In Lab 4, we will explore different ways of choosing data classification and color schemes for choropleth maps. As a cartographer, you will often have to choose between several of these options - many of which may seem at first glance to be equally appropriate. In Lab 3, we used data from the American Community Survey, provided by the US Census - a commonly-used source of data for statistical maps. In this lab, we use the same data source but focus on a specific variable frequently in focus during public policy debates: health insurance.
The first part of Lab 4 will focus on data classification. There are many ways to classify statistical data on maps, and it is important that you understand them, and be able to defend your choice of classification scheme to others. As we will be not only be classifying data but also adding that data to maps, this lab will also focus on the use of color on maps. Finally, as suggested in the lesson content, we will explore ways of making comparable maps - in this lab, we will be making three pairs of maps.
This lab, which you will submit at the end of Lesson 4, will be reviewed/critiqued by one of your classmates in Lesson 5.
For Lab 4, you will create three pairs of maps, each pair as its own full-page map layout. In total, you will have three separate pages. Two maps will appear on each page. You will also write a short reflection statement about each pair of maps.
A rubric is posted for your review.
More instructions are available in the Lesson 4 Lab Visual Guide.
This is your starting file in ArcGIS Pro. It includes county-level boundary data for the United States. This county-level file has been joined with health insurance data for New England from the American Community Survey (ACS). A state boundaries file is also included – this file is not needed to map the health insurance data, but you may choose to symbolize it to create visible state boundaries on your map.
Within the health insurance data provided in the Lab 4 zipped folder, find two variables you are interested in and their associated universes. For example, if you were interested in uninsured people under 18, your value and universe would be those shown in Figure 4.2 below. (note: this is one variable, you need to choose two).
Paste the four columns you will need "as values" (see Figure 4.3) into the Chosen Data sheet. (Reminder: use something other than just age for your maps). This will eliminate the clutter of the full dataset, giving you space to calculate standardized values from your data. We will use these standardized values to determine class breaks for our first set of maps.
Once you have your two variables of interest (and their universes) in the Chosen Data sheet, use Excel to calculate a standardized column of data for each of your variables. You want to divide each variable of interest by its universe (recall the Data Standardization section in Lesson 4).
Insert a column of 1s and 2s as shown - we will use this to create a dot plot. When you select columns A and B below and insert a scatter plot, this will create a dot plot showing the distribution of your two standardized variables along the number line.
Draw lines with the "insert shape" tool to illustrate where you will be placing breaks in your data. Annotate your lines if you choose the breaks for a reason other than just eyeing the dot distribution. For example, if you place a break at the national average for a variable, annotated this break with a text box explanation such as "US national average." Ex: “national average."
Note that Figure 4.7 is an example of how to draw lines above your dot plot, but these are not good breaks.
We will not be importing our excel data into ArcGIS, as I have already loaded the health insurance data into ArcGIS for you. We only needed the Excel file to decide on what breaks to use for our data classification. Instead of importing standardized values, use ArcGIS to standardize your data for you: make sure the variables you choose match the ones you chose earlier!
You will then manually edit your class breaks to match the ones you drew on your dot plot (use your eye to estimate the values). The screenshot in Figure 4.8 (below) is an example of a screenshot from the Symbology Pane. You will submit a screenshot of the Symbology Pane for both maps in layout one, in addition to an image of your dot plot with annotated breaks.
For these maps, you will be setting a critical class break (e.g., based on the mean of the data) and a diverging color scheme. To create your second pair of maps, choose a diverging color scheme. Then, set a deliberate and useful critical class or break. Once the break is set, you should manipulate the other class breaks manually. As a suggestion, for the other class breaks you could start with the manual breaks you chose for your first two maps, but may need to adjust them to work with this new color scheme. Reference the Lesson 4 reading for ideas and advice on how to choose a critical class or break.
For the third set of maps, abandon your previously-selected class breaks. In this set of maps, you will compare the visual difference between a classed map and an unclassed map. Use the same sequential color scheme for both maps so they can be adequately compared. You should also use consistent line design, etc., so as to not distract from the primary difference of interest - the classification method used. Unlike with the first two sets of maps, you will not be mapping two different variables for comparison here. You will choose just one of the variables from your previous maps, and visualize this variable on both of maps 5 & 6.
For your classed map, choose any of the methods available in ArcGIS Pro – but have a reason why! You will discuss your reasoning for choosing one of these methods in your write-up for this map pair.
Natural Breaks (Jenks): Numerical values of ranked data are examined to account for non-uniform distributions, giving an unequal class width with varying frequency of observations per class.
Quantile: Distributes the observations equally across the class interval, giving unequal class width but the same frequency of observations per class.
Equal Interval: The data range of each class is held constant, giving an equal class width with varying frequency of observations per class.
Defined Interval: Specify an interval size to define equal class widths with varying frequency of observations per class.
Manual Interval: Create class breaks manually or modify one of the present classification methods appropriate for your data.
Geometric Interval: Mathematically defined class widths based on a geometric series, giving an approximately equal class width and consistent frequency of observations per class.
Standard Deviation: For normally distributed data, class widths are defined using standard deviations from the mean of the data array, giving an equal class width and varying frequency of observations per class.
For this lab you will submit three layouts, each containing a pair of maps. You will also submit a write-up document, with a 100+ word explanation of your design (data classification and color) choices for each map pair. Make sure to also design a neat and useful layout - see Lesson/Lab 2 for layout design advice.
Don’t copy this (poor) layout design – use your own knowledge and judgment. Clean up titles, marginal elements, alignments, etc. – use either portrait or landscape, whichever you prefer. Note that elements which refer to both maps (legend; north arrow; scale bar) need only be included once.
Don’t copy this (poor) layout design – use your own knowledge and judgment.
Use convert to graphics to manually improve your legend. Use a text box to annotate your critical class/break!
Don’t copy this (poor) layout design – use your own knowledge and judgment. Remember this map pair uses the same data for each map – it is demonstrating the effects of classification. Your goal should be to make a clean, useful legend for each map - make it look better than the legend design below.
Think about color and what you are mapping. Are you mapping insured or uninsured? Choose colors wisely – what do they represent?
Remember that you can employ text to explain your map! Use text sparingly but effectively – don’t be afraid to use convert to graphics and/or manually edit text and layout elements. When choosing a color scheme as well as when doing your write-up, keep in mind: the perceptual progression of your data should match the perceptual progression of your color scheme.
Credit for all screenshots is to Cary Anderson, Penn State University; Data Source, US Census Bureau.