![]() ![]() The more classes you use, the less data generalization (which is good), but this comes at the expense of legibility and the associated risk of map reading errors since more colors are harder to see and print reliably (which is bad). Of course, your goals and data should also play into this decision: For example, political maps in the United States often have only 2 classes (the well-known red state / blue state maps) or a map that wants to simply highlight places above and below a national average. If you want to be safe, make a map with 3–7 data classes. For a more complete discussion of color in thematic mapping, have a look ColorBrewer. Note: it may be harder for your audience to locate places on the map without those borders. You may even decide to not draw those enumeration unit borders (no stroke, just fill). The colors of the enumeration unit borders (county and state lines here) also have a very large impact on the look of the map, so experiment with both fill and stroke color combinations. Note that the appearance of the choropleth colors will appear to change depending on what other colors are used on the map, such as blue water or black city labels. With sequential color schemes, it is traditional to use darker/stronger colors for larger numbers. Example Classed Choropleth Mapīelow is a 5-class choropleth map that uses a sequential color scheme (from light to dark) attached to an equal-interval classification scheme. Not sure you should use a choropleth map? Good alternatives include dot density maps, graduated/proportional symbol maps, and cartograms: Furthermore, while choropleth maps require that your data are standardized (rates, ratios…e.g., X per square kilometer or Y per 100,000 people), these other 3 map types can all handle raw data (e.g., simple counts, totals). The less the thing you are mapping is tied to enumeration units, the less sense a choropleth map makes. By comparison, tax rates are very closely tied to enumeration units, do change abruptly, and make perfect sense as a choropleth map. However, most cartographers would argue choropleth maps are over-used and commonly misused if the geographic phenomena being mapped aren’t intrinsically tied to enumeration units: For example, communicable diseases, soil types, or age demographics don’t care much about county lines or zip codes and rarely do they change abruptly at those human-created boundaries. One reason they’re popular is that much of our geodata is reported by enumeration units, such as census data, and so we are accustomed to thinking of the world as divided into spatial units like census tracts, counties, and provinces. ![]() That’s good because it means your audience is likely to understand them. map showing the percentage increase in home value from 1980 to 1990 by Canadian provinceĬhoropleth maps are extremely popular, probably the most common thematic map in use today.world map of percentage of population under 18 years old, reported by country.map showing the percentage change in skin cancer from 1990 to 2010 by Australian state.map showing number of births per 100,000 in 2009, reported by U.S.world map of income tax rates by country.For example, number of people is a count and not appropriate for choropleth maps number of people per square mile is a ratio and is a continuous statistical surface (even if it drops to zero over uninhabited places, every location has a data value) and, thus, is appropriate for choropleth maps.Įxample datasets appropriate for choropleth maps: geofile('/d3-geomap/topojson/countries/USA.json')ĭ3.csv('/data/venture-capital.csv').You can use a choropleth maps when your data are (1) attached to enumeration units (e.g., counties, provinces, countries), (2) standardized to show rates or ratios (never use choropleth with raw data/counts), and (3) you have a continuous statistical surface, in other words, you could conceptually measure the phenomena anywhere in space (n.b. The remaining code should be straight-forward, provided you read the explanation of the choropleth world map example. Since we don't use the default projection ( d3.geoNaturalEarth) we need to set the scale so the map fits well into the layout, in this case 1000 is a good value. The unitId used in this TopoJSON source is the FIPS code. Since this is a map of the US, we load the appropriate TopoJSON file and set the map object to use the d3.geoAlbersUsa projection. I'll only explain the differences to the example choropleth map of the world, where you can learn about the meaning of the other settings. This example shows how to create a choropleth map of states in the US using data from the National Science Foundation about venture capital spent in the US in 2012. ![]()
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