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Case study content:
Work
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MeteoGenerative
MeteoGenerative
Meteo Generative is a generative artwork and data visualization piece that uses real-time weather data to create abstract geometric representations. Installed in multiple exhibitions, this project invites viewers to take a meditative approach to technology by observing and interpreting its evolving visual language.
Meteo Generative is a generative artwork and data visualization piece that uses real-time weather data to create abstract geometric representations. Installed in multiple exhibitions, this project invites viewers to take a meditative approach to technology by observing and interpreting its evolving visual language.
Year :
Year :
2020
2020
Transforming Weather Into Generative Art
Transforming Weather Into Generative Art
The project was born from a desire to transform environmental data into a poetic visual experience. A weather sensor collecting temperature, barometric pressure, and humidity serves as the data source, reflecting the space where the installation stands. The abstract visuals require patience and contemplation to interpret—a response to the fast-paced, gratification-driven interactions of modern technology.
The project was born from a desire to transform environmental data into a poetic visual experience. A weather sensor collecting temperature, barometric pressure, and humidity serves as the data source, reflecting the space where the installation stands. The abstract visuals require patience and contemplation to interpret—a response to the fast-paced, gratification-driven interactions of modern technology.
The Concept: Turning Real-Time Weather Data Into Visuals
The Concept: Turning Real-Time Weather Data Into Visuals
The Concept: Turning Real-Time Weather Data Into Visuals
When developing Meteo Generative, the visual language took a backseat to the project’s core concept of "slow data." I chose a geometric style, focusing on basic shapes—particularly circles. At the time, I was intrigued by the simplicity and versatility of fundamental forms and wanted to explore how they could be manipulated to convey meaning.
Using circles felt natural for a few reasons. First, their simplicity made them easier to program in Processing, allowing me to focus on the behavior and interaction of the shapes rather than overly elaborate details. Second, the geometric approach helped the graphics remain abstract but still legible enough for viewers to start recognizing patterns over time.
The visualizations responded dynamically to the weather data, with the shapes changing in color, size, and spacing based on temperature, barometric pressure, and humidity readings. These shifts offered a meditative experience, encouraging patience as viewers began to piece together the relationships between the visuals and the environment.
Rather than sketching out ideas first, I worked directly in Processing, experimenting with different configurations until I found ones that resonated with the project’s goals. This hands-on, iterative approach allowed me to keep the visuals cohesive with the broader intention of the artwork.
When developing Meteo Generative, the visual language took a backseat to the project’s core concept of "slow data." I chose a geometric style, focusing on basic shapes—particularly circles. At the time, I was intrigued by the simplicity and versatility of fundamental forms and wanted to explore how they could be manipulated to convey meaning.
Using circles felt natural for a few reasons. First, their simplicity made them easier to program in Processing, allowing me to focus on the behavior and interaction of the shapes rather than overly elaborate details. Second, the geometric approach helped the graphics remain abstract but still legible enough for viewers to start recognizing patterns over time.
The visualizations responded dynamically to the weather data, with the shapes changing in color, size, and spacing based on temperature, barometric pressure, and humidity readings. These shifts offered a meditative experience, encouraging patience as viewers began to piece together the relationships between the visuals and the environment.
Rather than sketching out ideas first, I worked directly in Processing, experimenting with different configurations until I found ones that resonated with the project’s goals. This hands-on, iterative approach allowed me to keep the visuals cohesive with the broader intention of the artwork.
The visualizations responded dynamically to the weather data, with the shapes changing in color, size, and spacing based on temperature, barometric pressure, and humidity readings.
The visualizations responded dynamically to the weather data, with the shapes changing in color, size, and spacing based on temperature, barometric pressure, and humidity readings.
Crafting Meteo Generative: From Concept to Reality
Crafting Meteo Generative: From Concept to Reality
Turning this vision into reality required equal parts creativity and technical know-how. The installation consists of four sleek boxes made from black acrylic glass. Inside each box is an LED matrix screen, programmed to display generative graphics that evolve in response to real-time weather data.
One challenge was optimizing the design to stay within budget. For instance, I used one weather sensor for all the Raspberry Pis instead of giving each its own. Another technical hurdle was ensuring the system could boot directly into the visuals at the push of a button, making it easy to operate.
Turning this vision into reality required equal parts creativity and technical know-how. The installation consists of four sleek boxes made from black acrylic glass. Inside each box is an LED matrix screen, programmed to display generative graphics that evolve in response to real-time weather data.
One challenge was optimizing the design to stay within budget. For instance, I used one weather sensor for all the Raspberry Pis instead of giving each its own. Another technical hurdle was ensuring the system could boot directly into the visuals at the push of a button, making it easy to operate.
A weather sensor is connected to one Raspberry Pi, designated as the server.
This server shares the data with three other Raspberry Pis over a closed wireless network.
The visuals, coded in Processing, are sent to the LED matrices via Python scripts running on the Raspberry Pis.
A weather sensor is connected to one Raspberry Pi, designated as the server.
This server shares the data with three other Raspberry Pis over a closed wireless network.
The visuals, coded in Processing, are sent to the LED matrices via Python scripts running on the Raspberry Pis.
