Image Data Visualization

Discover what you can do with image data visualization and try it yourself.

Data visualization typically involves the generation of images and statistics. There are certain cases where we may want to extend this analysis and visualize different types of images. We'll take a brief look at a few of these examples below:

  • Visualizing maps

  • Using icons for comparisons

  • Using design guidelines from images to structure content

Visualizing maps

Overlaying geographical information over a map is a popular technique data storytellers use to bring external visualizations to their plots. In the below example, we reference an example of a Plotly Bubble Map with the Gapminder dataset. Here, the size of the bubbles indicate the population of different continents in 2007, while the ISO Alpha codes ISO Alpha codes: Internationally recognized codes that designate every countryprovide the geolocation information.

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import plotly.express as px
df = px.data.gapminder().query("year==2007")
fig = px.scatter_geo(df, locations="iso_alpha", color="continent",
hover_name="country", size="pop",
projection="natural earth")
fig.write_image(file="output/image.png") #Save the image to display
fig.show()

Icons

The use of icons or pictograms can, at times, convey visual information more simply than even the simplest visualization can. Consider the following visualization, which uses icons of people to demonstrate a customer satisfaction score.

Using pictograms to convey data
Using pictograms to convey data

Note: When constructing pictograms (e.g., for infographics), the statistics for the plot, such as 6/8 or 2/8 values, are computed on the dataset and the icons are typically created using a drawing tool.

As an alternative to a bar plot, which would require labels to be added at the top of the bars to convey the values, this visualization can effectively convey the statistics, i.e., the ratio of the number of unsatisfied to satisfied customers, with colors and icons.

Using design guidelines

Brilliant data storytelling pieces effectively leverage layout, composition, and other elements to guide the audience's attention from one component of the narrative to another, such as using the distance between text to indicate gaps between certain concepts as part of an infographic or visualization.