I’m getting into more canvas and JavaScript for interactive mapping. Much of the Flash/ActionScript work I’ve written or come to rely upon is directly portable to JS/canvas. What’s missing is a sweet RIA framework and IDE for the kind of development Flash and Flex have made possible for years.

Luckily it’s not hard to roll our own interactive web map using web standard technologies. In this post I’m just showing off the basics: dynamically loading geodata, projecting it client-side, and rendering to the canvas element.

Hopefully the above map shows up for you. It’s loaded into this blog post with dynamic KML data, projected using the Proj4js library, and drawn onto HTML’s canvas element using JavaScript. You can check out the P.O.C. on a separate page.

Loading geographic data

It all starts with data. Points, polylines, or polygons — typically defined by latitude/longitude coordinates. Your data may be in a CSV file or in a database. For a simple interactive web map it’s best if it’s in a common GIS file format, like the Shapefile or KML.

These days, it’s not too hard to load a geographic layer on top of a web map — using Google Maps or OpenLayers, say. But since we’re looking down the road to interactivity, custom projections, and thematic mapping, it’s best to roll our own. Luckily, getting the data in is pretty easy.

In ActionScript I would use Edwin van Rijkom’s ESRI SHP parser, my own E00 parser, or some simple custom methods I’ve written to load in KML documents. Tom Carden of Stamen has done some great work porting the AS3 SHP library to JavaScript, with additional classes and methods to allow basic layering, panning, and zooming.

Carden’s classes are great; for demo purposes, and to keep this as lightweight as possible, I’ve just written a quick JavaScript method to grab what I need from a KML document:

You’ll notice a bit of jQuery in there. And you’ll also notice that it grabs only coordinate data and works only for polygons. But it produces an array of feature coordinates, which is an array of ring coordinates, which is an array of lat/long coordinates, which is all we need for the current application.

Projecting geographic data

One of my biggest beefs with the typical online map providers is that they’re all rendered in a Mercator projection. No problem for most purposes (and great for producing those 90 degree road intersections), but not so great for country-level mapping and bad for many thematic mapping pursuits. That’s one reason we’re rolling our own here.

PROJ.4 is a generally sweet projections library, originally written in C by Gerald Evenden then of the USGS. It’s been ported to JavaScript as Proj4js. To use it you just have to define source and a dest objects:

And thereafter you can call

where pt is any object with x and y properties. So all coordinates gathered from the KML above can be run through the Proj4js.transform() method, in this case applying a custom Albers Equal Area projection (proj=aea) for the African continent.

Drawing geographic data on the canvas element

The results of the above can be easily rendered to HTML’s canvas element using JavaScript. I’m used to ActionScript’s Graphics class, and its assorted vector drawing methods. Of course, given the common ECMAScript heritage, the JS methods are nearly identical. So the projected linework is rendered thusly:

That method’s made a bit longer by that bitchin’ drop shadow (sorry Firefox, but you Konqueror folks should be cool). See above, or the P.O.C. on a separate page.

Up next

So far this has been pretty sweet: we’ve loaded coordinate data dynamically, projected it, and drawn it to the canvas element. But it hasn’t exactly lived up to the “interactive” part of the title. Next time I hope to get going on panning and zooming, feature mouse-over, and perhaps even attribute loading and thematic mapping.

French cartographer Jacques Bertin died in Paris on May 3rd. This news has been out on a few English-language lists, but I otherwise hadn’t seen it mentioned, so thought I’d note it here. Bertin’s Semiologie and seven visual variables greatly influenced cartographic and graphic design theory and education over the last 40 years. Sadly out of print for years, his Semiology of Graphics will be reissued in extended form by ESRI Press this November.

photo © pavilion tone

For some more indie maps, check out our gallery. To try it out, just head over to indiemapper.com: you can sign up for a free 30 day trial without entering a credit card; after your trial, indie is just $30/month ($20 academic). Oh, and I guess there’ll be a free version, but it’d be pretty embarrassing if you were caught using that.

A little history

Indiemapper took a long time coming. The seeds were planted in December 2007. I had just discovered Edwin van Rijkom’s AS3 SHP library, which lets you load and draw ESRI shapefiles at run-time in Flash. Using widely available formulae for simple projections like Mercator and Lambert conformal conic I began loading and projecting shapefiles in Flash at run-time pretty quickly. These were heady times, and this seemed pretty cool.

Andrew “Woody” Woodruff joined in and we set off to create a full-fledged thematic mapping tool in Flash. We designed the product for ourselves, honestly, and the cartographers we knew. We hoped it’d be useful to other map-makers, whether casual or professional, who may not need nor want to learn the extensive analytical capabilities of a modern GIS, but do want a multitude of symbology and design options for their geographical data.

We presented a super-early version of indiemapper to MACDAD on May 9, 2008.

As you can see, the basic workflow and trademark indie (#0099cc) blue were already in place.

We had some pretty gnarly styling panels, though; at this point we were just working in Flash and creating all the panels from scratch.

Of course things like getting Master’s degrees, real paying projects, moving, and having actual jobs (however briefly) got in the way, and development on indiemapper didn’t restart for another year or so. Axis Maps (where Andy became a partner in Summer 2008) took up development of indie while I went and did something else for a while. Axis Maps released indieprojector as a preview in May 2009, not realizing it’d be another 11 months before the full thing was ready. I joined the team later in 2009, and have been co-developer (with Andy) since.

Development

Mark and Ben are our static and interaction designers here, with Mark also providing the “learn more” content, drawing from his years of academic cartography experience. Dave is our project manager, marketing guru, and AJAX guy; he created the backend to our user management and sharing systems.

Andy and I both wrote AS3 code and MXML markup, but he was mostly focused on the former with me working more on the latter. This meant I programmed more of the framework and UI, while Andy was rocking the math-heavy projections work and all the exporting/imp/svg stuff.

When I joined the company, I immediately set to work refactoring the indiemapper code base to incorporate the MVC framework Mate. Mate, as they say, is “a tag-based, event-driven Flex framework”. I had to use Cairngorm for work a while back, and by comparison Mate is quite lightweight and unobtrusive. There’s much more to say here, but I’ll save that for later.

Though it’s old school what with FXG, I still really like Degrafa, and used it a fair amount for skinning. Aside from Mate, Degrafa, and a few open source Flex components, we pretty much wrote our own little AS3 GIS SDK.

This is just the beginning

Given our platform and development philosophy, we can quickly respond to our users’ needs in a thematic and reference mapping tool. Please let us know what you want.

I think our first goals are:

• performance improvements so you can load larger geodata files faster
• attribute editing and joining
• more charting options for your data as an aid to classification and symbology selection
• more symbologies, like chorodot and additional cartogram styles
• custom choropleth color schemes and graduated symbol size schemes
• line generalization

But let us know what else you’d like to see. We’ve already been able to respond to a few new feature requests and, oh, maybe one or two bugs in our first week.

Many aspiring social cartographers of the last few decades (including myself) drew inspiration from the legend of William Wheeler Bunge, Jr. Unfortunately, little information about this geographic and cartographic pioneer exists online. Below’s a biography of the radical cartographer and anti-academic, “Wild Bill” Bunge.

Mini bio

There are unfortunately few sources of biographical information on William Bunge. Here, I rely on the autobiographical first chapter (”Preface and Acknowledgements: Rethinking”) to Bunge’s last published book, the Nuclear War Atlas (1988); UCSB professor Michael Goodchild’s (2008) review [pdf] of Bunge’s (1962) Theoretical Geography; many of Bunge’s articles (see “Further reading” below); and a handful of secondary sources.

Early career

At the start of the Korean War in 1950, a 22-year old conscripted Bunge was teaching atomic warfare at Camp McCoy, Wisconsin. He completed his Masters degree in Geography in 1955 at the University of Wisconsin (natch), where he was influenced by Richard Hartshorne and Arthur Robinson. Establishing a pattern to be repeated throughout his turbulent academic career, Bunge was rejected by Wisconsin and continued his PhD studies at the University of Washington. There, Bunge and fellow grad students Richard Morrill and Waldo Tobler were influenced by the quantitative geographer William Garrison.

Interestingly, this was also the height of the University of Washington’s cartography program. The program led by John Sherman and Donald Hudson was then rivaled only by the University of Wisconsin. Chapter 2 of Slocum et al’s cartography text describes the era well.

Bunge completed his PhD in 1960; his dissertation would become the touchstone in quantitative geography, Theoretical Geography.

Theoretical Geography and the spatial-quantitative revolution

In a 2001 retrospective on the classics of human geography, Kevin Cox wrote that Theoretical Geography is “perhaps the seminal text of the spatial-quantitative revolution” and that (speaking of the broader field of human geography) “if we want to see where we have come from, what our intellectual debts are, there are few better places to start than Theoretical Geography.” Bunge’s work promotes geography as a science and mathematics as the most fruitful language and tool for its study. As the title makes clear, the author was concerned with theory, and believed that simple laws were discoverable about the patterns of social phenomena found on the earth’s surface.

