This week I completed the 5-week MOOC, Maps and the Geospatial Revolution, taught by Dr. Anthony Robinson of Penn State on the site Coursera. Over 47,000 students enrolled and over 35,000 students participated, making it the largest GIS course ever taught. I found the course to be a good overall survey of the world of GIS (geographic information systems). I recommend it to anybody wanting to learn about GIS or who wants to see what it’s like to take a MOOC (massive open online course). Below is my brief overview which only skims the surface of what we covered.
In the first week, Dr. Robinson illustrated how geospatial technology has revolutionized the way we navigate, make decisions using geography, and share stories using geographic references, like geotagging and geocoding.
The second week covered reference maps and thematic maps. We saw thematic maps showcasing geographic observations, including choropleth maps, dot mapping, proportional symbol mapping, and isoline maps. The concept of spatial autocorrelation was introduced (the measure of similarity of observations that are close to one another), as well as Tobler’s first law of geography:
Everything is related to everything else, but near things are more related than distant things.
A cool project highlighted in week two was Ushahidi, a non-profit organization that built a platform for volunteered geographic information in disaster areas. Ushahidi was instrumental in relief efforts in Haiti where online maps were very poor at the time of the earthquake.
Week three introduced GNSS (global navigation satellite systems) ranging from America’s GPS, GLONASS in Russia, to the E.U. Galileo system. We also covered the vector and raster spatial data types, as well as remote sensing, LIDAR, spatial metadata, and ArcGIS Online.
Week four discussed map overlays, first introduced in Ian McHarg’s 1969 book, Design with Nature. A video explained how Dr. John Snow’s map of the cholera outbreak in 1854, Soho, London, was used to trace the cholera outbreak to a bad well. That was the first major use of cluster detection. We learned that spatial correlation is not causation, and we learned about the MAUP (modifiable areal unit problem), where scale effects how geospatial observations are visualized. We covered how important normalization is to avoid skewing maps by population density. To normalize data you calculate the rates of occurrence as a proportion of the overall population.
Finally, week five covered the art of map making, which starts by clearly understanding the audience, target format, and specific purpose of the map. It’s difficult to give a concise summary, so I’ll just say the lesson taught that it is important to use the right thematic map for the type of data (nominal, ordinal, or interval/ratio data). On the topic of color, we covered sequential colors, diverging colors, and qualitative colors. We then explored data classifications: equal interval classification, quantile classification, and natural break classification.
The final assignment was a map of your choosing. I chose to use to running and cycling data that I extracted to create a surface analysis map. The data has some serious flaws (a triathlon at Clinton Lake skewed my data), but it was adequate for my proposes.
I’m already looking for other MOOCs of interest to me that are offered by Corsera.