DIGITAL MAPPING TYPES

 

 

“Thick” Mapping

Thick mapping is the processes of collecting, aggregating, and visualizing ever more layers of geographic or place-specific data to embody temporal and historical dynamics through a multiplicity of layered narratives, sources, and even representational practices. It is a method of doing ethnography – extensive description about cultural context and meaning – a path to understanding. The Hypercities project (see http://www.hypercities.com and http://hypercities.ats.ucla.edu) is seen as one of the most extensive examples of thick mapping.

 

Choropleth Mapping

Choropleth maps are great to show clear regional pattern in the data, or for local data. For example, regional patterns could be an unusually high unemployment rate in neighboring counties, or the contrast between cities and rural areas.

 

 

Choropleth Mapping Guidelines

 

• Choropleth maps work best when showing just one variable. This variable could be the difference between two variables (e.g. the change of the unemployment rate from last year to this year). But if you want to show the correlation between values, choropleth maps might be not your best choice. Consider a dotplot or scatterplot instead.

• Make sure to use the right color scheme for your data. There are three different kinds of color schemes for maps: Sequential (e.g. from bright blue to dark blue), diverging (e.g. from red via white to blue) and qualitative/categorical (e.g. one green color, one blue color). Consider a sequential color scheme if you want to drive the attention to the high values, e.g. for unemployment rates. Consider a diverging scheme if you want to drive the attention to both extremes of the scale, e.g. too show the difference in votes between two competing parties. With any color scheme, do use colorblind-friendly colors.

 

• Choropleth maps are great to see the big picture, but not for subtle differences. Readers will have a hard time perceiving the small differences between colors on your map. In addition, the intervals between the colors are not necessarily the same intervals between the values in your data.

 

• Make sure there is a lightness difference in your sequential/diverging color schemes. A gradient from a light color to a dark color enables your readers to quickly spot the regions with low and high values. Using different colors can increase the contrast, but shouldn’t be overdone. In the case of a diverging color scheme, the color in the middle should be the lightest one and the extremes should be the darkest ones. If you’re unsure, use the Datawrapper defaults or use the ColorBrewer palettes.

 

Choropleth Mapping Best Practices

 

 

Examples of Choropleth Maps

 

• The Guardian: EU referendum: full results and analysis Shows the use of a cartogram (a big yellow London) and scatterplots for geographical data.

 

• Berliner Morgenpost: Where the population of Europe is growing – and where it’s declining Demonstrates the importance of small units to see regional patterns.

 

• Berliner Morgenpost: Alle Stimmen der 1779 Wahllokale A election results map that shows that it can be ok to have more than three color hues if readers know the color encoding.

 

Dot Density Mapping

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Dot density maps are a simple yet highly effective way to show density differences in geographic distributions across a landscape. Dot density maps show us intuitively where things clump or cluster. There are two basic types: one-to-one dot density maps (one dot represents one object or count) and one-to-many dot density maps in which one dot stands for a number of things or a value (e.g., 1 dot = 1,000 acres of wheat production).

 

All dot density maps must be drawn on an equal area map projection. This is critical — using a map projection which does not preserve the size of areas will distort the perceived density of the dots.

 

Advantages of dot density maps

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1. you can map raw data / simple counts (e.g., number of farms) or rates and ratios (e.g., number of farms per sq kilometer)

2. your data does not need to be tied to enumeration units and hence some of the concerns inherent in choropleth maps can be side-stepped with dot density maps

3. dot density maps work fine in black and white, when color is not an option

 

Limitations of dot density maps

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1. users cannot retrieve rates or numbers from the map directly (you need to add numbers directly on the map or provide a table to accompany the map for your readers)

2. although most dot density maps distribute dots randomly, map readers may potentially infer dot locations as precise locations of the phenomenon being mapped (e.g., the actual exact location of a person)

 

Proportional symbol maps

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Proportional symbol maps scale the size of simple symbols (usually a circle or square) proportionally to the data value found at that location. The larger the symbol, the “more” of something exists at a location. The most basic method is to scale the circles directly proportionate to the data so that if, for example, Toronto has twice the population of Vancouver, the population symbol for Toronto will have twice the area. However, you can also group your observations into categories or numerical ranges and created graduated symbol maps that may, for example, only have three symbol sizes corresponding to three categories of city size (e.g., cities of <1 million, 1-4 million, and over 4 million people).