In part, this was a reaction against the idiographic perspective then present in the discipline. Idiographic methods are concerned with describing the infinite variation in these social phenomena rather than discovering generalizable laws common to the entire surface. Bunge thought such an idiographic view would continue to marginalize geography as a science, but received much opposition from geographers who valued the traditional role of description in geographic practice. Later opposition formed against Bunge’s conflation of geometric pattern with explanation; mathematical functions and simple geometric patters couldn’t, in and of themselves, be said to explain anything.

The ideas contained within are too complex and far outside my areas of expertise to cover here; for a better summary of the issues and debates involved, see Michael Goodchild’s 2008 review (pdf). Though his book would become a classic, Bunge had a hard time getting it published stateside, and apparently Torsten Hägerstrand had a hand in its eventual publication by Swedish (”that freer place”) company Gleerup in 1962. Before this, but with his University of Washington PhD in hand, Bunge received his first academic appointment at the University of Iowa in 1960. By 1961, he’d been fired. The next year, with his book published, the activist/academic Bunge moved to the Detroit neighborhood of Fitzgerald to join the Geography Department at Wayne State University.

Detroit: Fitzgerald and the Detroit Geographical Expedition and Institute

Fitzgerald: Geography of a Revolution was published in 1971, a year after Bunge’s infamous Detroit Geographical Expedition and Institute (DGEI) lost the support of Michigan State University. But it was written in 1967, the year before Bunge formed the DGEI, so will be covered first.

Fitzgerald is an experimental work of urban geography, and one of the most impressive scholarly works I’ve seen. The delay in publication was due to the book’s odd size (23 x 29 cm), format (text, photos, and maps of divergent scale and symbology) and content (very critical of the pervasive racism Bunge seemed to find everywhere). Bunge describes Fitzgerald thusly:

Fitzgerald: The Geography of a Revolution is a humanist geography, describing Fitzgerald, a community in Detroit. The book is science: its data are maps, graphics, photographs, and the words of people. But the book also makes a value judgment — the desirability of human survival — and thus transforms itself into a steel-hard hammer of humanism.

Fitzgerald’s history is traced from the first white settlers arriving in 1816 to the racial tension and revolution Bunge saw taking place around him in the 1960s.

Much of Bunge’s cartographic theory is contained in the foreword to the book. Speaking of a historical farm map created for the book (portion above):

Maps attempt to integrate over time, that is, maps assume an average span of time. This means that nothing that moves is mapped, and therefore property is inherently preferred over humans…In order to restore truth to the map it is necessary to achieve a fiction of accuracy through an assumption, namely that the map is drawn at an exact instant of time. In this case, the time is June 20, 1915 at 2 p.m. on a sunny day. This fiction freezes the men and horses on the roads, the strawberry pickers in the fields, as well as the crops in rotation and the animals in pasture. This restores life to the dead map of property.

Bunge wasn’t a passive observer of the revolution taking place in Fitzgerald. He was an active member of the Fitzgerald Community Council and developed deep connections in the neighborhood; this laid the groundwork for the Detroit Geographical Expedition he would later found (more below). Here, Bunge at a block club meeting in his Fitzgerald home:

And Bunge in Fitzgerald doesn’t merely describe; he advocates. Here, Bunge on cannabis:

The confusion between marijuana and heroin and other hard drugs seems self-serving on the white adult society. Marijuana grows so easily and abundantly that the apparatus of provision is uncomplicated. It is almost like air, a free resource. Further, unlike alcohol, marijuana seems to produce no aggressive outburst, judging from the teenage dances where the odor in the men’s room indicates its use. It has been argued that marijuana leads to heroin and other hard drugs, but cause and effect in this case is murky since baby’s milk also leads to heroin in the sense that it always precedes it.

The above must smack of the idiography Bunge opposed with Theoretical Geography. Bunge tried to preempt such criticism by insisting on the “disciplined objectivity” of Fitzgerald and the generalizability of the story:

Another major technical difficulty is the generalizability of the story. What can we learn about America from studying a core region of only one square mile? At first it was thought that especially the historic geography would be very spotty and unrepresentative of America, but discovery after discovery gradually reversed this apprehension. Who could possibly believe that this region was typical of all America? Would one square mile in the middle of Cincinnati or Miami turn up Indians, land speculators, gypsies black pioneers and so forth? Yes, if it were studied deeply enough.

Fitzgerald’s reception at the time wasn’t great. Bunge attributed all negative responses to racism and entrenched conservative attitudes within academic geography at the time. Legitimate criticism actually centered on the experimental method employed, Bunge’s generalizations of white racism, and the conclusions drawn from a square mile study area. Further, Bunge’s loving but at times simplistic descriptions of “black culture” would now appear politically incorrect or as unfounded/inaccurate as any racialist descriptions.

Detroit Geographical Expedition and Institute

Besides Theoretical Geography, Bunge should be most well known for the radical mapping + educational mission undertaken in downtown Detroit between 1968-1970 under the name Detroit Geographical Expedition and Institute. Bunge formed the DGEI with a poor 18-year old resident of Fitzgerald, Gwendolyn Warren (below at 16, from Fitzgerald).

Variously affiliated with the University of Michigan, Michigan State University, and Wayne State University, the Expedition attempted and at times succeeded in providing free college courses to young inner-city Detroit residents. Bunge wanted to do research in the black community while also teaching the skills necessary for them to conduct research for themselves. All volunteer faculty were used, and the Expedition was generally successful in attracting experts from across the U.S. to teach courses at facilities freely provided on Wayne State University’s Detroit campus.

Classes focused on geography and geographic skills. Two courses — Cartography and Geographical Aspects of Urban Planning — provided the context for perhaps the Expedition’s greatest achievement: A Report to the Parents of Detroit on School Decentralization [pdf] (1970). Expedition participant and Geography professor Ronald Horvath describes the relevance of cartography to the students:

The material covered just did not seem terribly important to the students. Before the request came in to do the decentralization study, a lot of effort had to be spent shoring up the morale of the students, who had real difficulties just showing up to even free classes — some came hungry, others couldn’t afford bus fare, one student had beer living in a car for five weeks. Learning how to make a clean line, lay a rip-a-tone pattern, or design a map with the right Combination of point, area, and line symbols did not seem to be critical knowledge to members of a survival culture. But the school decentralization study made sense. The next three weeks both saved and came to define the potential of the Expedition.

The decentralization report — rich in graphs and maps created by Bunge and the Expedition’s students — was adopted by a community group and forced the Board of Education to respond to charges that its school districting plans were illegal. The Expedition released a few other documents using cartographic experimentation as a weapon of social justice, including the “Geography of the Children of Detroit”. At its height in the Spring of 1970 the Expedition enrolled nearly 500 inner-city students in 11 different courses. By that fall, zero courses were being taught and the Expedition was effectively over, having lost the support of Michigan State University.

Leaving the U.S.

Below you’ll see Bunge’s name on part of an infamous listing of 65 “radical” speakers compiled in 1970 by U.S. Representative Richard H. Ichord, then chairman of the House Un-American Activities Committee. This blacklist was the subject of much controversy and a federal court order actually prohibited official government publication of the names of these anti-war protesters and/or open communists.

After his brief (1960-61) stint at the University of Iowa, Bunge was employed for the Fitzgerald and early DGEI days by Wayne State University (1962-69). He was fired from Wayne State University when his attempts to draw black geographers to the department rankled administrators. Unemployment allowed Bunge to travel to campuses in the U.S., Canada, and Britain on his “have map, will travel” campaign. He used an overhead projector to show “peace maps” as a protest against U.S. militarism (these would evolve into the Nuclear War Atlas; see below). Bunge’s speeches during this period drew the attention of Representative Ichord, and the resulting blacklist made Bunge basically unemployable in U.S. academia.

Bunge moved to Canada and had two more brief stints in academia: at the University of Western Ontario (1970-71) and York University (1972-73). This period was Bunge’s most prolific with regard to non-book publication: I count 12 single-author pubs between 1970 and 1974. In this period, Bunge formed a few short-lived expeditions on the Detroit model: the Society for Human Exploration (1971-72), Toronto Geographical Expedition (1972-73), and the Canadian-American Geographical Expedition. Bunge touted such expeditions as the highest calling for the geographer.

I’ve barely done justice to the history, theory, and impact of these urban expeditions. I recommend seeking out the primary and secondary material on the period (see “Further reading” below), particularly Ronald Horvath’s “The ‘Detroit Geographical Expedition and Institute’ Experience”, Bunge’s “The First Years of the Detroit Geographical Expedition: A Personal Report”, and the decentralization study linked above.

Later in the decade, now outside both Canadian and American academia, Bunge would become a cab driver in Quebec. Though an odd turn for such an influential geographer, Bunge had previously compared cab drivers glowingly to the native guides who led explorers across the Americas and Africa. Bunge had elsewhere praised cabbies: the “regions within cities are better known by cab drivers than by ‘urban geographers’”. Bunge lived in Arthabaska, Quebec from some time in the late 70s to some time in the early oughts.

