Some general considerations about this course and other geospatial analysis courses offered in Geography.

Geospatial Thinking Video

An overall theme of the geospatial courses offered in this department is to enable you to become geospatial thinkers. To think spatially requires both:

• a theoretical perspective–knowing how things can be represented in a spatial database, knowing the kinds of things you can do with spatial data (e.g., buffer, intersect, create a friction surface, perform a geographically-weighted regression), knowing where the uncertainties lie and how to reduce them, knowing cartographic conventions, etc.; and
• practical experience–the more you know of the commands / tools / drawbacks / protocols associated with geospatial data entry, analysis, output, the easier it will be to become a (computational) spatial thinker (See: Find a geoprocessing tool in ArcGIS Pro.)

How we see things determines what actions we may take in response to perceived problems. The actions we take, be they simply producing a map highlighting the ‘hot spots’ of criminal activity, or conducting a complex multi-criteria analysis, are dependent upon us knowing what is possible.

For example, when we look at high power transmission lines, we see them as passive objects that criss-cross the landscape. However, ecologists have noted that many animals tend to avoid power lines. They couldn’t determine why the power lines created such a barrier to the animals until someone looked at the power lines in the same way that animals do, from an ultraviolet perspective–how they appear to birds and most other animals. (How birds see.)

In a similar light, geospatial analysis requires you to see things differently–you need to learn to think ‘spatially’. For example, the shortest least-costing path between two points isn’t always the ‘shortest’ path (i.e., as the crow flies). Some examples of current uses of ArcMap.

Geospatial analysis requires you, for example, to know how to properly prepare the data for spatial analysis (see the problem associated when working with multiple projections). Therefore, you should perform all needed transformations to the data (e.g., projections, vector-to-raster conversion, variable normalization) before doing any analyses, but do so only as necessary (e.g., projecting a raster layer more than once can greatly increase the uncertainty associated with that layer–why? [here, look at the discussion around resampling])

You also need to be aware of all of the different kinds of spatial analysis that are possible, awareness that only comes through knowledge gained in classes (e.g., learning the theory behind geostatistical routines such as spatial grouping analysis), reviewing examples of analyses (e.g., published papers, videos), and performing analyses (e.g., the labs and your project).

So, as remote sensing (in the above case, being the ability to see the world in UV) has enabled biologists to see a world that they hadn’t been able to see before (and therefore much better understand the behaviour of many species), GIS has enabled geographers to examine spatial relations in a way they had never been able to do so before. However, in order to fully appreciate the benefits that GIS can provide to geographers requires you to become a geospatial thinker.

GIS has recently been put into a much broader perspective with the development of the concept of Geodesign. To learn what some consider geodesign to be, and how GIS fits within geodesign, review the Geodesign Summit conference pages

Questions:

When were GISystems first developed?
In what decade was the term GIScience coined?