Mapping is all about graphic presentation, but sometimes the best solution is a simple, concise, text explanation. We discussed dealing with outliers earlier in the lesson-one option for dealing with a relevant outlier is simply to point it out to your readers via explanatory text. Learn about Americas People, Places, and Economy on the official United States Census Bureau data platform. However, given the topic of the map, this explanation is important. Tell us how we can make your experience better. Due to the classification scheme used, the location indicated by the leader line and Prisons* note does not immediately stand out as an outlier. Additional legend annotations (e.g., “High proportion of AIAN are young”) serve to clarify the map.įigure 4.7.4 below similarly uses a text explanation to clarify the data mapped. The break is annotated to inform the reader of this fact-without this annotation, the use of this specific break would not be useful. This map also purposefully places breaks in the data-for example, one break is placed at 24 percent, which is the percentage of all people in the US who are under 18 years old. However, without this level of detail, the content of the map would be confusing, and many readers would likely misinterpret it.Ĭredit: Cynthia A. ![]() It may seem at first that this legend is too text-heavy: you don’t generally create visual graphics with the intention of asking people to read. Though it is now easy to compare these states, we are unable to discern which areas of Vermont are more populated than others: they are all simply classified as "less than 562 residents per square mile." Making maps that work well both independently and when compared is a challenging task, and one which we will contend with in Lab 4.Īnother important aspect of choropleth-and any-map design is making sure that marginal elements such as legends and labels are well-crafted to support reader comprehension of your map. ![]() This gives us an entirely different view of the data-New Jersey is now visible as obviously more densely populated. Note, however, that this map just took the default classification scheme from New Jersey, and applied it to Vermont, which is still not a good solution. Credit: Cary Anderson, Penn State University, Data Source: US Census Bureau.
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