The 80s and the Nuclear War Atlas

William Bunge was published thrice in the 1980s: one article (correction: found another article, though it was originally submitted in the late 70s), one epilogue, and one book. The book, Bunge’s latest, was also his most experimental and map-heavy work. The intro contains a limited biography of the man himself. Further chapters include remixes of his previous works, copious maps (see “Maps” section below), and Bunge’s ruminations on the potential destructiveness of U.S. and Soviet nuclear power.

The atlas started as a poster (from Martin Dodge’s blog):

In the appendix, Bunge called Geography the “Queen of the Peace Sciences”:

Interestingly, geography, a subject currently in low academic status, dying out in one university after another, can actually make a serious claim to be the ‘queen of the peace sciences’ — not out of our innocence, but rather out of our guilt. It is the quintessential war science. This might account for its decline. Today, who needs more expertise on how to kill people?

The Nuclear War Atlas is a significant geographic and cartographic work. Again, I do it little justice. The atlas may have had more traction had the wall not fallen a year after its publication, but the maps and text within are still necessary reading for any radical geographer or social cartographer.

Back to the USSR

Since the Nuclear War Atlas little has been heard from William Bunge. Nothing was published in the decade until 1998. By this time Bunge’s social liberalism seemed to be verging on Stalinism. His latest published article (that I know of) — 1998’s “Where are the Germans?” — was printed in Northstar Compass, the journal unabashedly dedicated to the re-establishment of the Soviet Union as a socialist state. I can’t find the text of the article (it’s not included in Northstar’s archives for the issue) but its publication was noted in a Spring 1999 edition of MADGEOGNEWS [pdf].

The same article notes that Bunge “was awarded the ‘Representative of the Parti communiste du Québec to the Federal government’ in 1998″.

A 2001 letter also contained a bit of revisionist Stalinism. The below was included in some discussion on a World Congress for Solidarity and Friendship with the Soviet People page.

We have a lot of basics in common. We remember and respect the fact that under the brilliant leadership of Joseph Stalin the Soviet Union practically single handedly defeated Hitler. Almost ninety per cent of all the military fatalities sustained by Hitler’s Nazi hordes in World War II were rendered by the Soviets, leaving but ten per cent for all the other “allies.” Many Western war veterans were saved by the defeat of the fascists by the Soviet Red Army.
We are also enthusiastic about the restoration of the USSR since its collapse had practically collapsed most of the left movement worldwide. Philosophically there would be no understanding of even pre-Leninist works, Marx and Engels, without the Soviet press and its publishing of all their works in many languages of the world.
At the World Congress which I shall attend I will try to bring the following question to discussion: “How does religion differ in Fidel Castro’s Socialist Cuba compared to Stalin’s Socialist Soviet Union?”

Certainly Bunge always leaned towards Marx — his later Stalinist leanings should by no means detract from his 30+ years of service to the downtrodden of America, those who’d been failed by the state supposedly dedicated to serving them. Bunge is alive and (I hope quite) well, and I regret missing this document distributed at the 2008 AAG conference in Boston, at which Bunge and I were both rumored to be in attendance.

Maps

But what the hell do I really care about?– maps. Here’s a gallery of works of his I’ve scanned from books and articles or found online.

From Professor Krygier’s Making Maps blog: “Redrawn from William Bunge, The Continents and Islands of Mankind. Areas in black have more than 30 people per square mile. Reproduced from Making Maps, p. 160-161″

Fitzgerald and the Detroit Geographical Expedition

Bunge’s Fitzgerald: Geography of a Revolution and the output of the Detroit Geographic Expedition (see bio above) included some of Bunge’s most experimental and powerful maps of Detroit.

A simple map I scanned from Fitzgerald

The above three maps were published last December in what I can only assume is [translated here] an excellent post on Bunge’s work in Detroit: William Bunge, le géographe révolutionnaire de Detroit.

Another example of Bunge’s cartographic experimentation: a rectangular cartogram when the form was quite rare. An image I clipped from the DGEI’s “A report to the parents of Detroit on school decentralization” [pdf]“

This map is from Fitzgerald, but I snagged it from a post on Krygier’s blog

Another map from the DGEI, this time scanned from an ESRI proceedings paper. I can’t make out all the map symbols, but some are dogs, cats, rubbish, gym equipment, bicycles, telephone poles, and broken bottles.

Nuclear War Atlas

Bunge’s Nuclear War Atlas is perhaps his most important cartographic work. Indeed, it is his only atlas (his Atlas of Love and Hate was never realized, not by Bunge anyway). The mostly duotone (red and black) maps contained therein display more cartographic experimentation than any of his previous works. There’s also some pretty weird shit. Bunge notes in the preface:

The color red is used throughout the atlas on the maps to depict death, and green life. This atlas is awash in red as our planet might soon be in death. Hopefully, at last my fellow revolutionaries will show some keen interest in conducting revolution without annihilation.

I had to search quite a bit to find any maps containing green (life).

Here’s a sampling of nine maps I scanned from the atlas, complete with original captions. Bunge’s work contains many redesigns of maps previously included in Fitzgerald or DGEI publications (for example, the “Region of rat-bitten babies” map below).

Someone helpfully pointed out to me that (as the source indicates) the above is actually reprinted by Bunge from Ronald Horvath’s (1974) article “Machine Space”

Influence

Bunge’s concept of the Expedition was very influential in geography in the 1970s. Unfortunately, the influence didn’t last long and there have only been minor resurgences since. Still, a small group of radical geographers — particularly those associated with the Antipode journal — have carried on the revolutionary spirit of Bunge’s geography.

Bunge is certainly an influence on academic John Krygier and anti-academic Denis Wood. The former has mentioned Bunge a few times on his blog and his definitional work on maps as propositions (see the sweet Krygier/Wood comic, Ce n’est pas le monde [pdf]) is inspired by Bunge’s advocacy maps of the 1960s. Wood too was influenced by the Detroit Geographical Expedition; his as yet uncompleted Narrative Atlas of Boylan Heights is directly influenced by Bunge in terms of scale, subject matter, and cartographic technique.

I am personally not as convinced as Bunge as to the state’s ability to solve the very real problems about which he writes and maps; and I just can’t get down with his more recent Stalinist leanings. I admire William Bunge primarily as a social cartographer willing to experiment with symbology and as a geographer willing to stand up for his political principles. Ultimately the latter led to him having no place in American academe. This is unfortunate, for his experimental cartography and radical geography writings could have had a much greater impact on critical cartography, public participation GIS (PPGIS), and human geography.

Further reading

By Bunge

Books

• Theoretical Geography. Lund, Sweden: Gleerup. (1962)
• Fitzgerald: Geography of a Revolution. Cambridge, Mass.: Schenkman Pub. Co. (1971)
• Nuclear War Atlas. New York, NY: Basil Blackwell Inc. (1988)

Journal articles and chapters

Bunge certainly shouldn’t be judged by his peer-reviewed scholarly output; in many ways he was an anti-academic who preferred action to being published. Nonetheless, here are all the published Bunge articles or chapters I could find. Links provided are occasionally to full-text content, but most are to references on JStor, OpenLibrary, and similar sources. I throw in a quote or an abstract here and there.

• (with Reid A. Bryson) “Ice on Wisconsin Lakes” (1956) Published by University of Wisconsin Dept. of Meteorology
• “Patterns of Location” [pdf] (1964) Discussion paper of the Michigan Inter-University Community of Mathematical Geographers, no. 3
• “Geographical Dialectics” (1964) Professional Geographer 16, pp. 28-29
• “Racism in Geography” (1965) The Crisis (NAACP magazine) October, pp. 494-497
• “Locations Are Not Unique” (1966) Annals of the Association of American Geographers 56, pp. 375-376
• “Gerrymandering, Geography, and Grouping” (1966) Geographical Review 56, pp. 256-263
• “Fred K. Schaefer and the Science of Geography” (1968) Harvard Papers in Theoretical Geography, Special Papers Series, Paper A
• “The First Years of the Detroit Geographical Expedition: A Personal Report” (1969) field notes, discussion paper no. 1, pp. 1-59
• “A report to the parents of Detroit on school decentralization” [pdf] (1970) Detroit Geographical Expedition and Institute
• “Geography of the Children of Detroit” (1971) field notes, discussion paper no. 3
• “The Geography” (1973) Professional Geographer 25, pp. 331-337
• “The Geography of Human Survival” (1973) Annals of the Association of American Geographers 63, pp. 275-295
• “Ethics and logic in geography” (1973) In R. J. Chorley (ed.), Directions in Geography, London: Methuen, pp. 317-31
• (with Robert Sack) “Spatial Prediction” (1973) Annals of the Association of American Geographers 63, pp. 566-569
• “Urban Nationalism: An Example from Canada” (1973) La Revue de Geographie deMontreal 27
• “Exploration in Guadeloupe: Region of the Future” (1973) Antipode 5, no. 2, pp. 67-73
• “Regions Are Sort of Unique” (1974) Area 6, no. 2, pp. 92-99
• (with Donald W. Fryer) “A Geographer’s Inhumanity to Man” (1974) Annals of the Association of American Geographers 64, no. 3, pp. 479-484
• “Practical Applications of Theoretical Geography” (1974) Published by the University of Iowa Dept. of Geography
• “Fitzgerald from a Distance” (1974) Annals of the Association of American Geographers 64, no. 3: pp. 485-489
• ( with R. Bordessa) “The Canadian alternative : survival, expeditions and urban change” (1975) Published by the Dept. of Geography, Atkinson College, Toronto
• “The Point of Reproduction: A Second Front” (1977) Antipode 9, no. 2, pp. 60-76
• (with John D. Nystuen) “The Philosophy of Maps” [pdf] (1978) Michigan Inter-University Community of Mathematical Geographers, Discussion Paper no. 12
• “Perspective on Theoretical Geography“ (1979) Annals of the Association of American Geographers 69, no. 1: pp. 196-174
• “The Cave of Coulibistrie” (1983; originally submitted 1978) Political Geography Quarterly 2, no. 1: pp. 57-70

