Spatial Thinking

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In a conference, Robert Weibel (Weibel 2012) proposed to discuss and compile a set of principles and theses coming from GIS Science which could be of interest to a broader audience.

Hereafter there is a incomplete compilation of such principles, theses and problems of Spatial Thinking:

  1. Layer principle - georeferenced, sharing same CRS, being able to do "spatial joins" and spatial operations (like "is within") and calculations (like "intersects/overlays").
  2. View principle - Separation of data and graphic visualization.
  3. GIS zoom - GIS zoom versus graphic zoom.
  4. "Geographic modeling and representation problem" - How to model and encode object and field entities? As vector or raster or...? While discrete objects can be represented naturally using points, linestrings, and polygons, the representation of continuous fields is more difficult. Typical implementations in GIS are mainly (1) regular grid points, (2) regular cell area, (3) irregular grid (points) / (4) point cloud, and (5) irregularly shaped triangles (triangulated irregular network, TIN). Besides the fact that raster data can be interpreted as regular grid points or cell areas, there are other field representations or implementations like isolines etc.. GIS can typically display irregular triangular and regular quadrilateral meshes, in addition to raster data.
  5. "Problem of absolute values in areal/linear features" - A counting value, like no. of people, represented in an area (or line) should not be be displayed as an absolute value but relative to the area size, i.e. people per square. (Goodchild 2004).
  6. "Homomorphic mapping problem" - How to map a message to a cartographic signature? A map consists of a combination of point, line and area symbols and text. Bertin (1974) defined a set of "graphical variables" which can be varied and which can be part of a rendering/styling configuration: FORM, SIZE, PATTERN, COLOR and LIGHTNESS. Point and area symbols as well as text fonts can additionally be displayed with a frame.
  7. "Data capturing dilemma" - Do not capture data for the map, but for the spatial database. People tend to map the visual but should map the abstract representation. And people tend to believe the satellite map instead of accurrate vector map.
  8. "Limited map space conflict" - "All important places are at the corners of four map sheets." (Goodchild 2004)
  9. "Scale/resolution problem" - A fundamental property of any geographic representation. Two characteristics: spatial resolution and spatial extent. Conflict over "large" and "small". What does it mean in digital data? (Goodchild 2004)
  10. "Modifiable Areal Unit Problem" - (MAUP) - or "Zone Definition problem" (ZDP). A potential source of error that can affect spatial studies which utilise aggregate data sources into higher level units and scales (Ratcliffe)
  11. "Tobler's first law" - "Nearby things are more related than distant things", Spatial Autocorrelation (Tobler 1970). Variation: "First Law of Cognitive Geography": "People think closer things are more similar" (Montello and Sarah Fabrikant)
  12. "Spatial heterogeneity problem" - Space is non-stationary and has uncontrolled variance (included by the MAUP but considering only variety, not scale/aggregation). Means that sampling is problematic; one must visit all of it to understand its full complexity. Results depend explicitly on the bounds of the study. (Goodchild 2004)
  13. "The Data Engineering & Analytics Challenge" - How to manage massive data and how to keep analysis performant (scaling out, SQL, NoSQL)?


See also: Geovisualisierung