle map) from Junior High School Mathematics by George Wentworth et al., (1917):

A “cartogram” from the Foreign Missions Year Book of North America for 1919:

A “cartogram” (choropleth map) from Studies in Occupations (1921):

A “cartogram” from Isaiah Bowman’s The New World: Problems in Political Geography (1921):

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Sara Fabrikant offers cartogram information, examples, and resources focused on the 2004 U.S. election. She includes references. The prolific Waldo Tobler has a half dozen of his cartogram articles available as PDFs.

Make your own cartograms with MAPresso and Frank Hardesty’s Cartogram Generator.

Cartogram Central has some interesting links, but has not been updated since 2002. Worldmapper has a passel of global cartograms.

Map of New York City, Showing the Distribution of the Principal Nationalities by Sanitary Districts published in Harper’s Weekly (June 1, 1894) using 1890 U.S. Census data.

This map looks great, revealing a substantial amount of information with its intense, juxtaposed patterns.

The textures on the map show the relative amounts of different nationalities (qualitative data) in each of the areas (sanitary districts) on the map:

The map shows if a district has more or less diversity (more or fewer lines of different textures), the relative proportions of different nationalities, the nationalities themselves, and, at a broader scale, the districts that are similar or differ in their nationality constitution. Because of the careful rotation of the lines of textures, the different sanitary districts can also be distinguished from each other.

To quote the text which accompanies the maps (and explains the methodology of the map):

The census of 1890 obtained the nationality of the residents of each sanitary district by descent from the mother. The table in which this appears was made the basis of the nationality map. As a basis it will appear fair enough when it is considered that at the time of the census over seventy-six percent of the white population in the city had foreign-born mothers, and over forty per cent. were foreign-born themselves. So the latter certainly, and probably a majority of the thirty-six percent. of native-born of foreign mothers, would show the traits of their maternal nationality. All the nationalities given in the table are not plotted. The Scotch, English, Welsh, Scandinavian, and Canadians have not collected in colonies, but are scattered over the city. These, being in small numbers, and perhaps less foreign than the others, were disregarded. They appear in the unclassified [category] in the diagram at the foot of the map. Of the nationalities represented only those making up two-thirds of the population of any district have been plotted. This rule was adopted to bring out in clearer contrast those that do exist to a greater extent. The nationalities are represented by bands conventionally marked. The breadth of a band in any district bears the same relation to the sum of the breadths of the different bands in that district as the number of the nationality it represents bears to the two-thirds of the population in that district. Sanitary district S of the Twelfth Ward and the Twenty-third Ward are not touched. These were left blank because the method of representing nationalities gives an erroneous idea in regard to the density of population. These thinly populated districts where natives preponderate slightly would appear as native settlements. These, of course, they are, but not like other parts of the city, for they are suburban.

A map of population density (shown below) accompanies the Nationalities map, using increasingly dense textures to represent quantitative population differences:

The Nationalities map illustrates (in 1894) Edward Tufte’s demand for maps (and other information graphics) that reveal the multivariate and complex. This is a map to spend time with, not because it is poorly designed, but because it contains a substantial amount of information.

The New York City Principal Nationalities Map in its entirety (1890 data; rotated to fit your screen):

The New York City Population Density Map (1890 data):

The map pair, side by side for comparison (as originally published):

The map was created by Frederick E. Pierce for the Tenement-House Committee, one of the progressive organizations working against urban slums and blight in the late 19th and early 20th centuries. The text notes that the original map was in color, but was redrawn for publication in monochrome.

•••••

Texture is usually included as one of the cartographic visual variables. The visual variables, which provide a guide for matching visual marks to data characteristics (such as qualitative vs. quantitative data), are attributed to French semiotician Jacques Bertin.

I discuss and illustrate, with contrasting good/poor maps, the basic visual variables (shape, size, color hue, color value, color intensity, and texture) in chapter 9 (excerpt here) of Making Maps.

Texture can be difficult to work with as it can imply either qualitative or quantitative differences, and has a tendency to vibrate.

