mapping

Resolutions

Driving at 70 miles per hour, the landscape dissolves into colors and concepts. Impressions coalesce into ideas, particles into patterns. It may seem like a paradox, but the faster we go, the clearer we can see. At high speeds, we can tune out the noise and see the forest for the trees. Our focus is on the destination.

At 30 miles per hour, the landscape resolves into discrete objects. We can now make out individual trees, buildings, the faces of drivers and pedestrians. Below 15 miles per hour, finally, we start to perceive a sense of place. We can make out the details of the objects around us, including their textures and physical properties. Any destination seems far more distant now.

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City archetypes

Composite map of Boston (Kevin Lynch)

Composite map of Boston (Kevin Lynch. Source: bostonography.com)

When Kevin Lynch conducted the research for a project called The Perceptual Form of the City, providing much of the material for his seminal work The Image of the City, he asked study participants to draw their mental models of the cities they lived in. Lynch then created composite maps generated from multiple drawings, resulting an archetypal, aggregate mental map of the city. He was able to identify five shared characteristics of the mental image people form of their environments: paths, nodes, districts, edges, and landmarks. While the individual interpretation of these five elements may vary, they form the vocabulary of what he called the imageable city.

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Invisible Cities

Invisible Cities is now in beta. The project has evolved from a visualization of collective memory for Taipei to a platform with the capability of surfacing data from the Twitter and Flickr services for any geographic area. While initially we have kept the piece focused on New York City due to the density of available data, we are planning to extend it with other cities as the project progresses. For documentation, please refer to the project site, as well as our recent article in the Volume 3, Issue 1 of the Parsons Journal for Information Mapping (Invisible Cities: Representing Social Networks in an Urban Context).

Many thanks to my collaborator Liangjie Xia for his tireless dedication and remarkable talent, and also to Jason Hsu for the inspiring discourse in the early phases of this project.

City Memory Visualization update (2) / 2 comments

New York landscape populated with status updates

New York landscape populated with status updates

The visualization continues to take shape (see these earlier posts for context). We are now parsing live data from Twitter and image tiles from the Google Maps API for the surface mesh. The user interface remains the point of focus at this point in time, though we are beginning to look for data parsing solutions to help construct the semantic pathways between status updates. Below are a few images of the latest progress.

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City Memory Visualization update

Sketch of geolocated tweets, manipulating the mesh surface below

Sketch of geolocated tweets, manipulating the mesh surface below

Joining the project is Liangjie Xia, a programmer and media artist based in New York and a recent graduate from ITP.

The current project focus is on a data landscape comprised of geocoded social updates, forming narrative pathways according to themes (we are evaluating Twitter, Flickr and Foursquare as data sources). As they occur, updates add points to a basemap of Taipei, cumulatively changing the elevation of the landscape. Updates, represented as nodes, are selectable, and articulate the landscape based on other thematically related updates. Finally, we are exploring toggling between two views: the surface mesh outlined above, and a view exchanging the mesh for narrative pathways, represented by hairlines connecting nodes related by topic and time.

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elsewhere