Smart Mapping: Charts

This quick guide introduces how to map and compare relative proportions of your numeric categorical data in a single map. This technique turns raw counts into simple-to-compare ratios symbolized as pie charts, making it easier to compare categories in a single feature and totals between and across locations. Often, we are mapping datasets with big numbers based on population or acreage that can feel rather abstract when looking at totals, especially when we want to compare to other categories within our dataset. Pie chart proportional symbology can come to our aid.

For example, mapping the relative proportion of people in age groups across the country provides a clearer interpretation of age distributions than putting up raw numbers in a pop-up or chart. To facilitate better understanding of categorical data, and provide your audience with easier interpretations and comparisons, mapping with proportional symbology should be considered.

The Charts style helps us compare relative proportions, or the relative predominance, of mulitple attributes. This drawing style works on either area or point data, using at least two or more numeric attributes from your dataset.

Charts and Size shows the relative predominance of your categorical data, and sizes your charts by the sum of categories - or the total number of the attributes you are comparing.

Smart mapping helps you create beautiful and informative maps quickly, suggesting appropriate styles according to your attribute’s field types. When working with your data in ArcGIS Online and Enterprise, choose the Styles option to work with smart mapping. When you add 2 or more numeric attributes to your symbology, Charts will appear as a suggested drawing style.

Charts show the proportions of your categorical data associated with a single feature, allowing you to visually compare using proportional size rather than trying to compare numeric values.

We often come across datasets that contain the counts of people by age group, the total number of households by owner or renter status, or even the count of people holding a particular type of insurance. The Charts mapping style can help us map these attributes, allowing us to compare the total numbers of multiple columns of data, or multiple attributes.

Note that the categories you map need to be mutually exclusive, meaning that an age group, household, or insurance type can be in only one of those categories. For example, we are mapping the proportion of people holding a particular type of insurance. Each category isolates a particular type of insurance for the population age 19 to 34. In order to compare, we show each insurance type as it's own category.

The Charts mapping style should be used to show proportions of categorical data associated with a single feature, using either a summary statistic or a count. This requires data containing numeric counts of different categories like:

• Languages spoken by number of households
• Counts of people by age group
• Number of mental health providers vs the number of people requesting mental healthcare services
• Total number of natural hazard events by natural hazard type

Best implementations of this style use a range of 2 - 6 attributes. The software allows up to 10 attributes to be compared, but think twice about whether or not a pie chart with 10 categories makes sense or will reveal patterns you are trying to highlight. If you have more than 10 attributes, they will be grouped into an Other category and patterns or relationships could be lost in the saturation of variables.

To make a Charts style map in Map Viewer, choose between 2 and 10 numeric attributes to compare within the smart mapping interface. This smart mapping style becomes available as soon as you add two or more numeric attributes to the map. Select your layer and open the Styles pane from the right toolbar.

You've now created a map showing relative proportions of expected annual loss due to natural hazards for buildings, population, and agriculture. This map is repeatable - simply choose the numeric attributes you want to compare, and smart mapping will give you a starting point to go from. Remember, spend those few extra minutes going beyond your defaults to learn more about your data, and illuminate hidden patterns.

Charts give us a superb launching point for comparing the relative proportions of categories in our data. But there are many more ways to customize our maps and enhance our map viewer's experience. Here are a few examples to get you inspired: