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All Places > Education > Blog > 2016 > February
2016

Cartograms are such a compelling
teaching and research tool.

Did you know that you can use
ArcGIS Desktop to create cartograms?

 

I wrote a short essay about that
very thing here:

https://blogs.esri.com/esri/gisedcom/2016/02/26/creating-cartograms-in-arcgis-desktop/

 

-- Joseph Kerski

Cartograms, because they distort our expected view of mapped variables, are wonderfully rich tools for teaching and research.  They allow us to see relationships and trends that may not be evident in a typical choropleth map.  A distance cartogram shows relative travel times and directions within a network.  More common is an area cartogram, a map in which some variable is used instead of the land area in each polygon to determine the size of that polygon. I remember using graph paper to make rectangular area cartograms as an undergraduate (though I realize I am dating myself).  Today, one can use Web GIS and desktop GIS to create cartograms. For example, nearly 700 variables can be mapped on www.worldmapper.org, and the data can be downloaded as Excel spreadsheets and analyzed within ArcGIS.

Let's say you wanted to dig deeper and make your own cartograms, with the ability to do further analysis within a GIS environment.  You can use the cartogram geoprocessing tool that my colleague Tom Gross at Esri created.  How can a GIS, which focuses on accurate spatial representations of features, be used to create cartograms? Download the tool and find out!  The tool also includes step-by-step instructions and a sample set of data.

Once you install the cartogram tool, you simply run it to create the cartograms.  An intuitive interface allows specifying input and output.  You can even distort your base layers (such as the imagery shown below) so that your cartogram can include these as reference layers.  I did this for cities, a 30-degree world grid, and a satellite image of the Earth to see these reference layers overlaid on my cartogram.

In this example, I chose to map the 2015 population by country.  Then, I mapped the total CO2 emissions by country in 2004, in millions of metric tons, from the US Energy Information Agency. What patterns do you notice?

The cartogram map layer has to be written into a geodatabase, but otherwise, the tool has few restrictions. I am very pleased cartographically with the results, and the methodology of how these cartograms are generated is well documented:   These are Density-Equalizing Cartograms using methodology developed by Mark Newman and Michael Gastner at the University of Michigan.

What other variables and at what other scales could you map and analyze as cartograms?
pop_2015_cartogram1.jpg

Cartogram of World Population in 2015.

co2_cartogram_with_gdp.jpg

Cartogram of C02 emissions in 2004, as a Map Layout.

Abductive reasoning (also called abduction, abductive inference or retroduction) is a form of logical inference that goes from an observation to a hypothesis that accounts for the observation. It ideally seeks to find the simplest and most likely explanation. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. One can understand abductive reasoning as "inference to the best explanation".  The fields of law,  computer science, and artificial intelligence research have renewed interest in the subject of abduction.

Abductive reasoning can be effectively taught through spatial thinking and analysis with the use of GIS technology.  Through the overlaying, swiping, and display of maps and imagery in a GIS, students are encouraged to make observations about the patterns, relationships, and trends, or lack of pattern.  They can then form a hypothesis about why the pattern exists and how it came to be.  They can then test that hypothesis against the data, by running a set of spatial statistical techniques, by testing different models, by symbolizing and classifying the data in different ways, and by examining different regions of the world at different scales, testing whether the relationship holds in all regions and scales, or just some.

All of this is what I find most valuable about GIS--it is one of those few tools that allow for inquiry, investigation, hypothesis testing, changing the variable(s) analyzed, all in one environment.

Consider the example below from a GIS-based investigation:  Say after observing the map that the student's hypothesis is that the savanna regime division is generally characterized by higher population densities in East Africa.  Then, they can investigate such questions as:  Does the savanna suffer from biodiversity loss to a greater degree than less populous ecoregions?  What are other factors that can help explain the pattern of population density in this area? At a larger scale, is population density still higher in the savanna than other ecoregions?  Why or why not?
ecoregions_pop_density_-1024x789.jpg

Examining ecoregions and population density in a GIS environment (ArcGIS Online).

This map in ArcGIS Online shows snow cover for the globe as it changes throughout the course of the year.  The patterns the map reveals is fascinating.  What is the effect of latitude on snow cover?  How does snow cover in the Northern Hemisphere compare to that in the Southern Hemisphere?  Which month had more snow on the ground than any other, in Canada, versus in Sweden?  What is the effect of elevation on snow cover?  During a specific month, which of the world's major cities had snow on the ground?  Measure the extent in square kilometers for each of the months for specific countries and graph your results.

This map features NASA’s Next Generation Blue Marble imagery in a set of 12 monthly composite images of the entire earth, using 500-meter-resolution imagery from the MODIS satellite.  More information is available than just the snow cover.  These monthly images reveal other seasonal changes on the land surface, such as the greening up and dying back of vegetation in the temperate regions such as North America and Europe, and the dry and wet seasons in the tropics.  The Blue Marble Next Generation imagery was produced by Reto Stöckli, NASA Earth Observatory (NASA Goddard Space Flight Center).  The data is from 2004.  For more information about this data layer, see this description.  The data layer can be added to any map that you are analyzing in ArcGIS Online, and compared to other data layers, such as mean annual precipitation, or ecoregions.   If you prefer to show the data as a web mapping application, see this app.

How might you use this map and its information in your instruction?
snowcover.jpg

Seasonal changes map in ArcGIS Online.

In an article in Directions Magazine, I describe five forces catapulting geography onto the world stage.  These five forces, including geo-awareness, geo-enablement, geotechnologies, citizen science, and storytelling, are transforming the audience for geography and the way geography is taught and perceived.

After I describe each of these forces and why they matter, I define in the article what I consider to be the three legs of the "geoliteracy stool" - (1) content knowledge, (2) skills, and (3) the geographic perspective.  I then ask, "Is geoliteracy becoming increasingly valued?  What role can and should teaching and learning with GIS play in geoliteracy? How can the community seize the opportunity that these five forces represent to foster geoliteracy and promote the use of GIS and spatial thinking at all levels of education?"

I look forward to hearing your comments.
5trends-1024x535.png

Five converging global trends that present geography with new global opportunities (Kerski, 2016).

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