  Geography and politics are intertwined in a study of a single small Caribbean settlement, Coulibistrie, on the windward island of Dominica. The sense of the place is provided through a sympathetic description of its rhythms, its people, their reactions to their poverty and their survival strategies. Political movements from Dreadism to Socialism are discussed in this specific context, and the position of women and children analysed. Political strategies are evaluated and the conclusion proposes a pro-children dialectical experiment of allying Marxism with the Catholic Church.

• “Geography is a field subject” (1983) Area 15, no. 3: pp. 209
• “Epilogue: Our Planet is Big Enough for Peace but Too Small for War” (1986) in R. J. Johnson and P.J. Taylor (ed.) A World in Crisis?: Geographical Perspectives, Oxford: Basil Blackwell
• “Where are the Germans?” (1998) Northstar Compass 7, no. 6
• “Author’s response: geography the innocent science – a completed geography
  awaiting its birth in print” (2001) Progress in Human Geography 25, no. 1, pp. 75-77

  So what is the state of geography today? It is in a mess – hyphenated, obfuscated, as confused as it is confusing. Why? Society is itself degenerating. The culture is coarse, vulgar, prostituted, chaotic, ‘dummied down’. We are in desperate need of intellectual reinforcements, and geography can help some.

Letters to the editor

• “A Geological Decision” (1962) Iowa Defender (Iowa City underground newspaper, 1959-1970), 5 January, 1, 3, 4
• “When to Organize” (1965) New York Times, 27 June, E11
• “Soviet Consumption” (1965) New York Times, 18 August, pp. 34

By others

Bunge has been covered in many divergent sources — below are just a few.

Articles

• Ronald J. Horvath (1971) “The ‘Detroit Geographical Expedition and Institute’ Experience”. Antipode 3, 1, 1
• Richard Peet (1977) “The development of the radical geography in the United States” in R. Peet (dir.) Radical Geography, alternatives viewpoints on contemporary social issues. Chicago: Maaroufa Press
• A. Merrifield (1995) “Situated Knowledge through Exploration: Reflections on Bunge’s ‘Geographical Expeditions’”. Antipode 27, no. 1, pp. 49-70
• K.R. Cox (2001) “Classics in human geography revisited: Bunge, W., Theoretical Geography. Commentary 1.” Progress in Human Geography 25, no. 1, pp. 71-73
• M.F. Goodchild (2008) “William Bunge’s Theoretical Geography” [pdf]. In P. Hubbard, R. Kitchin, and G. Valentine, editors, Key Texts in Human Geography. Los Angeles: SAGE, pp. 9–16. [450]

Blog posts

• Cyber Badger Research Blog (by Martin Dodge): “William Bunge’s Nuclear War Atlas“ (October 2006)
• New Maps of Ulster: “William Bunge” (June 2008)
• John Krygier’s Making Maps blog: “Making Advocacy & Humanitarian Maps” (June 2009)
• Le Monde diplomatique: “William Bunge, le géographe révolutionnaire de Detroit“ (December 2009)

For a while now, I’ve wanted to build geographic topology in Flash. Topology, as described in an ESRI white paper, is

a set of rules and behaviors that model how points, lines, and polygons share geometry. For example, adjacent features, such as two counties, will share a common edge.

For the applications I’ve had in mind, polygonal or areal topology is all that’s required: I need to know which features share a common border with which features. As a bonus, it’d be nice to know how long a border they share (how contiguous are they?).

Described further down is a raster method to determine vector feature adjacency. First, though, I’ll cover why such information must be built in the first place and how it is useful for certain cartographic applications.

Application to cartography and visualization

Topology needs to be built because it is not encoded in the most popular vector GIS formats (KML and the shapefile). In these formats, features (states or counties perhaps) are all stored separately; redundant points (like the shared corner of the “four corners” states) are repeated. There are topological GIS formats, like Arc/Info Export (e00), but geospatial data are rarely distributed in such formats.

One potential use for topological information is graph decomposition. Dasgupta et al write in Algorithms:

A wide range of problems can be expressed with clarity and precision in the concise pictorial language of graphs. For instance, consider the task of coloring a political map. What is the minimum number of colors needed, with the obvious restriction that neighboring countries should have different colors? One of the difficulties in attacking this problem is that the map itself, even a stripped-down version like Figure 3.1(a), is usually cluttered with irrelevant information: intricate boundaries, border posts where three or more countries meet, open seas, and meandering rivers. Such distractions are absent from the mathematical object of Figure 3.1(b), a graph with one vertex for each country (1 is Brazil, 11 is Argentina) and edges between neighbors. It contains exactly the information needed for coloring, and nothing more.

The simple graph above is two steps away from a circular cartogram of the form popularized by Daniel Dorling. On such maps, 1) feature circles are sized according to some numeric attribute, and neighbors — instead of being connected by lines — are 2) iteratively moved together or apart so that adjacencies are maintained where possible while avoiding circle overlap. The circular cartogram below is a snapshot of a NY Times interactive app developed in part by Lee Byron.

In addition to knowing the neighbors of all features, Daniel Dorling’s original algorithm requires as input the shared border lengths of all contiguous features. These are used in the force-repulsion algorithm — while attempting to maintain all feature adjacencies, Dorling’s algorithm applies extra attraction forces to features that share relatively longer borders.

In cartography, topology has much value beyond that described above. Generalization immediately comes to mind; to generalize/simplify/smooth the outline of a polygonal feature, one must also consider the feature’s neighbors. If the cartographer fails to do so, gaps may be created where features previously neatly adjoined. That noted, I should say that the method described here — while quite accurate in evaluating adjacencies — can only determine adjacency and relative (pixel-based) feature overlap, whereas polygonal shapefile generalization necessitates a true (and computationally intensive) topological indexing of each and every coordinate in the dataset. More on this later.

Method

Features stored in KML and shapefiles are defined by a series of latitude-longitude coordinates. The brute force method of determining if two features are topological neighbors would be to loop through their coordinates searching for an exact match. Not only would this be quite computationally intensive, but it wouldn’t detect true neighbors that were digitized independently; a shared border does not necessarily mean that the two features are defined using the exact same control points.

Of course it can be done. Most GIS software today will accurately build topology from a non-topological data source. The NY Times’ Matt Bloch detailed a server-side method in his Wisconsin (natch) Cartography MS thesis (as well as a 2006 AUTOCARTO proceedings paper), used in his MapShaper app, for essentially converting a polygonal shapefile to a non-redundant polyline shapefile. Though more efficient than simple brute force, these applications still rely on algorithms that require a large number of computations, and this scales up quickly as features get more complex. This is why topology is always built server-side.

The method I settled on is similar to pixel-based collision detection (also called “pro pixel collision” or “pixel-perfect collision”) familiar to game developers. In Flash, hitTests are only possible with specific points. To test for overlap of complex polygonal features, game developers have often relied on a pixel-level bitmap test for overlap. As described in an article by Troy Gilbert,

It’s pretty straightforward: you render two display objects to separate color channels, combine the color channels, then search the resulting image for any overlapping color.

I first encountered the technique in a post by Grant Skinner describing a method for shape-based collision detection in Flash published in 2005. GSkinner’s method doesn’t work as is in detecting adjacency in geographic features drawn from shapefiles. But with a few additions, the method is quite accurate and efficient in detecting feature neighbors in real-time. Try it out:

Some code

This isn’t really fully developed. It’s just something I’ve been thinking about for a while and wanted to get out there. Here though is a class I wrote to perform this pixel-based adjacency testing, as used in the above demo. It contains two chief public static methods:

• getNeighborsForFeature(): Returns a Vector of DisplayObject features determined to be neighbors of the passed-in feature (from a passed-in Vector of all features)
• checkFeatureAdjacency(): Checks the two passed-in DisplayObject features for adjacency. Returns the number of overlapping pixels.

How it works

To test for adjacency, features are drawn to a bitmap. The second feature is overlain using the difference blend mode. Any pixels shared by both features will now be lighter than before; I can check for such pixels using the BitmapData.threshold() method.

