Geographical imagination systems


Luke Bergmann and Nick Lally, in their article "For geographical imagination systems" (paywalled link, pdf via Nick Lally), state that

knowledge that was once entwined with particular knowers and communities and contexts is first alienated into separate data layers, then those layers are then reintegrated by GISystems according to common location.

(Bergmann and Lally, p. 8)

As a result, GIS are built around some absolute coordinate system to which all data must be referenced before we can analyze them or create visualizations. GIS in this paradigm are essentially UI wrappers around libraries like PROJ and GDAL that convert data from different coordinate systems and formats into a common reference frame. Information that doesn't fit neatly into a coordinate either needs to be forced into the GIS or discarded altogether.

Bergmann and Lally present a prototype "geographical imagination system" enfolding that challenges this paradigm by building visualizations of geographic information using non-Euclidean distance metrics that highlight relations between phenomena that coexist with traditional spatial relations. I think they are mostly interested addressing challenges that arise with GIS in human geography, but their articulation of these limitations of GIS illuminates some of the difficulties I have encountered in dealing with geospatial data in the geosciences. Earth observations are noisy, incomplete windows into the world. When we try to build and test models using these data, we need to be aware of how our inferences might be affected by the circumstances of their acquisition and processing. Our GIS generally don't make this easy for us. But they could.

One possibility that I have been thinking about recently is replacing the GIS notion of data layers with a probabilistic graphical model (PGM) in which data is represented by a collection of vertices that represent the different quantities of interest, including both observed and unobserved, and edges that encode hypothesized probabilistic relationships between quantities. Some of those quantities might be spatial coordinates associated with a particular object, but every quantity need not have a spatial representation. Multiple spatial representations can coexist, with deterministic coordinate transformations replaced by probabilistic mappings. Because everything is framed as a PGM, it could be written in a probabilistic programming language to facilitate automatic statistical inference, uncertainty quantification and model validation and comparison.

I would really like GIS software that centers model building as an interpretive tool, and I think this PGM approach has some potential to do that. Different researchers working with the same data may embed them in different models, so that a dataset need not have a single, fixed meaning. Visualization, which often seems to be the central focus of GIS, becomes a tool to inspect the performance of models and encompasses a much wider range of data visualizations than cartographic ones. A major challenge, I think, is in designing software that allows users to gradually integrate these modeling ideas into their workflow.