Maps need context by Jon Schwabish and Bryan Connor.
From the post:
It might be the case that maps are the most data-dense visualizations. Consider your basic roadmap: it includes road types (highways, toll roads), directions (one-way, two-way), geography (rivers, lakes), cities, types of cities (capitals), points of interest (schools, parks), and distance. Maps that encode statistical data, such as bubble plots or choropleth maps, are also data-dense and replace some of these geographic characteristics with different types of data encodings. But lately we’ve been wondering if most maps fail to convey enough context.
As an example, consider this map of poverty rates by districts in India. It’s a fairly simple choropleth map and you can immediately discern different patterns: high poverty rates are concentrated in the districts in the northernmost part of the country, on part of the southeast border, and in a stretch across the middle of the country. Another set of high-poverty areas can be found in the land mass in the northeast part of the map. But here’s the thing: we don’t know much about India’s geography. Without some context—plotting cities or population centers—we can only just guess what this map is telling me.
Many readers will be more familiar with the geography of the United States. So when maps like this one from the Census Bureau show up, we are better equipped to understand it because we’re familiar with areas such as the high-poverty South and around the Texas-Mexico border. But then again, what about readers familiar with basic U.S. geography, but not familiar with patterns of poverty? How useful is this map for them?
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In addition to establishing the potential need for more context, Jon and Bryan go on to describe a tool for building and comparing maps with different data sets included.
You should take context into account in deciding what groups of topics and associations to merge into or leave out of a topic map. Too much detail and your user may lose sight of the forest. Too little and they may not be able to find it at all.