Vague Terrain 13: citySCENE has just launched. As editor Greg J. Smith writes:
This issue of Vague Terrain is founded on two notions - that the city is a stage set for intervention and an engine for representation.The collection expands out from this premise in multiple directions: carto-mashups, projection-bombing, sound walks, psychogeographic imaging and ubicomp experiments. Early highlights for me included Crisis Fronts' Cognitive Maps and Database Urbanisms, which presents some impressive work on data visualisation and generative models as urban mapping strategies (below: Case Study: Los Angeles). Overall, on a first look, this collection is incredibly rich. It shows that a creative, wired-up, critical urbanism is not just a wisftul aspiration of the technorati, but a real practice.
Having said all that, it's a privelege to be a part of this collection. My contribution is Watching the Street (Navigator), a browsable visualisation of a single day of images from the Watching the Street dataset. It tests out the hunch that these time-lapse slit-scans can be used to read real patterns in the urban environment - that they are (or can be) more than just suggestive abstractions. It uses a simple interface to display both a single source frame, and a correlated slit-scan visualisation, with image-space and time-space sharing an axis, a bit like a slide rule. Greg Smith called it an "urban viewfinder", which sums the intention up nicely.
Playing with the navigator for a while seems to confirm that hunch. The composites reveal temporal patterns in the environment, but not the spatial context that allows us to identify their causes; the source frames show that spatial context, but not the change over time. Reading the two against each other involves chains and cycles of discovery, analysis and inference. These might be open-ended (spatiotemporal browsing) or more directed. What time do the sandwich-boards go out? How long does the delivery truck stay?
Building the navigator presented some interesting technical challenges: mainly, how to make a web-friendly interface to 1440 source frames (240 x 320) and 480 slit-scan composites (720 x 320). That adds up to about 75Mb of jpegs. Processing 1.0 came to the rescue, with its new built-in dynamic image loader. requestImage() pulls in an image from a given URL, on cue, without bringing the whole applet to a grinding halt; it provides some basic feedback on the state of that image - whether it's loading, loaded, or un-loadable. I also blundered into two other useful lessons: how to use the applet "base" parameter, and how to manage Java's local cache, which kept throwing up earlier versions of the applet during testing.
Having made a lean, mean, browser-friendly version, I'm now thinking of adapting the navigator into a full-screen, offline app, with the whole eight-day dataset, and perhaps some tools for annotation and intra-day comparison. Best of all would be a long term installation; a sort of urban space-time observatory, watching the street but also opening it up to ongoing interpretation. If you'd like it running in your foyer, let me know.
