On Orientations, Metrics, and Opportunity (Scale and GIS, Part 2)

In GIS projects, designers can also cater to broader social inequities, rather than solely the production of futures in terms of resource management. The RPA Access to Jobs platform, for instance, is a project based off OpenStreetMap which scales accessibility to employment based on different modes of transportation to which one has access. It also offers the option of visualizing jobs by both one’s desired industry and level of education.

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This information, in turn, comes from an eclectic range of sources; the project cites the U.S. Census’ Origin-Destination Employment Statistics, NJTPA Regional Transportation Model, NYCDOT, NY Waterway, and Nassau Inter-County Express (among various other organizations) for its material. With this, locations can be rescaled in relation to the desired information. The information, too, gets rescaled not just based on the filtering but also through comparing those different orientations with increased time spent on the platform.

Facet Decision Systems’ Opportunity Mapping further shows these distinctions at work. These opportunity maps use measures like “SAT score average, travel-time to work, and housing prices.” For the maps, the developers convert the results of such measures into Z scores so that they can be compared against each other and used toward forging aggregate scores. The filtering on this platform, then, becomes critical toward establishing a metric of opportunity out of scaling such measures into Z scores.

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The platform, then, entails an explicit use of location-aware platforms to investigate the dimensions of meaning-making in stratified contexts and in direct relation to orientation – or perhaps, put another way, meaning-marking. To do so, it rescales information in direct relation to location for comparative purposes, constructing a metric of opportunity within that process.

When thinking about these platforms together, it seems that the ordering logic of GIS more broadly inspires the formulation of specific methods and metrics for abstract issues. They enlist different kinds of actors and data sets and the particular ways these platforms scale these issues, as well as the ways they scale metrics for those issues, is incredibly meaningful and worth analysis.


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