Art Meets Cville Open Data


Some of first things that come to mind when we think of open data are tables, charts, and maps, where digital facts and figures are transformed to graphics for printing on paper or visualization on digital screens. This may be true in practice, but it is a limited vision for our data. Our numbers, codes, and text need not be relegated to these basic forms and displays, but can serve as the elements of sophisticated artistic design.

Take this interesting picture …

A view on despair image A View on displair

It can be viewed and appreciated as a lovely, abstract cityscape. But the drawing tells a more somber and deeply personal story. In the words of the artist, Sonja Kuijpers, “You might be wondering what you are viewing here. This landscape, each element in it represents a person who committed suicide in the Netherlands in the year 2017.”  View the original image from Kuijpers’ website, posted with permission.

Kuijpers has taken data on Dutch national data suicide by age, sex, and method and converted it into graphical elements. Trees represent hanging; waves, drowning; clouds, taking drugs/alcohol/medicines; buildings, jumping from a height; and stars an unknown method. As she states, “The categories are split into 8 age-groups between < 20 years and > 80 years. Differences in colour, form and/or size of the elements show the different age-groups. The landscape is “split” into men and women: men on the left, women on the right.”

Kuijpers work shows us how our data need not be constrained to traditional representations, but can be transformed into art having elements with a deeper layer of meaning.

We will take a look at three other types of artistic representations of open data: sculpture, jewelry, and a musical composition. In the interest of full disclosure, I am no artist … I didn’t get the gene. These examples are provided to stir your imagination about the potential for turning open data into art and data stories.


“Streets of Downtown Charlottesville” is a three-dimensional model showing the streets of Charlottesville without other features.  Its sparse appearance resembles the ancient stick charts used by Polynesian islanders for long distance navigation.

I created the model using the Road Centerline dataset from the Charlottesville Open Data Portal.  It was generated using a 3D printer at the Northside Library of the Jefferson-Madison Regional Library (JMRL). JMRL makes the 3D printer available to local residents at no charge or will print a model for a nominal material fee. The library also offers free training on operating the equipment. JMRL offers an excellent introduction to 3D printing … from designing the objects, to creating print files, and ultimately printing the objects. You can learn about the process by creating small, single color objects.

Geographic data is ideal for 3D printing, as location defines two of the three dimensions and the third dimension is left to the designer’s imagination. The third dimension could be elevation or another statistical measure, such as population or happiness. While experimenting with 3D models, I also generated 3D models of elevation relief for Virginia and population by Census block group for Charlottesville.


“Charlottesville 2018 Temperature Differences from Normal” is a necklace showing the 2018 temperature differences from normal temperatures in degrees Fahrenheit. Each month is represented by two beads and the colors represent 2-degree differences, with below normal temperatures shown in shades of blue and temperatures above normal shown in colors from yellow through orange and red to purple. The necklace quickly shows that nine months in 2018 had above normal temperatures with two months, February and May, having temperatures over 8 degrees above normal. Only three months were below normal.

The necklace was created with data from the National Oceanic and Atmospheric Administration’s (NOAA) National Centers for Environmental Information by using their Local Climatological Data. Pony Beads and necklace colliers from Michaels served as the materials.

Temporal data collected at any regular interval lends itself to this type of presentation. It could be weather data, financial data, census data, or other data. The interval does not need to be measured in calendar time, but can be in any linear sequence. Sports data showing wins and losses or point differential for each game over a season is also suitable. In addition, this data need not only be incorporated in jewelry, but the data could become a pattern for any linear artifact such as a scarf or banner.


Our final example, a short musical composition, “BGN Decisions,” uses data from the U.S. Board on Geographic Names (BGN). The BGN is responsible for approving geographic names for the Federal Government and they publish their domestic decision minutes online.

BGN Decisions

The composition represents a selection of the names from the BGN’s decisions for March 2018. Created using the Python Pyknon module: the pitch represents a letter in the name, the musical instrument represents the type of feature (Acoustic Piano – Lake, Tubular Bells – Mountain, String Ensemble – Stream, and Glockenspiel – Other), the volume represents approval (low – no approval, high – approval), and the duration represents relative location (short – east of Mississippi, long – west of Mississippi).

A Python Charlottesville (PyCHO) Meetup on sonification inspired this activity. During the meeting, Erin Braswell (Center for Open Science) described the process of sonification, gave examples of her work, and pointed the audience to Python resources for sonification. You can download the book “Music for Geeks and Nerds” by Pedro Kroger at no cost.

Sonification lends itself well for representing multivariate data, where variables can be mapped to pitch, duration, instruments, volume … a combination that lends itself to mapping both numbers, codes, and text. The Sonification Data Handbook chapter on Audification provides an in-depth look at the conversion of data to audio.


Hopefully this short blog has convinced you to experiment with your data, going beyond tables, charts, and maps, to creating art that uses data to infuse meaning into objects and tell a story. The possibilities are limited only by your imagination … you can convert your data into different materials, textures, sizes, shapes, and colors or music with varied instruments, notes, volume and duration. Resources to get you started are available locally and inspiration lurks in unexpected places.

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