![]() This default is just a starting point, it is NOT the one-size-fits-all solution for making maps. This is useful information to have as you think about how to style this map. In this case, the source data did not provide the national average for percent low birth weight, but a broader search found several indications that 8.1% is indeed the national average. (Note: this is the average of the data, not necessarily the true national average, because counties vary widely in population from hundreds to millions.)Īt this point, I always go search the documentation or online for what the literature has to say about this subject. From the legend or by hovering over the x in the histogram, you can see that the mean is 8.1 for this data set. Where did these values come from? They are 1 standard deviation above the mean (10.0) and below the mean (6.1). I’d call this “data-aware color” or “detailed color” or “data-faithful color.” Sometimes referred to as “unclassed” or “continuous” color, it’s value is that you get an overall pattern on the map, and you can see how neighboring counties vary slightly. Values between 10.0 and 6.1 are shaded a color somewhere between dark blue and yellow, depending on where the value falls. These extreme values are not the main story in this map style. ![]() Counties with 6.1% or lower will be given a full yellow color. In this case, counties with 10% low birth weight or higher will be given a full dark blue color. For the “High to Low” theme, the little handles indicate at what values dark blue or yellow are applied. Open this web map from the Living Atlas in ArcGIS Online, hit “Modify Map” in the top right corner, and look at the purple layer titled “County Health Rankings 2018.” Or, just cycle through each layer one at a time to follow along this blog.ĪrcGIS Online shows you the color ramp next to a histogram of the data. How can you tell a thematic map has been rushed into use without a specific purpose?ġ)ĝefault colors, default outlines, default classification settingsĢ) The breaks used to set the colors have no intrinsic meaning – they are the numbers generated by a classification algorithm.ģ) The colors have not been chosen to emphasize the interesting part of the data.Ĥ) The legend contains unnecessary levels of precision They choose a default classification technique, verify that the map shows some variation in colors, and call it a day, when in reality that map is unfinished. That first step (exploring the data) is key – unfortunately a lot of people simply want to get the thematic map done as quickly as possible without thinking critically about the data. Pretty straightforward.Īs always, let’s explore the data on the map first, to compare what we know about the subject to what’s on the map, and then make a thematic map of it. It is easy to imagine a choropleth map of the counties, each colored by its Low Birth Weight percent. It represents the percentage of all births in a county that meet the standard of low birth weight. Let’s pick just one subject among the many attributes in this gold mine o’ data: Percent Low Birth Weight. In this blog we’ll cover how the software (in this case, ArcGIS Online) starts the thematic map, and how a human improves what the software suggests to give the map purpose. The software can map it, but it takes a human to make it meaningful. The layer contains dozens of useful measures, each waiting to be turned into useful information in a thematic map. county health rankings data from the Robert Wood Johnson Foundation and University of Wisconsin Health Institute and made a few maps for policy making from it. To start, we need data, and an idea of what we want to map. The purpose of this blog is to discuss how a typical thematic map of a percentage comes into focus and how you give it purpose. Because I am a geographer who makes a lot of thematic maps, over time I’ve noticed the key moments in the decision making process that dramatically influence each map.
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