Adjacent features, though, will only overlap if they’ve been drawn with an external stroke. The two methods above accept a strokeWidth parameter. If this is less than 2 (features with external strokes of 1 sometimes fail to detect overlap accurately), I apply a precise GlowFilter to each raster. This increases accuracy but affects performance; though it’s not necessary, the methods perform best when features are initially drawn with a 2px stroke (as in the demo above).

Limitations

Accuracy

This method is quite accurate for most realistic geographies. But of course it’s all dependent on 1) the scale and complexity of your data and 2) the size of the BitmapData instance used for testing. One particular area of concern is features that share a corner but no actual border line segments. In some applications, these should be considered neighbors; in others, not. With a large enough BitmapData, such adjacencies will be detected.

If such features should not be considered neighbors in your application, a threshold may be set. checkFeatureAdjacency() returns the number of overlapping pixels; if this number is sufficiently low, features can be considered non-adjacent.

Performance

I haven’t done any real performance tests, but the method works fine, in a test with the 3000+ U.S. counties drawn from a large-scale shapefile in Firefox on my MacBook; neighbor detection was detected in real-time and nothing was cached.

As noted above, the methods perform fastest when features are initially drawn with a 2px stroke. If the methods are to be called repeatedly, it’s best to pass in shared BitmapData instances to be used for testing; the larger the BitmapData instance, the greater the accuracy, though this is offset by increased processing time for the BitmapData instance methods. No matter what you pass in, though, this raster-based method is far faster than any true coordinate-based adjacency testing algorithm (important b/c it is being performed client-side).

Generalization

This method simply doesn’t work for generalization (simplification or smoothing of feature border linework). Such processes require knowledge of all shared line segments, necessitating true coordinate-based topology building; my method only returns the number of overlapping pixels, which only correlates with actual shared border length (this, though, is sufficient information for the Dorling circular cartogram algorithm).

But I hope it’s useful for at least a few visualization applications. True topological indexing is best; but this computationally intensive process cannot yet be performed client-side in Flash. I’ll be working with these methods in the future, most likely in an implementation of Dorling’s circular cartograms in indiemapper.

The six symbologies are the classic thematic cartography representation methods: choropleth, proportional symbol, dot density, flow, isarithmic, and cartogram. Borden Dent’s great cartography textbook, in the section titled “Techniques of Quantitative Thematic Mapping”, dedicates a chapter to each of these symbologies, and to no others. The classic Robinson textbook, as well as the modern Slocum et al one, also dedicate more space to these six representation methods than to any others.

isoline

The first isarithmic (representation of continuous phenomena using lines of equal value) map is often ascribed to Edwin Halley, with his 1701 isogonic contour maps of magnetic declination.

Not so, as the symbology has been traced back to as early as 1584. Robinson notes in Early Thematic Mapping:

The contour had tentative beginnings as early as the end of the sixteenth centruly in the form of lines of equal depth on a map of the River Spaarne made in 1584 by the Dutch surveyor Pieter Bruinsz. The next use of the symbol came more than a hundred years later in 1697 by Pierre Ancellin…

You can’t see too much in the above — a terrible scan I made from Robinson’s Early Thematic Mapping. This map isn’t listed on Milestones, nor could I find any reproductions of Bruinsz’s map on the internet. What I did find was a detail image of unknown provenance on the interesting site, Dutch thematic maps on the web.

On the above you can clearly see the dashed bathymetric line of equal depth.

choropleth

Frenchman Charles Dupin’s (1826) Carte figurative de l’instruction populaire de la France is the first known choropleth map.

Dupin’s map was and is well known, and had a great impact on cartographers and statisticians at the time. Interestingly, the particular form of Dupin’s choropleth map — the unclassed variety, in which each unique value gets a unique shade — didn’t stick. The classed variety quickly took hold, with the first such map appearing in an 1828 Prussian atlas, Administrativ-Statistischer Atlas vom Preussischen Staatae.

Notice not the subtle lean in my scan (from Robinson since I couldn’t find it elsewhere), but the class breaks shown along the bottom. The author of the thematic portion of the atlas is unknown.

dot density

Dupin’s fellow countryman Frère de Montizon is responsible for the first dot density map, Carte philosophique figurant la population de la France (1830).

Unlike Dupin, who is fairly well known, Robinson describes Montizon as a “mystery man”. He continues,

It is one of the accidents of history that Frère de Montizon’s invention of the thematic dot map should have gone completely unnoticed. In terms of cartographic innovation it ranks with the isothermal map, yet as far as can be ascertained no reference to him or to his dot map was made by anyone well into the twentieth century…this basically simple, logical idea had to wait some thirty years to be reinvented and much longer than that to become generally known.

The symbology was “reinvented” by Thure Alexander von Mentzer in his 1859 map of population distribution in the Scandinavian peninsula. I can’t find any reproductions of this map anywhere.

proportional symbols

The first known example of a proportional symbol map appeared in the Atlas to Accompany Second Report of the Railway Commissioners, Ireland (1837). Though the atlas was apparently quite innovative with regard to thematic cartography, it went largely unnoticed due to limited distribution.

Here, Henry Drury Harness’s map of Irish population density from the atlas.

They’re a bit hard to make out in this horrible scan (again from Robinson — listed as part of his personal collection), but there they are: circles scaled according to population centered at various Irish cities. This wasn’t the first time that proportional circles had been used to convey data, but it was the first time it had been done on a map. Notice the shading?– this is also one of the first dasymetric maps.

flow maps

The following map is more well known, and could share the title of first proportional symbol map, as it appeared in the same 1837 atlas as the above map. Here, the innovation is the use of line thickness to convey a quantitative value, in this case the traffic between Irish cities (again by Henry Drury Harness).

cartogram

I’ve written about this before, so below’s a quick summary.

Many sources, including Dent’s text, point to Émile Levasseur as the originator of the value-by-area cartogram. Levasseur included diagrammatic maps like the following 1868 map of Europe in his economic geography textbooks.

The above map was reprinted by Funkhouser in 1937 and by Waldo Tobler in his Thirty Five Years of Computer Cartograms. In the latter, Tobler notes:

This is a map of the countries of Europe in which each country is represented by a square whose size is proportional to the area of the country, and with countries in their approximately correct position and adjacency. Could this be called an equal area map? Or is it an equal area cartogram?

I believe the former: that this should be considered a diagrammatic equal area map, but not a value-by-area cartogram. Sara Fabrikant makes the case for the German provenance of the first cartogram:

Another notable and early European contribution to the thematic mapper’s toolbox is the value-by-area cartogram. Hermann Haack and H. Wiechel published a cartogram depicting election results from the German Reichstag in 1903 (cited in Eckert 1925).

Haack and Wiechel’s map is indeed cited in Eckert’s famous Die Kartenwissenschaft volumes, but it isn’t reprinted there. Nor is it clear that unit areas on their election maps represented something other than land area. The earliest true value-by-area cartogram that I’ve seen reproduced came by way of Professor John Krygier, who reprinted William Bailey’s 1911 population cartogram.

I doubt the above is truly the first cartogram, and welcome any scans or references to true value-by-area cartograms that precede the 1911 map.

conclusion

Four of the six classic thematic cartography symbologies — choropleth, dot density, proportional symbol, and flow — originated between 1826 and 1837. Two of them — proportional symbol and flow — were initially produced by one man (Harness), and appeared in the same obscure railway atlas. All were refined in the 19th century, and only one (isolines) predate the century.

Conspicuously absent above are the names William Playfair and Joseph Minard. Indeed, some sources still cite these well-known engineer-statisticians as the originators of the proportional symbol and flow symbologies. Playfair may have been the first to use proportional (value-by-area) symbols (specifically circles) to represent quantitative data, but it wasn’t on a map.

Funkhouser credited Minard with the invention of the flow symbology, ignoring Harness’s innovation eight years earlier:

The second, called cartograms with bands (cartogramme a bandes), was originated simultaneously and independently by the French engineer, Minard, and the Belgian engineer, Belpaire, in 1845. This consists of colored bands or ribbons which follow the watercourses and railways on a map, the width of the band being proportional to the amount of traffic or number of passengers carried.

It’s certainly the case, though, that Playfair paved the way for much thematic mapping experimentation in the early 19th C. and that Minard helped popularize the flow, proportional symbol, and choropleth symbologies.

]]> http://indiemaps.com/blog/2009/11/the-first-thematic-maps/feed/ http://indiemaps.com/blog/2009/10/classed-cartograms/ http://indiemaps.com/blog/2009/10/classed-cartograms/#comments Sat, 17 Oct 2009 04:15:36 +0000 zach'ry http://indiemaps.com/blog/?p=97

Classification is commonplace in thematic cartography. In choropleth mapping, classification is the norm. This was not always so. The first choropleth map (created by Baron Charles Dupin in 1826) was unclassed.

According to Arthur Robinson, in his Early Thematic Mapping in the History of Cartography:

So far as we now know, the first choropleth map to provide a legend and give class limits to the tones was the 1828 Prussian manuscript map of population density. After the early 1830s most choropleth maps employed classes of various sorts, some based on rankings of the districts, some based on percentage departures from a mean, and some based on categorizing the data itself.