Back in the day, Zip-a-Tone (a brand of screentone) was used to create texture and gray-scale patterns on maps (and comics). Printed on a thin, clear material with adhesive backing, many a cartographer spent hours searching through sheets of Zip-a-Tone, cutting it to size, and adhering it to different areas on the map. Now diverse pattern fills are available in GIS and graphic design software (Illustrator, Corel).

The problem is that while generic textures – Zip-a-Tone or digital – could be used as “area fills” for a contemporary map of nationalities (like the 1890 Nationalities Map), one would still have to engage in the painstaking task of calculating ratios and creating appropriate areas for the different textures: the same task that engaged Mr. Pierce more than 100 years ago.

When I come across maps such as the 1890 Nationalities Map it reminds me that, despite the millions of maps generated by sophisticated software every day, few are information rich, complex, and multivariate – Tuftian. At least in this context – the high art and science of multivariate, data-intense mapping – we may not be much better off than we were in 1890.

Deane Powell | Life | December 1, 1910

Denis Wood, co-author of Making Maps, has been working on an atlas of the Boylan Heights neighborhood in Raleigh, North Carolina since the mid 1970s. The atlas, which has never been published in its entirety, is called Dancing and Singing: A Narrative Atlas of Boylan Heights.

Inspired by Bill Bunge’s radical cartography in the 1960s and 1970s, the atlas contains diverse examples of creative, place-inspired maps, including maps of night, crime, fences, graffiti, textures, autumn leaves, routes, the underground, lines overhead, stars, and jack-o-lanterns. The atlas is of particular interest to those engaged in planning, urban history, urban geography, landscape architecture, participatory mapping and GIS, subversive cartography, counter-mapping, and psychogeography. Or anyone who enjoys creative mapping.

Sign Map (736kb PDF here)

The Atlas has been featured on NPR’s This American Life and in Katharine Harmon’s book You Are Here. All or or parts of the atlas have been shown at The Brattleboro Museum and Art Center, Brattleboro, Vermont (1989), the Tang Teaching Museum at Skidmore College in Saratoga Springs, New York (2001), at SCI-Arc in Los Angeles (2002), at Colby-Sawyer College in New London, New Hampshire (2002), at designbox in Raleigh, North Carolina (2004), and Publico Galleries in Cincinnatti, Ohio (2007). The image which opens this entry was taken at the Publico Gallery.

A description of the atlas by Denis and more of it’s maps follow.

I began the atlas in 1975 when I started teaching landscape architecture studios. I knew nothing about landscape architecture but I was a geographer. I thought I might help my students learn something about how to read the landscape by focusing hard on a small piece of it.

For a lot of reasons I picked the neighborhood I lived in. One of the things my students and I ended up doing was mapping a lot of its less obvious aspects. Out of this work, and the example of the radical geographer, Bill Bunge, and his mapping of the neighborhoods of Detroit, came the idea of making an atlas of Boylan Heights.

I pursued the idea off and on more or less seriously with different groups of students through the early 1980s. Then, in a series of studios and seminars, a handful of students joined me in a major effort to get something done.

There was a lot we wanted to do. Certainly we wanted to use the mapping to help us figure out what a neighborhood was, but we also wanted to use the mapping as a kind of organizational tool, as a way of bringing the neighborhood together and helping it to see itself. This meant we wanted to be able to get copies of the atlas into the hands of the residents and so we planned a black and white atlas that could be cheaply reproduced on a copy machine. At the same time we wanted to make something beautiful, almost a livre d’artiste. I in particular was impatient with distinctions between art and science – it was an important part of my teaching that these distinctions were arbitrary and obfuscatory – and I wanted the atlas to read almost like a novel.

One outcome of this was a paper I presented at the Schools Atlas Conference in Calgary in 1986, “Pleasure In the Idea: The Atlas As Narrative Form” (9.2mb PDF here.) (Cartographica, 24(1), Spring, 1987, pp. 24-45). Here I wrote about the narrative structure of atlases, and advocated making explicit what is usually implicit.

Sadly enough this paper may have been the only material outcome of the atlas project. Our energy flagged (or it was diverted) and the project ended up in boxes (lots of boxes) shoved under a table. Little work has been done on the project during the past fifteen years.

I think these boxes would still be under the table were it not for Ira Glass and This American Life. It was 1998. Ira wanted to do a show about maps. Ira’s producer learned about me from my cartographic rant, The Power of Maps (Guilford 1992). I was to give the show background and history. Ira must have assumed I also made maps, because after a long interview about the ground covered in my book, he asked about the maps I made.

I don’t make maps. I certainly never thought about myself as a mapmaker. Except for little sketch maps to give people directions, these maps of Boylan Heights are about the only maps I’ve done. When Ira asked about my mapmaking, it was these that came to mind. Ira so loved what I was saying that a second interview unfolded from the first, and that was the stuff he used for the show.

This American Life has a website and the show’s producer wanted maps to put on it. I’m not kidding when I say the maps were in boxes under a table, in a bunch of boxes under a table, this stuff jumbled up there, that here. Any remnant order had been destroyed when I’d moved my office. The show proved popular and has been replayed often during the past ten years.

Jack-o-lantern Map (2.8mb PDF here)

The atlas was going to be three sections, corresponding to what we’d decided neighborhoods did. This was to transform universal stuff – things in general – into particular things, into the ding an sich. And vice versa.

For openers a section concerned with stars and sunlight, rock and water, leaf and tree … in general. Except for the map of pumpkins, the maps we completed are from this section.

Street Light Map (2.6mb PDF here)

For closers a section concerned with the same things but … as they uniquely manifest themselves in their individuality, which is where the pumpkins would have been. In this section we’d concern ourselves with this shadow cast by this streetlight on this sidewalk at this moment. One night we walked the neighborhood with a light meter – it took hours – recording tiny incremental changes in light levels. From this we drafted (but never finished) a map of the light at night (different from the streetlight map which is no more than a map of the locations of the streetlights). As part of the spread we’d planned a very detailed rendering of a single block face. The background would consist of a photographic image of individual shadows on an individual sidewalk. It would be like … zooming in on the pattern.

In between these general and particular sections came the transformer section. At issue here was how Boylan Heights transformed a citizen of Raleigh (someone who could vote in its elections) into a unique individual (who had brown hair and green eyes and so on) and who lived here (at this unique address); precisely as the neighborhood transformed this individual, by dint of living at that address in Boylan Heights, into a citizen of Raleigh (just one of many). This section would have had a strongly processual, indeed historical character.

Among other things, we’d compiled a record of every property transaction from the subdivision of the original plantation grant to the present (this in the late 1970s (it would have to be updated (which at this point would not be easy (but extremely interesting)))).

We envisioned 64 double-page spreads, close to a hundred maps altogether, maybe more. In addition to those here, and more or less at random: sounds (walking the neighborhood with a sound meter, et cetera), mentions in the neighborhood newsletter, crime, fences, sidewalk graffiti, viewsheds (what of the rest of the city can be seen from any location in Boylan Heights), housetypes, yard furniture, landscaping, property values, property ownership, trees by category and species (many maps), the embeddedness of the neighborhood in other zones and districts (police precinct, school attendance zones, voting precincts, et cetera), time-space maps (of the paperboy’s route, the mailman’s route, city bus routes, et cetera), magazine subscriptions (we’d done an elaborate door-to-door survey in 1976, some of which we replicated in 1982 (it’s time to do it again)), weather, the color of the leaves in the fall …

There were going to be a lot of historical maps.

I’d mounted an insolometer on my roof to measure incoming solar radiation.

No wonder we ran out of energy!!

Let me describe some of the maps in a little more detail.

Light at Night Map: As I mentioned, we walked the neighborhood one long night with a light meter, noting our readings on a large scale map of the neighborhood we’d cut into smaller sheets. We never got around to reducing the data to the smaller scale the atlas would require, but I did make the light contour map for the 300 block of Cutler Street, and have blown up the photo for the background. It would be – could be – a neat spread.