Exhibition Highlights and Reception
Exhibition Highlights and Reception
The first time Meteo Generative was exhibited was in 2020 at Kunstverein Leverkusen Schloss Morsbroich, as part of my solo exhibition, Crossroad. I displayed all four boxes in a single, uninterrupted line, creating a continuous visual flow.
In 2022, the project was exhibited again at K19. Due to space limitations, I could only display three boxes, but I maintained the same linear arrangement to preserve the coherence of the visuals.
Seeing the work in these different spaces was a rewarding experience. Each location brought its own context and audience, shaping how the work was perceived and interacted with.
One of the most interesting aspects of the project was observing how people engaged with it.
Some were immediately drawn to the hypnotic, abstract visuals and simply enjoyed them as aesthetic objects. Others took their time, trying to decipher the connection between the graphics and the weather.
What stood out to me the most were the reactions of people who revisited the installation at different times. They noticed how the visuals changed based on the weather, which deepened their appreciation and curiosity about the work.
The first time Meteo Generative was exhibited was in 2020 at Kunstverein Leverkusen Schloss Morsbroich, as part of my solo exhibition, Crossroad. I displayed all four boxes in a single, uninterrupted line, creating a continuous visual flow.
In 2022, the project was exhibited again at K19. Due to space limitations, I could only display three boxes, but I maintained the same linear arrangement to preserve the coherence of the visuals.
Seeing the work in these different spaces was a rewarding experience. Each location brought its own context and audience, shaping how the work was perceived and interacted with.
One of the most interesting aspects of the project was observing how people engaged with it.
Some were immediately drawn to the hypnotic, abstract visuals and simply enjoyed them as aesthetic objects. Others took their time, trying to decipher the connection between the graphics and the weather.
What stood out to me the most were the reactions of people who revisited the installation at different times. They noticed how the visuals changed based on the weather, which deepened their appreciation and curiosity about the work.
What stood out to me the most were the reactions of people who revisited the installation at different times. They noticed how the visuals changed based on the weather, which deepened their appreciation and curiosity about the work.
What stood out to me the most were the reactions of people who revisited the installation at different times. They noticed how the visuals changed based on the weather, which deepened their appreciation and curiosity about the work.
Reflection: Project Challenges and Future Possibilities for Data Art
Reflection: Project Challenges and Future Possibilities for Data Art
Every project comes with its own set of challenges, and Meteo Generative was no different.
One major challenge was cost. Weather sensors and LED matrix displays aren’t cheap, so I had to carefully design the system to optimize the use of resources without compromising the vision.
Technically, making the system easy to use was another significant task. Exhibitions can be hectic environments, and I wanted to ensure that anyone could operate the installation without needing a technical background. Writing clear instructions and designing a smooth startup process were crucial parts of the solution.
Finally, there was the challenge of communicating the concept to the audience. While people appreciated the aesthetics of the visuals, many didn’t immediately understand what they were looking at. This led me to provide written and verbal explanations, which added an interesting layer to the experience.
Every project comes with its own set of challenges, and Meteo Generative was no different.
One major challenge was cost. Weather sensors and LED matrix displays aren’t cheap, so I had to carefully design the system to optimize the use of resources without compromising the vision.
Technically, making the system easy to use was another significant task. Exhibitions can be hectic environments, and I wanted to ensure that anyone could operate the installation without needing a technical background. Writing clear instructions and designing a smooth startup process were crucial parts of the solution.
Finally, there was the challenge of communicating the concept to the audience. While people appreciated the aesthetics of the visuals, many didn’t immediately understand what they were looking at. This led me to provide written and verbal explanations, which added an interesting layer to the experience.
There was the challenge of communicating the concept to the audience. While people appreciated the aesthetics of the visuals, many didn’t immediately understand what they were looking at.
There was the challenge of communicating the concept to the audience. While people appreciated the aesthetics of the visuals, many didn’t immediately understand what they were looking at.
Working on Meteo Generative reinforced my passion for data visualization and generative art. I love the idea of using data to tell a story or create an experience, and I’m excited to explore this further in future projects.
One area I’d like to experiment with is integrating human interaction into such works. How might viewers not just observe but also influence the visuals? I’m also intrigued by the possibilities of AR and VR, which could open up entirely new dimensions for this kind of work.
Above all, I want to keep creating projects that invite people to slow down, observe, and interpret. In a world that often values speed and convenience, I think there’s something powerful in asking people to take their time.
Working on Meteo Generative reinforced my passion for data visualization and generative art. I love the idea of using data to tell a story or create an experience, and I’m excited to explore this further in future projects.
One area I’d like to experiment with is integrating human interaction into such works. How might viewers not just observe but also influence the visuals? I’m also intrigued by the possibilities of AR and VR, which could open up entirely new dimensions for this kind of work.
Above all, I want to keep creating projects that invite people to slow down, observe, and interpret. In a world that often values speed and convenience, I think there’s something powerful in asking people to take their time.
One area I’d like to experiment with is integrating human interaction into such works. How might viewers not just observe but also influence the visuals? I’m also intrigued by the possibilities of AR and VR, which could open up entirely new dimensions for this kind of work.
One area I’d like to experiment with is integrating human interaction into such works. How might viewers not just observe but also influence the visuals? I’m also intrigued by the possibilities of AR and VR, which could open up entirely new dimensions for this kind of work.
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