It seems that classification in choropleth mapping was adopted for technical rather than theoretical reasons. Robinson notes that “engravers were unsuccessful in making such [unclassed] maps, since they did not have full control over the darkness of very many flat tones.” Nonetheless, empirical research, begun in the late 1970s and continuing into the 1990s, backed up the decision, at least as regards the acquisition of specific information. Data on the recall of specific information and the acquisition of general information was less conclusive; a good summary of this research is provided in Slocum et al’s cartography textbook.

(classed histogram legend from my first thematic map, produced for Mark Harrower’s Geography 370: Introduction to Cartography)

Range-graded proportional symbols

In proportional symbol mapping, the unclassed form is the norm. This, too, seems to be a technical rather than theoretically-based trend. As noted in the Slocum text:

Classed, or range-graded, maps can be created by classing the data and letting a single symbol size represent a range of data values, but unclassed proportional symbol maps are more common. This might seem surprising given the frequency with which classed choropleth maps are used. The difference stems, in part, from the ease with which unclassed proportional symbol maps could be created in manual cartography (either an ink compass or a circle template could be used to draw circles of numerous sizes).

The logical extension of classed choropleth mapping to proportional symbol mapping was first suggested by Hans Joachim Meihoefer in the late 1960s. Here, too, classification is purported to make the thematic map easier to comprehend; also from Slocum:

Range grading is considered advantageous because readers can easily discriminate symbol sizes and thus readily match map and legend symbols; another advantage is that the contrast in circle sizes might enhance the map pattern, in a fashion similar to the use of an arbitrary exponent…

This comes at the expense of precision, as map reader’s can never get exact values for enumeration units. Borden Dent notes, “In this scaling method, symbol-size discrimination is the design goal, rather than magnitude estimation.”

Rarely, though, is exact value retrieval (magnitude estimation) the goal of thematic cartography, at least of the static variety. Further, map readers are notoriously bad size estimators, as an array of psychophysical studies in and outside of academic cartography have established. The graph below (from John Krygier’s excellent blog post on perceptual scaling) sums up the average response to 1, 2, and 3D proportional symbols.

The above suggests that subjects uniformly underestimate the areas of proportional symbols. Perceptual scaling has been developed in response, in which symbol sizes are perceptually scaled according to a power function, rather than being scaled mathematically.

Perhaps more important, though, are the deviations among subjects hidden in the graph above. T.L.C. Griffin, in a 1985 study, found an average underestimation similar to previous studies, but stressed that “perceptual rescaling was shown to be inadequate to correct the estimates of poor judges, while seriously impairing the results of those who were more consistent.”

Both the Slocum et al and Dent cartography textbooks introduce range-grading of proportional symbols as a potential solution to the 1) average poor estimation of symbol size and 2) high deviation in this underestimation.

Cartograms then

This makes a case for classification in proportional symbol mapping. It also makes the case for classification on cartograms. Cartograms can be considered a variant of proportional symbol map in which the symbol shape used is that of the original enumeration unit in some projection. But in my cartogram research for the M.S. Cartography degree at the University of Wisconsin, I ran across no references to classifying the area of cartogram units. A more recent search also revealed no references. Classification seems particularly appropriate to cartograms because the limited research done on their perception suggests that users estimate cartogram feature area much less accurately than the simplified shapes of standard proportional symbol maps.

The above is a normal, unclassed cartogram showing U.S. population. Notice the continuous range of state areas, shown below in order of population (but smaller).

Despite the two legend chips, I think it’s quite difficult to estimate the population of states with any degree of precision. The following classed cartogram abandons precision in favor of a more easily and quickly interpreted map.

Exact values can never be retrieved, but each state can quickly be matched with one of four classes (grouped according to Jenks natural breaks classification).

The above maps were created with a beta version of indiemapper. No other mapping software allows the creation of classed cartograms. You can get around this when using tools such as ScapeToad or Frank Hardisty’s Cartogram Generator (both of which utilize the contiguous Gastner-Newman cartogram algorithm) by pre-classing your data, so that only 3-7 unique values are found in the dataset for a given attribute.

In addition to 1) increased discriminability of symbol sizes and 2) a potentially enhanced spatial pattern, classed cartograms may hold a third advantage over the unclassed variety, though only in bivariate mapping. Cartograms, though, are most typically and appropriately employed in bivariate mapping. When constructing a bivariate cartogram, the cartographer sizes enumeration units according to one variable (typically population) and colors the units, choropleth-style, according to some other variable (often election results). In unclassed cartograms, some features may shrink down so as to be nearly invisible, making the reading of the second (coloring) attribute impossible. In the classed variant, a small but still legible minimum size can be established for the first class of data, ensuring that the coloring attribute can be interpreted on all units. A minimum size can be established on an unclassed cartogram, but if mathematical/proportional scaling is employed it may result in absurdly large features at the higher end of the mapped variable.

Of course, classed cartograms won’t always have a larger minimum size; this must be a conscious design decision (in the U.S. cartograms above, the classed cartogram minimum size is smaller than the minimum size found on the unclassed version).

Inappropriate

Classification in cartogram scaling is not always appropriate. Indeed, the typical unclassed form is preferable in many cases. Borden Dent writes the following of classification and choropleth mapping; I believe it’s equally applicable to the proportional and cartogram symbologies:

The purpose of the choropleth map dictates its form. If the map’s main purpose is to simplify a complex geographical distribution for the purpose of imparting a particular message, theme, or concept, the conventional [classed] choropleth technique should be followed. On the other hand, if the goal is to provide an inventory for the reader in a form that the reader must simplify and generalize, then the unclassed form should be chosen.

This sounds a lot like the modern distinction between cartography and geovisualization, or like the older one between communication and represenation. These distinctions have been overblown; experts and amateurs are often looking at the same map. But there are certainly cases where the purpose and audience are more clear-cut. In such cases, classed cartograms may be an option.

While providing the cartographer with the “ability to direct the message of the communication” (Dent), this power comes with great responsibility — specifically the responsible choice of the 1) number of classes, 2) classification method, and 3) appropriate and easily discriminable symbol sizes for each class.

Research on classification in choropleth and proportional symbol mapping has settled on three and seven as the minimum and maximum number of classes that are effective. Classification methods have been much studied in academic cartography, statistics, and other fields. Jenks natural breaks classification is often employed; equal intervals and quantiles are also typical. Cartographers have less guidance on the third choice, that of an appropriate symbol size for each class.

The above shows a set of ten class sizes for range grading developed by Hans Joachim Meihoefer as the result of a visual experiment; the symbol sizes were developed for maximum discriminability, while maintaining a realistic size range (none too small, none too large). Ten classes weren’t recommended, but supposedly any five contiguous sizes could be chosen. Indiemapper uses a slight variation of Meihoefer’s sizing scheme for classed proportional symbol and cartogram sizing.

Despite the additional responsibilities placed on the cartographer, I believe a carefully-crafted classed cartogram can be a very effective representation of some datasets for certain audiences.

Here the same, as displayed by MLB’s Gameday, for which the initial pixel coordinates were collected.

The conversion from the pixel-based coordinates used on the above to a geographic latitude-longitude space took a fair amount of work. More on that in a bit. So why even attempt it? What is the value added by presenting these data on a satellite photo?

At first, it was just to see if I could do it. From Wal-Marts to crimes to tweets, the slippy map has become the de facto platform for geovisualization. Hits — balls in play — are inherently spatial, though at a micro level that gets rare consideration from neocartographers. The conversion to geographic points would also let me take advantage of the various symbologies written for mapping APIs, from heatmaps to clusterers.

As I continued work, though, I became more interested in the issue of scale: the images produced reveal the scale of baseball, easily compared to the scale of urban life. For example, most MLB ballparks have fences as far as 400 feet from home plate. In New York City, the average N-S block is 264 feet long. Such a block may contain dozens of businesses or residences, all of which we’d expect Google Maps and others to accurately geolocate. So why not hits?

Here a Jake Fox homer at the intersection of Kenmore and Waveland outside Wrigley Field.

And here, a recent Washington Nationals game, with hits placed as though home plate was located at the center of Dupont Circle.

To get an idea for how field dimensions affect the game, we can display hits from a particular stadium as if they occurred at any other. Here a recent Pirates-Phillies game that took place at the relatively small Citizens Bank Park. In the second image, I’ve placed and oriented hits as though they took place at the Pirates’ PNC Park. As you can see, two, maybe three of the home runs would have been swallowed up by the visiting team’s more cavernous outfield.

What follows is a description of how I got the data, transformed it to lat-long coordinates, and displayed it on a slippy map. For the demo proof of concept application, go here.

Getting the data

MLB’s PITCHf/x system provides highly accurate real-time data on pitch speed, break, and location, all measured using high-speed video cameras. The data — freely available from MLB Gameday — are used in real-time by a number of media outlets and after the fact by many number-crunching sabermetricians. A similar system for batted balls does not exist, though is apparently in the works. Fairly accurate hit location data are available from the for-profit firms Baseball Info Solutions and STATS Inc, and data on home run distance/location is disseminated freely by HitTracker. But the only free source of locational information on all balls in play is the observational data collected by MLB Gameday for non-analytic purposes.

