GMU:Speculative Atmospheres II/Sophia Amelia Eickhoff: Difference between revisions

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[[File:Screenshot (48).png|400px]]
[[File:Screenshot (48).png|400px]]


After each walk the collected Data will be read out and oil painted onto a cardboardcanvas.
*Time/Duration= Size of the canvas
*Time/Duration= Size of the canvas
*Route= Shape of the line
*Route= Shape of the line
Line 19: Line 21:
*Air quality= contour of the line
*Air quality= contour of the line
*Pulse= Height of the line  
*Pulse= Height of the line  
*Humidity=   
*Humidity=  Thickness of the line
*(distance= space around the line)  
*(distance= space around the line)  



Revision as of 18:20, 14 December 2021

Idea

Mapping of the Invisible Enviorment

For my project I want to collect the invisible elements (data) that shape the natural atmosphere on my walks and visualize them artistically.

On the route, light, air quality, temperature, pulse and humidity are measured every minute using an Arduino and sensors. These are stored via an SD card and read out with a code. The time and route are stored using a tracking app.The data determines the parameters of the subsequent image design. For transport, a "rack" is built that attaches the breadboard to the body.

Screenshot (48).png


After each walk the collected Data will be read out and oil painted onto a cardboardcanvas.

  • Time/Duration= Size of the canvas
  • Route= Shape of the line

Linearroute.png

  • Temperature= Color
  • Light= Saturation of the color
  • Air quality= contour of the line
  • Pulse= Height of the line
  • Humidity= Thickness of the line
  • (distance= space around the line)


  • (Background= Photograph of the Walk)

Hintergrund.png



Additional

The place and the area of the walked route are indicated. Depending on what dominates the walk, a text (internal state) or a photo (external state) is attached. Also, an attempt is made to put this state in context with the collected data.


Keywords

  • Determination-Indetermination
  • History of Mapping
  • Strollology
  • Correlation of Data
  • Context/ Asssociation of Color