Note that the default aggregation for the Percent-2012 measure is SUM by default. The map view changes to a filled map mark type and the polygons are colored green. There is now a data point for every county in your data source.įrom the Data pane, under the countyObesity$ folder, drag Percent-2012 to Color on the Marks card. On the Marks card, click the plus icon on the State field to drill down to the County level of detail. In the map view, select the Alaska and Hawaii data points, and then click Exclude on the tooltip that appears. A map view is created with a data point for each state in your data source. State is added to Detail on the Marks card and Longitude and Latitude are added to the Columns and Rows shelves. In the Data pane, under the State, County folder, double-click State. To follow along with this example, download the Create Choropleth Maps in Tableau Example Workbook (Link opens in a new window) from Tableau Public, and open it in Tableau Desktop. One or more geographic units (dimensions with geographic roles assigned) Latitude (continuous measure, latitude geographic role assigned) Longitude (continuous measure, longitude geographic role assigned) Stateīasic map building blocks: Columns shelf: ![]() It contains columns for State, County, and Obesity Percent - 2012. The following table is a snippet of the countyObesity + (Obesity_State_County) data source, which is included in the Create Choropleth Maps in Tableau Example Workbook (Link opens in a new window) on Tableau Public. ![]() For more information, see Create Tableau Maps from Spatial Files (Link opens in a new window). Location names (if recognized by Tableau), or custom polygons.To create a choropleth map, your data source should include the following types of information: Follow the example in this topic to learn how to set up your data source and build the view for a choropleth map. This topic illustrates how to create a choropleth map using an example. For example, you are likely to have a higher count for sales in regions with a higher count of people. Counts are often related to size or population for regions. Note: When using aggregated data, be careful when using counts. For more information, see Create Territories on a Map (Link opens in a new window). They can even be custom territories created in Tableau. These polygons can be counties, regions, states, or any area or region that can be geocoded in Tableau. These types of maps are called choropleth maps, or filled maps.Ĭhoropleth maps are best for showing ratio or aggregated data for polygons. Mapping is all about graphic presentation, but sometimes the best solution is a simple, concise, text explanation.You can create maps in Tableau Desktop that show ratio or aggregated data, similar to the example below. 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. ![]() However, given the topic of the map, this explanation is important. 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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