Daniel Johnson’s model uses only non-athletic data to forecast Olympic medal counts: “per capita income, population, climate and political structure of the nations competing, along with the obvious advantage of hosting the Games.” His predictions caused some to-do in January and February, especially since they have been historically quite accurate; I intended to see whether they held their weight this time around.

I came across a blog post containing the following YouTube video and was intrigued by the idea of a city’s collective aural identity. This map was an experiment in mapping the soundscape of Vancouver, just at the moment when Canada scores the winning goal in the February 28th hockey game. Coloring and symbology were assigned to the waveform of the YouTube audio, with the frequency of the sound becoming the symbol itself. If I had more time, I would have liked to try to make this into an interactive graphic. Please listen to the video as you view the map.

The above map was inspired by Jörg Colberg’s American Pixels project. I was interested in reducing the detail of the country boundary to the extent that it was just an artifact of compression technology. I rasterized the country outline at a very low resolution, scaled up the raster, and colored in the darkest pixels with solid black squares. Each black and grayscale pixel received one letter of a monospace font. The complete sentence reads: “Brazil was to enjoy nearly a half century of internal peace and rapid material progress. Brazil won three internatio…”

I created a Photosynth of the Manabi & Sumi Hirasaki Family Garden at the Japanese American National Museum in downtown Los Angeles. The technical requirements to do so were substantial: I borrowed a friend’s DSLR camera, stuffed its 1 GB memory card full of pictures (this storage capacity even paltry by modern standards), and used my broadband Internet connection to upload them to the Microsoft website.

Thus, in choosing the object or locale to synth, the culture with access to technology is the one to determine what facets of other cultures are worthy of its gaze—creating critical power relationships. I might have chosen this garden courtyard, but why is it a subject more worthy of consideration than Skid Row? Alternately, for the American tourist who captures the photosynth of Stonehenge, what claim does he now have over the “experience” of Stonehenge that he takes away from those who consider it the treasure of their cultural heritage—as was, in fact, the case with the Australian Aborigines and Goodchild’s example of Uluru/Ayers Rock? From a legal aspect, what recompense do I owe the museum for the product that I now freely share with others?

The actual content of the synth is also of questionable objectivity. The camera-holder determines what to highlight in the image, revealing his own aesthetic (or social, cultural) sensibilities, and again, imposing his hegemonic gaze. The viewer may be frustrated or alienated by the inability in some places, and the ability elsewhere, to look at certain items more closely, even though the technology seems to promise the opportunity to “be there in person yourself.” Comparably, in a traditional map, projection and orientation are political tools that can be used to exaggerate the size and presumed strength of a country. A map centered on North America will by definition marginalize the countries at the edges of this point of reference.

Finally, much has already been made of the privacy concerns around Google Street View, perhaps the most popular instance of images as maps. Though volunteered geographic information seems to have the support of government right now, as Goodchild argues, at what point does it overstep its bounds? Consider the reporter on the ground in Haiti who creates a photosynth of an obliterated city block. If this technology shows us something that we as a society don’t want to see, at what point will we simply turn away?

New York Times Election Results 2008 Map (source):

My replica:

Objective 2: Critique the above map and offer three ways in which it can be improved.

My foremost issue with the map is that emphasis is placed on the number of electoral votes won by each candidate, but this information is not reflected in the map, either in the coloring or labeling of the states. One way to remedy this, supposing that the states shapefile is not altered or distorted (à la the “Purple America” map) and that we are strictly working with the state electoral results (versus county-level popular vote data), would be to weight the color of the state based on the number of electoral votes, e.g. darker blues and reds for states with more votes and lighter or less opaque blues/reds for those with fewer votes.

Since the map was probably interactively updating as the popular votes were tallied, it makes sense that there would be a scheme to show which states were “leading” blue or red. After the election, though, the legend should have been updated to remove this extraneous and possibly confusing information. Furthermore, what I noticed as I was extracting the color information from the map is that the legend’s particular swatches for the “win” states do not exactly match the blue and red colors used in the map. I would recommend revising those swatches, if only for consistency’s sake.

Finally, if I were to disregard the abovementioned conditions and revise the map altogether, I would use the state-level popular vote data to color the map on a blue-purple-red gradient. This would help to incorporate the information about which states had been leading in which direction, which was otherwise lost. (However, I still would not distort the shapes of the states, since doing so would seem to cause unnecessary confusion for the viewer.) With either the blue-purple-red or the less transparent/more transparent schemes I mentioned, the otherwise uniform “365/173″ bar at the top of the map could be divided into sections in proportion to the number of electoral votes for each state. If each section was then colored according to the chosen scheme, the bar itself would become more informative as well.

The Topography of Mars by the Mars Orbiter Laser Altimeter (source) combines the shaded relief of the Martian surface with the colorized raw elevation data from Mars Global Surveyor. This map communicates its purpose quickly and with clarity. White represents the highest elevations, a choice which might evoke snow-covered mountains to the casual viewer; conversely, the deep purple area is immediately understood to be an impact crater. A side effect of this color ramp is that the northern lowlands, hypothesized by scientists to have been once covered in ocean, are shown in the blue color typically associated with water. The map is both informational and aesthetically pleasing, though it seems to lack a conventional scale bar.

Though the title of this map is technically Human Geography: Great Plains (source), it seems too general and inaccurate a descriptor for the actual map, which shows Spanish exploration routes, Native American battles, the overlapping text “Great Plains U.S. Base Map,” etc. These data, moreover, are confusingly represented on the map. Irrelevant information is displayed: the states are unnecessarily colorized, and highways are shown in red rather than a less distinctive color. Only the states with battles have been labeled, but the selective labeling does not apply to cities or other landmarks (Trail of Tears). Finally, none of these style choices carries over to the Oklahoma inset.