Crime Map: We copied off the police blotter for the six months July to December 1981, and using the cops’ codes worked them into a map. The size of the number denotes the frequency at that location of that type of call. A 17 is a motor vehicle accident. A 16 is a vehicle blocking the flow of traffic. As you’ll see on the map of street signs, there are a lot at one intersection. No surprise that this is also the corner where there were lots of problems with cars (at the time, U.S. 64 and 70 turned 90º here).

From our perspective the maps of crime, street signs, and traffic were about the world … passing through the neighborhood.

Fences Map: Originally students designed their own map pages. Helen Waldrop drafted the map of the neighborhood’s fences which we’d have reworked for the atlas.

Sidewalk Graffiti Map: Susan Edwards drafted the map of sidewalk graffiti, all of which had been made in wet concrete. Naturally we made rubbings of all of them.

Lines Overhead Map (484kb PDF here)

Lines Overhead Map: Shaub Dunkley, our tree man, made the first attempts at this. What to show? There are as many things going on overhead as underground: electric, telephone, cable. How to get the data? The utilities map this stuff all the time, but the maps are proprietary. Finally Carter Crawford, a student, and I just went out and mapped the whole neighborhood. I’ve got this raw data. Also a couple of sheets of corrections following further field work. Still, what to show? And how? A graduated circle map might have made the relative density of connections most clear, but it wouldn’t have … gotten to the wires. Stubbed wires … Then the magical diagram which, while perhaps harder to interpret quantitatively, actually makes it most clear … what’s overhead.

Textures Map: For our Textures map we rubbed lots of “regular” sidewalk, house siding, tree bark, and all the manhole covers et cetera (some of which, radically reduced, appear in the text as breaksigns). The map itself is hard to visualize.

Stars Map (4.1mb PDF here)

Stars Map: I knew I wanted to situate Boylan Heights in everything, that is, in the universe, but it wasn’t simple to figure out how to pull this off. When it came to us to map the stars as they spread themselves over the neighborhood we knew it was right – the stars in the neighborhood – but it sounds easier to do than it was. Armed with a magnetic compass we lay down in the middle of the intersection of Boylan Avenue and Dupont Circle and looked up at the sky. We did a rough sketch of the horizon – fumbling flashlights – and the stars. A couple of days later we lay down in the intersection in the middle of the afternoon to get the horizon in finer detail. What we were trying to do was a kind of fish-eye view, splashing up out of the neighborhood into the night sky. Using an atlas of star positions we improved our plotting of the night sky. This was not easy either because the projection we were making and the projection the star atlas used were very different. We pulled the rest of the stars from Galileo’s Sidereus Nuncius both because they are so beautiful and because I liked the way Galileo integrated text and image. Crawford did the drawing, laid out the spread, and did the photomechanical work.

Mentions in the Newsletter Map: Our big discovery here was how important the address was. It didn’t matter who lived at the most frequently mentioned address. Whoever it was, was always most frequently mentioned. This took us into the social geography of the neighborhood, and how it maintained the social structure built into the neighborhood by its original planners (which was reflected by the distribution of pumpkins at Halloween and everything else).

Car Space Map (1.6mb PDF here)

Autumn Leaves Map: We simply walked the neighborhood annotating the leaf color on a sheet of trace layered over one of Shaub Dunkley’s maps of the trees in Boylan Heights. Shaub was the tree man on our team. How to display this? I transformed the color image into color names and began laying these down on the compositor. Then I thought, maybe use the word “leaves” and vary the typeface to indicate the color. I’d have gone with the color names. It would have made a gorgeous map and piece of concrete poetry. A compositor was a special IBM typewriter you could “set type” on. I should say that we had no computers. Boy, that’s a revolution that’s come through fast! I did all the autumn leaves stuff.