These data are purely observational, meaning hits are plotted by a MLB employee watching each game. For more on the accuracy of such data, see Is Seeing Believing? at The Hardball Times. For each ball in play, then, we have an X-Y coordinate. This coordinate is in an arbitrary pixel space and must be transformed quite a bit; more on that later.

Gameday’s XML data

MLB Gameday provides both the PITCHf/x and observational hit location data in online directories, starting here, that are organized chronologically and then by game. No API is provided, though, for querying this massive database of millions of pitches and hits — just directories and directories of XML files. Each game is described by dozens of XML files; the root game URL is accessed thusly:

http://gd2.mlb.com/components/game/mlb/ + year_{year}/month_{month}/day_{day}/ + gid_{year}_{month}_{day}_{away_team}mlb_{home_team}mlb_{game_number}/

Ex. gid_2009_07_21_balmlb_nyamlb_1, gid_2009_04_05_atlmlb_phimlb_1

In Baseball Hacks, Joseph Adler provides Perl scripts for traversing these directories, parsing the various XML files found in them, and storing the parsed data in a MySQL database. Mike Fast has updated the scripts, which can be used pretty quickly to create a pitch and hit database with this structure. I’ve done so, and the advantages of such a database are many, but the speed of queries is perhaps the greatest. For a small webapp, though, I didn’t want to go to the hassle of talking to a database, nor did I want the server-side dependencies. The Yahoo Query Language (YQL) provides a great solution in such cases, and allowed me to create an API where none existed before and use SQL-like syntax to query it.

My YQL tables

YQL lets you use a universal SQL-like syntax on any API or web data source. Since you can use subselects, YQL also lets you join disparate data sources. Typically it is used as a wrapper for APIs, so that developers may use the same syntax across many different data sources. They do so by writing a XML file that describes a YQL table. In a basic table, data are returned from the the API unmodified. Since I’ve no API to call, I rely on YQL’s execute element to run server-side Javascript for each query. With just a dozen or so lines of Javascript in the execute element of each table, I can quite easily return usable data from MLB’s directories of XML files.

I can also use the execute element to insert records into my own database (which can be MySQL or otherwise), allowing me to keep my database up-to-date more easily than with the Adler/Fast Perl update script. I should note, though, that the YQL queries are nowhere near as fast as querying a local database, and likely aren’t quick enough for actual production (though they’re great for the kind of experimentation and rapid prototyping described here).

I initially set out to recreate Mike Fast’s database environment with YQL tables: seven tables, one each for games, game types, pitches, pitch types, players, and umpires. For this exercise, I needed only the games and atbats tables. The latter includes the hit location information if a ball is sent into play. For both, I’ve added fields, grabbing more data from the Gameday XML files as needed. I also needed a stadiums table, though this draws from data I’ve personally collected, rather than the Gameday XML files. The three tables are hosted on github and available for use by anyone. If you’re logged into Yahoo, you can try em out right now by loading up the YQL console with the mlb.gd2.env environment file. You can then use SQL syntax on these tables. A few example queries (links are to REST queries that can be called from any web app):

• SELECT * FROM atbats WHERE gid=”gid_2009_04_11_nyamlb_kcamlb_1″ AND hit_x<>“NaN”
  
• SELECT stadium_id FROM games WHERE year=”2009″ AND month=”07″ AND day=”23″
• SELECT * FROM stadiums WHERE id=”3″

Transforming the data

Pixels to feet

The hit location data recorded by MLB is reported in pixels. The employees watching the games plot locations on a 250×250 image of the ballpark; the (0,0) origin is — not home plate — but simply the upper left corner of the image. The first step in making these data usable is to convert from this arbitrary pixel coordinate system to one in feet or meters. We need to know 1) the scale of the 250×250 image (feet per pixel) and 2) a known point on the field (preferably home plate) in the original pixel coordinates.

Initially, I assumed that these numbers would be the same or similar for all MLB ballparks. An article by Peter Jensen disabused me of this notion. Jensen undertook to determine the home plate X-Y location and distance multiplier for each MLB ballpark by assuming a uniform hit ball distribution and imposing physical constraints on hit balls. Unfortunately, when I plotted hits using Jensen’s numbers, the ball distribution diverged quite obviously from the Gameday-plotted version: outfield balls were plotted too far from home plate, infield balls too close. Jensen’s study used 2007-2008 hit locations, and MLB changes their images from season-to-season, so this may explain the divergence. Either way, I needed a new method.

I devised this simpler but less systematic approach. To determine the distance multiplier, I took a look at the stadium images provided by MLB Gameday. The stadium diagrams shown on Gameday’s real-time app are all 250×250 in expanded mode. Though I’m not positive that these are the exact diagrams upon which hits are initially plotted, they likely are as the resultant distance multipliers are quite accurate. Given a known distance (typically the straightaway center field fence distance), I can calculate the scale of the image in feet per pixel.

Determining home plate locations in Gameday’s coordinate system was less straightforward. In Baseball Hacks, Joseph Adler creates spray charts of the field using Gameday data. Unlike Jensen, Adler simply uses (125, 210) as the home plate location in pixels. I figured I could use this as a starting point, but to my surprise I found that I only needed to modify this origin for a half dozen stadiums. Of course, the only accuracy check I have is eyeballing back-and-forth between my rendering and the Gameday version, but given the observational nature of the data, I believe this is good enough.

Stadium locations and orientations

Using Google Earth, I recorded home plate latitude-longitude coordinates and eyeballed stadium orientations for all open MLB ballparks. Two stadiums are domes, so they’re out.

And three of the league’s six retractable-roofed stadiums were closed during their satellite shots.

LandShark Stadium was being used as a football field, and Citi Field hadn’t been built yet in Google’s satellite imagery, so home plate locations and stadium orientations weren’t collected for these.

For the other 23 stadiums, home plate latitude-longitude and X-Y coordinates, distance multipliers, and orientations are available in my YQL stadiums table.

Feet to latitude/longitude

Given the above, I can convert all pixel locations to feet with home plate as the origin. For stadiums oriented away from due north, these feet-based locations can be rotated about their home plate origins. The final step — converting from feet to geographic coordinates — is fairly simple, though I initially tried a couple of more complicated methods. First, I converted hit locations to meters, converted the home plate lat-long to UTM coordinates, added the hit coordinates to the latter, and converted back to lat-long. This worked fine, as did the second method I tried: a formula that calculates the hit’s geographic coordinate given the distance and bearing from home plate.

Given the huge scale and low accuracy required, I can utilize a much simpler method. Most mapping APIs include a distance method of some kind, taking as input two lat-long coordinates and returning the great circle (spherical, so perhaps off by as much as .3%) distance in meters or km. Here’s Yahoo’s, Google’s and Mapstraction’s. With this method, I can determine an approximate conversion factor between meters and degrees latitude-longitude:

Accuracy

A game was taking place when the satellite image of AT&T Park was taken; you can see the fans, players, and — importantly — the bases. Using the formulas above, I’ve placed white squares on the map where the bases should be, and this shows the typical accuracy achieved.

I’ve also plotted known outfield fence coordinates with similar accuracy.

Putting it all together

I created a demo application using Mapstraction, jQuery, and YUI. It’s very much a proof-of-concept, and the YQL queries can take quite a while to populate the map and table. Nonetheless, this is the only non-trivial web development I’ve done outside of Flash/Flex, and I’m quite impressed by these tools.

The app lets you visualize the hits in any individual game that took place in an open stadium in 2009. It’ll show games from earlier years, but I can’t vouch for the accuracy of the plotted points due to changes in Gameday’s observational data collection. By default, the app shows this year’s all-star game at Busch Stadium. To see another game, add a gid URL parameter. Here, for example, a few games from last night.

• the perfect game: gid_2009_07_23_tbamlb_chamlb_1
• gid_2009_07_23_sfnmlb_atlmlb_1
• gid_2009_07_23_seamlb_detmlb_1

The display is pretty basic: just pink dots for outs and blue ones for hits. And if you want to do any of the crazy stuff with plotting hits at different ballparks or arbitrary locations, you’ll have to dig around a bit. I can imagine many aggregation, filtering, and visualization options for these data. None are explored here. But all the code and data’s up on github if you want to have a go at it.

update Aug 08 2009: My proof-of-concept app was failing for a few days. Should be back now. My MLB Gameday YQL tables weren’t working as loaded from github, so I copied em over to indiemaps. Project’s still hosted on github. Any query links below utilizing the tables have been changed.

I first saw this map browsing technique in a minimal browser Matt Bloch created for an older static project.

I implemented a lens tool in the final project version of the World Freedom Atlas. I also experimented in some of the early prototypes with a continuous fisheye effect for map browsing. The latter never really took off because of the distortion and pixelation inherent in the raster method.

And the same idea in a Google Maps mashup.

