Visualization In Python V : Geoplots, Dot Map, Connection Map, Choropleth Map

Geoplots

Geological plots are a great way to visualize geospatial data. Choropleth maps can be used to compare quantitative values for different countries, states, and so on. If you want to show connections between different locations, connection maps are the way to go.

1. Dot Map

In a dot map, each dot represents a certain number of observations. Each dot has the same size and value (the number of observations each dot represents). The dots are not meant to be counted; they are only intended to give an impression of magnitude. The size and value are important factors for the effectiveness and impression of the visualization. You can use different colors or symbols for the dots to show multiple categories or groups.

Diagram shows a dot map where each dot represents a certain amount of bus stops throughout the world.

Design Practices

  • Do not show too many locations. You should still be able to see the map to get a feel for the actual location.
  • Choose a dot size and value so that in dense areas, the dots start to blend. The dot map should give a good impression of the underlying spatial distribution.

2. Choropleth Map

In a choropleth map, each tile is colored to encode a variable. For example, a tile represents a geographic region for counties and countries. Choropleth maps provide a good way to show how a variable varies across a geographic area. One thing to keep in mind for choropleth maps is that the human eye naturally gives more attention to larger areas, so you might want to normalize your data by dividing the map area-wise.

Diagram shows a choropleth map of a weather forecast in the USA

Design Practices

  • Use darker colors for higher values, as they are perceived as being higher in magnitude.
  • Limit the color gradation, since the human eye is limited in how many colors it can easily distinguish between. Seven color gradations should be enough.

3. Connection Map

In a connection map, each line represents a certain number of connections between two locations. The link between the locations can be drawn with a straight or rounded line, representing the shortest distance between them.

Each line has the same thickness and value (the number of connections each line represents). The lines are not meant to be counted; they are only intended to give an impression of magnitude. The size and value of a connection line are important factors for the effectiveness and impression of the visualization.

You can use different colors for the lines to show multiple categories or groups, or you can use a colormap to encode the length of the connection.

Diagram shows a connection map of flight connections around the world

Design Practices

  • Do not show too many connections as it will be difficult for you to analyze the data. You should still see the map to get a feel for the actual locations of the start and end points.
  • Choose a line thickness and value so that the lines start to blend in dense areas. The connection map should give a good impression of the underlying spatial distribution.

Geoplots are special plots that are great for visualizing geospatial data. In the following section, we want to briefly talk about what’s generally important when it comes to creating good visualizations.

What Makes a Good Visualization?

There are multiple aspects to what makes a good visualization:

  • Most importantly, the visualization should be self-explanatory and visually appealing. To make it self-explanatory, use a legend, descriptive labels for your x-axis and y-axis, and titles.
  • A visualization should tell a story and be designed for your audience. Before creating your visualization, think about your target audience; create simple visualizations for a non-specialist audience and more technical detailed visualizations for a specialist audience. Think about a story to tell with your visualization so that your visualization leaves an impression on the audience.

Common Design Practices

  • Use colors to differentiate variables/subjects rather than symbols, as colors are more perceptible.
  • To show additional variables on a 2D plot, use color, shape, and size.
  • Keep it simple and don’t overload the visualization with too much information

With this we have come to end of this visualization series, I hope you enjoyed it and learnt something valuable…See you in the next one.

If you missed previous posts then its here.

And yes you connect with me for more awesome contents. 😀

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CEO Techneophyte | Python Developer | ML Engineer | Data Scientist | Flutter Developer | Penetration Tester | Software Engineer at Infosys

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Abhijeet Srivastav

Abhijeet Srivastav

CEO Techneophyte | Python Developer | ML Engineer | Data Scientist | Flutter Developer | Penetration Tester | Software Engineer at Infosys

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