Routes, Time-space Maps: We did a lot of work on this, but finished none of it. One was a map of a paper route, with a black dot representing one newspaper passing through the neighborhood. It started with Scott Lameroux’s route. Scott delivered bundles of Raleigh Times to paperboys. (The Times is no longer published. How time flies!) Scott ran through the neighborhood to drop a bundle of papers in front of Lester Mim’s house (where the black dots begin to rise vertically). Scott left the neighborhood with no black dots because he’d left the paper we’re following. The papers sat there. Since the bundle wasn’t moving in space but only enduring in time, the dots simply rise up the page (in the temporal dimension) until Lester picked the bundle up. Lester was the paperboy. His paper route snaked through the neighborhood, and the black dot moved with Lester until he delivered the paper we’re tracking to Oot’s house on Dorothea Drive. The black dot representing the newspaper simply rises here, because it doesn’t leave the house. Lester went on to finish his route and return home. When Oot’s family finished with the paper they threw it out. The paper rises off the Day 1 page onto the bottom of the Day 2 page sitting in the garbage can (today this would be the appropriate recycling bin, another dramatic change). The entire snake on Day 2 is that of garbage truck #1135, Larry Mitchell’s. When Mitchell’s truck picked up the paper the black dot joined the truck’s route. Tim Hess complied the base map. Diane Pacella complied Lester’s route. I complied the other routes and drew the diagram.

Another map shows the buses passing through the neighborhood between 3:00 and 3:20 any weekday afternoon, time rising vertically again. Included here would be a map showing the same routes with the time collapsed out of them lying flat on the page.

Triangle Correctional Center (since closed) ran a bus of working prisoners through the neighborhood back to the prison. Capital Area Transit ran the old Boylan Heights route (since abandoned). Others would be school bus routes, two from one elementary school, one from a 6th grade center (since absorbed into a middle school), and one from a junior high (since turned into a middle school).
We also made maps of the other CAT bus routes, and a map of the mailman’s route.

Underground Map (1.7mb PDF here)

Underground Map: Did we struggle to understand and represent the gas, water, and sewer systems! Dawn Davis and Tim Hess went down to the city and the gas company. They came back with the raw data, long linear maps of the streets and alleys. Tim then drafted many versions. Carter Crawford built on Tim’s work in his effort to combine all the data into a single image. It took him a lot of drafts. Most of the trials vanished into the wastebasket. I used the final image in The Power of Maps (p. 19), to make a point about the social construction of maps. It’s the most widely reproduced of the maps.

Boylan’s Hill Map: The problem with Boylan’s Hill was to get as much information as we could into the spread and still maintain some semblance of elegance. At the very least we wanted a contour image, but to make plainer what this means, a profile as well. Since the spread was in the general section, we also wanted to show something of the topography of the area surrounding Boylan’s Hill. We made a lot of stabs at this. We thought of making a model and photographing that, but Tanaka’s method for representing landform relief proved more than adequate. Jimmy Thiem did all the spade work for this spread, but the final work is Crawford’s.

All maps copyleft Denis Wood (2008).

Google’s My Maps allows the easy creation of pseudo map mash-ups, where you can map your own data as points, lines, and area symbols with Google Maps as the background.

I wrote about My Maps – basic how-to and some of its limits – in another blog post, Allelopathic Maps & Google’s “My Maps.” One of the My Maps limits, the inadequate and corny set of available map symbols, has been removed: you can now create and use your own map symbols in My Maps.

To work with My Maps you need a Google account, and to use custom symbols (icons, as Google calls them) you need some server space to upload your symbols: you must provide a URL to the symbols. You should be able to find a free web hosting service that allows hot-linking (the placement of an image hosted on the free site in a My Maps map in this case). You can also embed images in the pop-up balloon associated with points, lines, and areas on My Maps, and you need server space to host those files.

In Custom Icons for Your Maps, a posting on the official Google Earth/Maps blog, PNG files with transparent backgrounds are recommended, although JPG and GIF should also work. PNGs and GIFs can have transparent backgrounds, essential if you don’t want a white box surrounding your symbol. As Google says PNG, lets PNG.

You can use or modify an existing symbol or create your own. A bunch of curious map symbols, for example, preface the map symbol chapter in the Making Maps book:

300 dpi PNG files of these symbols, annotated (376k) and unannotated (84k) can be downloaded.

Many graphics software packages allow you to grab a single symbol and save it as a PNG file with a transparent background. Two well regarded, easy-to-use, and free software options, for the Mac and PC, are detailed below.

•••••••••••

If you are using a Mac, try Seashore. Download the open-source, free software here. We will work with the unannotated PNG linked above. There are several routes through Seashore to a transparent PNG, here is one:

• Open the file of symbols (mm_symb.png).
• Zoom to your symbol of choice.
• Use the Rectangular Select tool to select a box around the symbol.
• Copy.
• From the File menu select New: select Transparent background and, under the templates menu, select Clipboard.
• From the Selection menu choose Anchor Selection. Save the file as a PNG.
• To remove the white background, and thus make the file background transparent, grab the Color Selection (or magic wand) tool and click on the background color (white in this case).
• Hit delete.
• Hit Save As… to resave the file.
• You should be able to look at the file on your computer desktop to make sure the background is transparent.

•••••••••••

If you are using Windows, try GIMP. Download the open-source, free software here. Again, we are working with the unannotated PNG linked above. There are several routes through GIMP to a transparent PNG, here is one:

• Open the file of symbols (mm_symb.png).
• Zoom to your symbol of choice.
• Use the Select Rectangular Regions (Rect Select) tool to select a box around the symbol.
• Copy.
• From the File menu select New.
• Under Advanced Options and Fill with: select Transparency and hit OK.
• Paste.
• To remove the white background, and thus make the file background transparent, grab the Select Contiguous Regions (magic wand) tool and click on the background color (white in this case).
• Hit Cut. You may have to select white interior areas and Cut them also; make sure the checkered background (indicating transparency) fills all areas you want transparent.
• Hit Save As… and name the file; under Select File Type choose PNG Image.
• Hit Save.

•••••••••••

Upload your symbol to your server space, and note the URL of the symbol. You can open the URL to the symbol in a browser to make sure it works. Go to My Maps and add a Placemark and edit it (or edit an existing Placemark). Click on the icon (the red map pin in this case):

Click on the Add an Icon link and enter the URL to your map symbol. If you want to try the symbol I created, the URL is

• http://makingmaps.owu.edu/blogs/sawmill.png

The symbol, and any other symbols you add, will be stored under the My icons link.

Your new symbol with a transparent background should show up on the map:

Tip: when using My Maps, you can map out an existing KML or KMZ file of placemarks by entering a URL to the file in the search maps box (then hit enter). A KMZ file to test is here:

• http://makingmaps.owu.edu/blogs/Geog_222_Spring_07.kmz

Click on Save to My Maps. Unfortunately, you can’t easily edit the Placemarks! Something for the Google folks to work on.

Update 10/22/08: see Map Animation with Google Earth (Google Document) for related information.

Google Earth can display geographic data with a time component, and thus show animated maps. Animated mapping has garnered much attention among cartographers in the last decade.

I created a few Google Earth animated choropleth (literally, area-filling) maps of population change in Ohio. One map shows total population by county from 1900 to 2006. The other shows percent population change from decade to decade. Details on how I created these animated maps along with links to the downloadable KMZ files are below.

In the most recent version of Google Earth (Mac 4.0.2736, Windows 4.1.7087), a “time slider” appears if a KML or KMZ file contains temporal data. For example, many GPS receivers can attach time to a recorded location. When formatted correctly, a series of points can be viewed as an animation in Google Earth, as in this example of a Whale Shark track [download kml]. More examples are in a list of the “Top 10 Time Animations from 2006 in Google Earth” and “Animation Roundup.”

