Cartograms of US Economy Quiz
Spatial data distortion (10 questions).
Cartograms of US Economy Quiz: Quick Study Notes
Cartograms function as a “corrective lens” for human geography, resizing states based on economic output (GDP) rather than physical square mileage to visualize where financial power truly resides.
A map where the size of a geographic unit is proportional to a statistical variable, not land area.
In US economic maps, California, Texas, and New York expand to dominate the visual field.
Large rural states like Alaska and Montana contract significantly due to lower economic density.
To remove “land-area bias” and show the true weight of human or economic activity.
Key Takeaways
- Standard maps distort economic reality by overemphasizing large, empty land masses.
- In a GDP cartogram, California often appears larger than the entire Midwest combined.
- The “Northeast Corridor” (Boston to DC) expands dramatically due to high economic density.
- Cartograms can be contiguous (warped shapes) or non-contiguous (separate shapes) to preserve topology.
- Visual distortion in cartograms highlights inequalities in resource distribution.
Frequently Asked Questions
What is a cartogram?
A cartogram is a map in which the geometry of regions is distorted in order to convey the information of an alternate variable, such as population or GDP, rather than land area.
Why does Alaska look so small on economic maps?
While Alaska is the largest US state by land area, its economy (GDP) is relatively small compared to states like California or New York, causing it to shrink in an economic cartogram.
Which state looks the largest on a US GDP cartogram?
California typically appears the largest, as it has the highest Gross Domestic Product of any US state.
How does a cartogram handle “land-area bias”?
It corrects the visual assumption that “bigger is more important” by shrinking physically large but data-poor areas and expanding small but data-rich areas.
What are Dorling cartograms?
Dorling cartograms replace geographic shapes entirely with simple geometric figures (like circles) sized according to the data variable, abandoning the original shape of the state.
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