# About symbolizing layers to represent quantity

Features have numerical attributes, which can be used to display layers to show quantities. The numerical measures used in your layer display might represent the following:

• A count
• A ratio, such as a percentage
• A rank, such as high, medium, or low

There are several methods with which you can represent quantity on a map—for example, using colors, graduated symbols, proportional symbols, dot densities, or charts. This topic provides an overview along with links to topics that describe how to create and use each type.

You can represent quantities on a map by varying colors, such as the colors used in choropleth maps. For example, you might use darker shades of blue to represent higher rainfall amounts.

When you draw features with graduated colors, the quantitative values are grouped into classes and each class is identified by a particular color.

Another alternative to represent map quantity is to use graduated symbols. When you draw features with graduated symbols, the quantitative values are grouped into classes. Within a class, all features are drawn with the same symbol. The map below uses four size classes to represent earthquakes of various magnitudes:

## Using proportional symbols

An alternative to representing data values by classes using varying symbol sizes for each class is to use proportional symbols. The size of each symbol reflects the actual data value. For example, you might map earthquakes using proportional circles, where the radius of the circle is proportional to the magnitude of the quake.

The image below is an example of how proportional symbols appear in the legend and table of contents (as a stack of progressively larger blue circles):

One difficulty with proportional symbols arises when you have too many values. Differences between symbols may become indistinguishable. In addition, the symbols for high values can become so large that they obscure other symbols.

## Using dot densities

Another method of representing quantities is to use a dot density map. You can use a dot density map to show the amount of an attribute within an area. Each dot represents a specified number of features—for example, one dot represents 1,000 people or 10 burglaries within a given area.

Dot density maps show density graphically rather than showing density value. The dots are distributed randomly within each area; they don't represent actual feature locations. The closer together the dots are, the higher the density of features in that area.

When creating a dot density map, you specify how many features each dot represents and how big the dots are. You may need to try several combinations of dot value and dot size to find one which best shows the pattern. In general, you should choose value and size combinations that ensure the dots are not so close as to form solid areas that obscure the patterns or so far apart as to make the variations in density hard to see.

In most cases, you'll only map one field using dot density maps. In special cases, you may want to compare distributions of different types and may choose to map two or three fields. When you do this, you should use different colors to distinguish between the attributes.

## Using charts

Bar/column charts, stacked bar charts, and pie charts can present large amounts of quantitative data in an eye-catching fashion. Generally, you'll draw a layer with charts when your layer has a number of related numeric attributes that you want to compare. Use pie charts when you want to show the relationship of individual parts to the whole. For example, if you are mapping population by county, you can use a pie chart to show the percentage of the population by age for each county. Use bar/column charts to show relative amounts rather than a proportion of a total. Use stacked charts to show relative amounts as well as the relationship of parts to the whole.

For bar/column charts, the value refers to the highest bar in the bar/column chart symbol. With stacked or pie charts, it refers to the sum of all fields being represented by the stacked or pie chart symbol.

Chart maps allow you to symbolize multiple attributes on one map as well as communicate the relationship among different attributes. Chart maps display charts—bar and pie charts—over features. This map shows you the volume and type of goods distributed throughout Asia:

## Counts and amounts, ratios, and ranks

Knowing what type of data you have and what you want to show will help you determine what quantitative value to map. In general, you can follow these guidelines:

• Use map counts or amounts if you want to see actual measured values as well as relative magnitude. Use care when mapping counts, because the values may be influenced by other factors and could yield a misleading map. For example, when making a map showing the total sales figures of a product by state, the total sales figure is likely to reflect the differences in population among the states.
• Use map ratios if you want to minimize differences based on the size of areas or number of features in each area. Ratios are created by dividing two data values. Using ratios is a mechanism used to normalize the data. For example, dividing the 18-to-30-year-old population by the total population yields the percentage of people aged 18 to 30. Similarly, dividing a value by the area of the feature yields a value per unit area, or density.
• Use map ranks if you are interested in relative measures and actual values are not important. For example, you may know a feature with a rank of 3 is higher than one ranked 2 and lower than one ranked 4, but you can't tell how much higher or lower.

## Should you map individual values or group them in classes?

When you map quantitative data, you can either assign each value its own symbol or you can group values into classes using a different symbol for each class.

If you are only mapping a few values (less than 10), you can assign a unique symbol to each value. This may present a more accurate picture of the data since you are not predetermining which features are grouped together. More likely, your data values will be too numerous to map individually and you'll want to group them in classes (classify the data). A good example of classified data is a temperature map. Instead of displaying individual temperatures, these maps show temperature bands, where each band represents a given range in temperature.

### Classifying data

How you define the class ranges and breaks—the high and low values that bracket each class—will determine which features fall into each class and what the layer will look like. By changing the classes, you can create very different-looking maps. Generally, the goal is to make sure features with similar values are in the same class.

Two key factors for classifying your data are the classification scheme you use and the number of classes you create. If you know your data well, you can manually define your own classes. Alternatively, you can let ArcMap classify your data using standard classification schemes. There are a number of alternative classification methods you can use.

10/25/2012