I have not seen any animated choropleth maps in Google Earth. I have my students create animated choropleth maps in one of my courses at Ohio Wesleyan, and thought it would be swell if the students could display the animated maps in Google Earth. The entire project works well for an undergrad cartography/GIS course. Here are my two examples:

Download Ohio Population Change 1900-2006 (192kb kmz)
Download Ohio Population 1900-2006 (208kb kmz)

There are undoubtedly many paths to getting animated maps into Google Earth. I used Excel (to process historical census data), ArcGIS 9.2 to create the maps, and a utility to export from ArcGIS to Google Earth (Arc2Earth). No free software here, but you may be able to repeat the process using less cash-intensive software (and I will try to do so – in a future post). Also, the animations have a few flaws (the flash between maps, a few counties show up with no color, etc.) that I have yet to work out. But the animations work, more or less.

For more information on timestamps and timespans in Google Earth, see the tutorial “Adding Time as a Fourth Dimension.”

The process below will be detailed in my class lab notes here when I update them later this summer. The jargon below will make sense to those who use ArcGIS.

1. Get the historical population data, via the web: grab U.S. Census historical county population data (1900-90) and (2000-06) for your state.

2. Clean up the data in Excel: so it looks like the image below; then save as DBF4.

3. In ArcGIS: Add a map of U.S. counties (I use ESRI’s counties.shp), select your state, and save the selection as a new layer. Join your DBF4 file to your state, using the county fips codes (FIPS) as the common column.

4. Export your state layer (right mouse click on layer, data, export data). This extracts your state from the entire US shape file, and also combines your joined data: you cannot export an ArcGIS project with a joined table to Google Earth using Arc2Earth (step 9 below) [update: I have been told this bug will be fixed in the next version of Arc2Earth]. Add this layer to your ArcGIS project and delete all other layers.

5. Generate the population change data: in a new field, use the field calculator to determine the percent change from decade to decade. For 1900 to 1910:

( ( ( [ Y1910 ] – [ Y1900 ] ) / [ Y1900 ] ) * 100 )

where Y1900 and Y1910 are the names of the columns of total population for 1900 and 1910. Do this for 1910 to 1920, 1920 to 1930, etc.

6. Copy and paste this layer: create as many layers as you have data for: one for 1900-1910, one for 1910-1920, and so on. I did the same for the total population data (one layer for 1900, one for 1910, etc.) but in a second ArcGIS project file (so two ArcGIS mxd files, one for population change, one for total population). Derived data, such as percent change, is best shown with a choropleth map. Total numbers, such as our total population, are not optimal for choropleth maps because geographic areas vary in size (and thus there may be more of something in a geographic area just because it is larger). But Ohio’s counties don’t vary much in size, so we can map totals (and gaze upon the map, as should you always, with a critical eye).

7. Classify your data: If you want to compare a series of maps, you have to use the same classification scheme, that is, same number of classes and same breaks between classes. The bottom of the classification scheme must be the lowest value in all the years in your series, the top of the classification scheme the highest value. If you have negative and positive values (as with my percent population change data) 0 is a good break. Six or seven classes are a good default but you are certainly allowed more or less. Create a second classification scheme that fits your total population data (in your second ArcGIS project file).

8. Choose colors for the maps: Choose an appropriate color scheme (color value is best). If you have negative and positive values use a double-ended color scheme (as in my percent population change example above). I made the county borders white, and 3 points in size. It looks bad in ArcGIS but good in Google Earth (always design for the final medium). ArcGIS allows you to set the classification scheme and colors in one layer and import them to another layer. This saves you from re-typing the custom classification scheme on each layer.

9. Export to Google Earth using Arc2Earth: While there are several free software packages that convert ArcGIS data to Google Earth data, Arc2Earth was strongly recommended to me (and was affordable, at least for my non-profit college). Arc2Earth operates from within ArcGIS. When you export the whole map, you have a series of options. I chose not to have a legend floating near the map itself (the legend does carry over to the Google Earth Places panel) and left the other settings at default. Select a specific layer and hit the time data tab. I applied a time value to the entire layer: so population change 1900-1910 is set as in this image:

Adjust the time settings in subsequent layers (change 1900 to 1910 and 1909 to 1919 for the 1910-1920 layer, etc.). Hit export.

10. Open the files in Google Earth: when you open the files, you should see the time slider:

You probably want to slow the animations down: hit the clock icon (on the left side of the time slider) to set options, including animation speed. You can hit the play button and watch the entire series of maps, animated, or grab the slider bar and move through the layers yourself.

The flashing of the animation is distracting, but that might disappear if overlapping time spans are imposed on each layer (something I will experiment with). Also, the time slider is broken down by month, implying a monthly resolution of time data rather than the decade resolution of census data. There are certainly a myriad of other details – why a few counties dropped out when exported from ArcGIS, how well colors are maintained from ArcGIS to Google Earth, where best to place a legend (on the map or in the Places panel), how to use placemarks and overlays in addition to the animations, etc. But it is great to be able to view animated maps in Google Earth.

I am hopeful that the diversity of ideas about animated maps and animated map types cartographers and others have generated over the last decade will translate into Google Earth.

Update: the GeoChalkboard Blog has a related tutorial entitled “Creating Google Earth Time Display Data with ArcGIS.”

The proliferation of mapping sites on the web provides ample fodder for critique by the map police (cartographic insiders). I usually feel a bit bad whining about the cartographic limitations of such sites. Cartographers have a history of obsessing with rules and such obsession has, arguably, limited creativity and undermined innovations. Bad cop. However, not following the rules does not necessarily produce creative and innovative mapping. I, for one, don’t entirely enjoy being the map police, but will try to at least be a good cop.

Lets look at a site that has been around awhile: The Modern Language Association’s Language Map. The site allows you map language data collected in the 2000 U.S. Census. A nice focused site with interesting data (I use it in my classes and the students enjoy pondering the patterns): here is the default map of the total number of language speakers in each county:

The basic language map allows you to view 33 different languages, mapped by county in the U.S. The total number of people who speak a particular language (above) can be mapped, but mapping totals can be deceptive, as the sizes of the counties vary. Thus a county may have more speakers of a particular language just because it covers more area than a smaller county. To account for these variations in county size, map the data as a percentage (the percent of people in a county that speak a particular language, see below). But you can map totals and there are sometimes good reasons to do so. Just realize the potential limitations of what you are seeing.

The basic map also allows you to map the data by state or zip code, and to add additional information (cities, roads, county & state names, etc.). The basic map does not allow you to change the classification scheme, and the arbitrary classification schemes the map uses are weird (but probably chosen so different maps can be compared to each other). Different classification schemes will produce different patterns, so take the patterns produced on these maps with a grain of salt: they will change if you (could) change the classification scheme. It would be also good to flip the legend, putting higher numbers (high =more) at the top of the legend, and lower numbers (low=less) at the bottom.

Here is the % by county map of the same data:

The symbolization of the county (or state or zip) boundaries on the map is annoyingly prominent. One of the Six Fancy Ideas (future post!) I took from Edward Tufte’s first book (The Visual Display of Quantitative Information, 2001) is to remove or minimize non-data ink, or, in this case, less important data-ink. This is a version of the old cartographic adage about visual hierarchies: stuff on your map that is more important should jump out, and stuff that is less important should fall back. The county boundaries (in gray) are so prominent that they obscure the data in smaller counties on the MLA map (at a national scale) and are certainly overly noticeable at all scales. Why not just make the boundaries white, and as fine as possible, so we can see the most important part of the map, the language data?

The map projection (the manner in which the surface of our 3D earth is flattened to 2D) is the plate carree, a typical default for web mapping software. Unfortunately, the plate carree distorts area (as you move north) thus this map projection is distorting the data. Dammit! Counties and states in the north are bigger than they should be in comparison to the counties and states in the south, which may distort your interpretation of the area patterns on the map.

Finally: map crap alert! Why include a north arrow when the vast majority of map viewers will recognize the U.S. and that north is up is? Why a scale bar showing 838 (?!) miles? Zoom in and you get 419 or 209 miles. Is anyone ever going to need to make measurements on this map? And the plate carree projection distorts distances anyway.

So: a decent, focused and useful site that could use a few tweaks. The MLA site also allows you to compare two language maps and play a bit with the data (in the Map Data Center).