What types of data are suitable for choropleth maps?

What types of data are suitable for choropleth maps?

You can use a choropleth maps when your data are (1) attached to enumeration units (e.g., counties, provinces, countries), (2) standardized to show rates or ratios (never use choropleth with raw data/counts), and (3) you have a continuous statistical surface, in other words, you could conceptually measure the phenomena …

What color scheme is best to use with a choropleth map?

Choropleth maps typically use sequential or diverging color palettes. The colors in a sequential palette should be ordered in such a way where your map reader can easily distinguish between high and low values. An effective sequential palette varies in lightness and saturation at each color stop.

How does a choropleth map show data?

Choropleth maps show interval data (data that is linked, rather than data from different categories) as colours. They are shaded in using one colour, where the darker shades represent high numbers and the lighter shades represent low numbers. Population density can be shown using a choropleth map.

Why would data be normalized to use with a choropleth map?

Normalization and proportional data You can counter the bias created from different-sized areas in choropleth maps by styling the maps by averages, proportions, rates, and ratios instead of counts or totals. Both maps above use colors to show the number of restaurants by county.

What is an example of an isopleth map?

A broad term for any line on a weather map connecting points with equal values of a particular atmospheric variable (temperature, dew point, etc.). Isotherms, isotachs, etc. are all examples of isopleths.

What to consider when creating a choropleth map?

Regional patterns could be an unusually high unemployment rate in neighboring counties, or the contrast between cities and rural areas. If your data doesn’t show a clear regional pattern, consider another chart type for your data.

How is the classification scheme used in choropleth?

In broad terms, the classification scheme defines the number of classes as well as the rules for assignment, while the symbolization should convey information about the value differentiation across the classes. In this chapter we first discuss the approaches used to classify attribute values.

How does a choropleth map reduce the cognitive load?

Chief among these is to reduce the cognitive load involved in parsing the complexity of an unclassed map. A choropleth map reduces this complexity by drawing upon statistical and visualization theory to provide an effective representation of the spatial distribution of the attribute values across the areal units.

How is the width of bins determined in Freedman-Diaconis?

The Freedman-Diaconis approach provides a rule to determine the width and, in turn, the number of bins for the classification. This is a special case of a more general classifier known as “equal intervals”, where each of the bins has the same width in the value space.