The originator of the fisheye/magnification method for multi-scale mapping is probably Edgar Kant, in a 1957 map he produced for a migration study of Asby, Sweden, by Torsten Hägerstrand. Here the “distance from the centre shrinks proportionally to the logarithm of the real distance.”

Much work proceeded on multi-scale map projections, with the touchstone article being Snyder’s 1987 “‘Magnifying-glass’ azimuthal map projections”. Good coverage on such projections (including parallels to cartogram distortion) can be found in Canter’s Small-scale Map Projection Design.

In non-mapping UIs, the magnification/fisheye effect is fairly common; the Mac dock does it and even Cover Flow can be considered somewhat of a variant. For browsing and selection from a “large linear list”, Ben Bederson at HCIL came up with fisheye menus.

So nothing new in general UI terms, but still pretty novel and perhaps especially applicable to online map browsing.

Map browsing

Axis Maps cartographer Andy Woodruff did a great post on a variety of map panning and zooming methods. The lens or magnifying glass tool performs both panning and zooming functions, and should be considered as an alternative to the nine methods outlined there.

In interactive applications, the approach’s major strengths are threefold:

• low mouse mileage for panning and making selections
• less disorientation or “getting lost” as general cues are always available
• the ability to see generals and specifics simultaneously

The last is particularly important in cartographic applications where the success of a good thematic map is often seen as its ability to present overall trends and specific values in the same map.

Semantic zoom lens tool

The Cartifact example above is particularly interesting because of the semantic zoom inherent in its lens tool. In normal, geometric zooming, a map (or other image) is simply blown up; more detail is shown by definition, because more pixels are dedicated to the image. In semantic zooming, different (typically more detailed) larger-scale renderings are shown at higher zoom levels; not only are features larger, but more details (and labels) are shown. Such semantic zooming is standard in slippy maps, which are produced and tiled at predefined zoom levels. Nonetheless, the application to a lens tool is noteworthy, especially in thematic cartography; generals and specifics can be presented simultaneously, and both can be tailored semantically to different zoom levels.

Here’s a quick example I threw together in Flex using Modest Maps (right-click to view source).

I like inverting the above, or perhaps more interestingly, showing Microsoft Aerial as the base and Microsoft Hybrid as the lens; the spotlight (zoomed in or otherwise) then serves to provide political/cultural details for the moused-over region.

Application to thematic cartography

In online thematic cartography, the practice of showing smaller enumeration units at higher zoom levels is somewhat common. The NY Times has done it a few times, including their Election 2008 results maps (the county-level choropleth is revealed by zooming in).

The same idea can be applied to a lens tool. Here the lens reveals the county vote results, and can be zoomed (again, a quick Flex job) to further investigate the local-level results. Click to launch the map (it’ll take a few secs to load, project, and draw the data).

The above is based on some projections and choropleth code I released last year. I think there’s more room for experimentation here: the size of the lens could be user-modifiable and semi-transparent (so you can still see where you’re mousing over the main map); I’d also like to create a less-pixellated fisheye lens and try out multiple lenses/fisheyes (for detailed comparisons of multiple areas while still providing context).

]]> http://indiemaps.com/blog/2009/04/lens-tools-and-fisheye-map-browsing/feed/ http://indiemaps.com/blog/2009/02/e00parser-an-actionscript-3-parser-for-the-arcinfo-export-topological-gis-format/ http://indiemaps.com/blog/2009/02/e00parser-an-actionscript-3-parser-for-the-arcinfo-export-topological-gis-format/#comments Sun, 22 Feb 2009 01:49:18 +0000 zach'ry http://indiemaps.com/blog/?p=83 First off, why mess with such a retro format as Arc/Info Export (.e00)?– any code written for this ASCII file type in the last few years has been on how to go from e00 to pretty much anything (especially to the non-topological data format, the shapefile).

Put simply, topological information makes a lot of things possible for the intrepid ActionScripter.

E00 files non-redundantly store all nodes, lines, and polygons that make up a geographic data layer. This geodata format is one of three currently distributed by the Census Bureau for boundary files (the others are the shapefile and the Ungenerate ASCII format). The GIS formats used in most web mapping applications (I’m thinking of shapefiles, GeoJSON, and KML) are non-topological, meaning features are stored independently, and topological information on shared borders and the like is quite difficult to extract. Like seriously hard. Something you don’t want to be doing in the browser. Matthew Bloch, of the NY Times, did his cartography master’s thesis (at Wisconsin, natch) on MapShaper, much of which involved a C++ server-side solution for building topology from a polygonal shapefile. Generalization requires non-redundant polylines so as not to create gaps between features when smoothing. Other visualization techniques, including cartogram construction and graph decomposition, also require knowing the shared borders of geographic features.

Ideally, such topological information could be created/extracted for any geography, regardless of the datasource. In reality, topology building is intensive and best suited to server-side processing. Using E00 files and my E00Parser lets you experiment with the visualization and cartographic techniques only possible when such topological information is known, without the expensive processing necessary to build it.

The code

I’ve gotten a ton of use out of Edwin van Rijkom’s SHP library. My noncontiguous cartogram, isolining, and political choropleth experiments relied on the code to load coordinate data in shapefile form at run-time, as did the early experiments that led to indiemapper. I’m hoping I’ll get just as much use out of this parser, for when adjacency information is critical to the visualization technique.

There are two main classes, E00Parser and E00Tools. E00Parser is based on the Perl extension Geo::E00 by Alessandro Zummo and Bert Tijhuis, with much aid from the (world famous) Arc/Info Export Format Analysis. There’s no way I would have attempted to write the AS3 E00 parser without Zummo and Tijhuis’ code, as theirs appears to be the only stand-alone open source code available for reading the format. Their Perl regular expressions were copied with few modifications, though I did fix an issue in some that was keeping their code from accurately reading certain sections of double-precision coverages. I wrote E00Tools to collect a handful of methods for working with the resultant data.

I setup a Google Code project for this work, as topology will likely form the basis for a decent amount of my cartographic experimentation in the near future.

• to browse the code, just go here
• the latest zip distribution is available here
• three examples are included in com.indiemaps.mapping.data.parsers.e00.examples

Oh, BTW:

ESRI considers the export/import file format to be proprietary. As a consequence, the identified format can only constitute a “best guess” and must always be considered as tentative and subject to revision, as more is learned.

(from the Arc/Info Export Format Analysis)

How to use

After loading your ASCII E00 file into a string, use something like the following to parse it.

var data : Object = E00Parser.parse( e00Text );

The returned data object includes all information contained in the file, and can have as many as nine sections. Of most use are the arc (non-redundant list of polylines), pal (list of all polygons and their associated lines), and ifo (attributes and labels) sections. The exact structure of the returned object is described on the wiki here.

There are three sweet examples to be found in com.indiemaps.mapping.data.parsers.e00.examples.

Tools

E00Tools contains some methods for working with the resultant data of E00Parser.parse(). Included are methods for:

• Drawing all features
• Drawing individual features
• Getting a list of polygon IDs for all features
• Getting the centroid of a feature
• Getting the shared border length of all features and their neighbors

Key above is the idea of the feature. Michigan is a feature. Features are not directly encoded in E00 files like they are in other formats. In a polygonal shapefile, for example, each feature is encoded as a multipolygon, constituted of one or more rings of points. In E00 files, only polygons are directly encoded; feature information (which polygons make up which features) can be ascertained from the INFO (ifo) section.

Experimentation

I created these AS3 classes for myself, because I wanted to experiment with topological geodata in visualization and cartographic applications. This typically boils down to knowing which features are neighbors and how much of a border they share. The E00Tools methods getAllFeatureNeighbors and getAllFeatureSharedBorderLengths gives you easy access to this information.

Daniel Dorling popularized the circular cartogram form among academic cartographers, outlining the symbology most notably in his 1991 PhD thesis and 1996’s Area Cartograms: Their Use and Creation (available here in PDF form along with many other gems of quantitative geography). Dr. Dorling made Pascal and C code available. I ported it to Python, and began experimenting, mostly in vain, on a method that worked with a shapefile as input, but without the expense of building topology. It produced at best a pale imitation. Dorling describes the gravity model used to produce the cartograms in his dissertation:

The algorithm which was developed to create the area cartograms worked by repeatedly applying a series of forces to the circles representing the places. Circles attract those they are topologically adjacent to; the strength of this attraction being greater the larger the distance is between them and the longer their common boundary.

The algorithm thus requires the shared border lengths of all features and their neighbors. Producing this info is easy with E00Tools, but it seems kind of backward to parse my geodata in ActionScript only to produce the rendering in Python. I’m working on porting Dorling’s algorithm to AS3 so I can go directly from geodata to cartogram without switching platforms.

Lee Byron mentions another technique, used to generate the Olympic Medal Count cartograms he helped produce for the Times. Byron didn’t release any code, but notes that a soft body force directed layout algorithm written in ActionScript was used. I haven’t been able to reproduce his method, but I’ve included an example that drops the topological information gathered from an E00 file into a Flare visualization using a force directed layout. The example is minimal, but shows how the E00 classes can be integrated with the Flare visualization API, and may point the way to a slightly different method for producing circular cartograms client-side.

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