Stefanie Posavec is a designer, artist, and author. Her mission is to push different ways in which data can be communicated to a diverse range of ages and audiences. What especially intrigued me about her is that her projects are “wearable or danceable, be found in hospitals, museums, or on television.”
An example of the wearable aspect of her work is “Air Transformed: Better with Data Society Commission”. This series is a collection of wearable air quality data, initiating a conversation about air pollution. My favorite piece is “Seeing Air”. The projects consists of 3 pairs of glasses, each pair demonstrating the levels of air pollution on a different day in Sheffield in 2014. Each pair of sunglasses also signifies a different type of pollutant: nitrogen dioxide, small particulates, and large particulates.
This program collected various data sets concerning COVID and its transmission. Studio NAND created this software to bring into perspective the true impact of COVID without false information or exaggerations. Using this application people can understand not only how quickly COVID can spread and how it spreads, but they can also see its effects on those who are vaccinated against those who are not. Users also have the option of choosing the vaccination rate to allow them to observe the influences of such a rate in a population. Overall, I think this software is a great tool that allows people to understand COVID transmission and the importance of vaccination. With so much misinformation involved in this pandemic, along with those who are apathetic, I believe simulations such as this can bring light to how important it is to understand how much of an effect the decisions a single individual can have on the population.
Jen Lowe, an artist and researcher, through her creative technology studio Maximal Expression, creates artistic data visualizations and looks at new ways to represent data that could open up brand new paths in data visualization. For example, her work “early particle flow lidar wind visualization,” is an incredibly smooth wind flow visualization. From the current wind flow diagrams in Architecture, which are static and more general wind visualizations, Lowe’s work opens up a new world for wind flow diagrams. The brightly colored work was made by taking raw, remotely sensed lidar data, filtering it, and putting it through an algorithm that creates wind vectors. The switch from static to animated flow fields inspires lots of new possibilities, personally, about integrating dynamic data visualizations with architecture to make it easier to read. I love how smoothly the colors and lines change, different from the usual static data visualizations. Lowe’s creativity in data visualization is seen all throughout her projects, but this one specifically sparked my interest due to its possible implementations in architecture.
Stefanie Posavec is an artist, designer, writer, teacher, and data visualizer who finds satisfaction in representing large amounts of data in physical and experiential artworks. Her artistic practice involves collecting data manually and gradually, and although laborious, I personally really admire artists who enjoy working manually in conjunction with digitally produced components, in contrast to those who find the quickest (often fully digital) approach. Posavec’s process is extremely experimental and takes many forms, whether that is through dance, hopscotch, fine art, sculpture, auditory or wearable art, she never is tied down to one singular style or medium. However, her overarching goal, woven into each project, is to make the layperson understand and connect to data, even if they have never had an interest or experience with any data previously.
Shimon is a musical improvisation robot developed at the Georgia Tech Center for Music Technology. The robot plays marimba following the performance of a human musician where, after hearing chords the musician plays, Shimon can then quickly take in the information and play a tune similar to what a human might improvise if given the same task. Jazz music, though known for its improvisation and “randomness,” so to speak, actually follows a series of rules that can be bent/structured in certain ways to find new creative sounds. Shimon works in a similar way where, although the improvisation can seem random, it actually works based on similar “rules” or detailed algorithms/ artificial intelligence that allows him to play a beat based on those already human-developed rules for improvisation. I find this project especially interesting due to its ability to connect humanity and robots through music, while still preserving the improvisational qualities of genres like jazz.
//SRISHTY BHAVSAR
//15-104 SECTION C
//PROJECT 7
var nPoints = 500;
function setup() {
createCanvas(400, 400);
}
function draw() {
createCanvas(480, 480);
background(0);
// draw the curve
fill(154,205,40,70); // yellow green color
ranunculoid(10,20);
hypotro();
noFill();
ranunculoid(30,50);
ranunculoid(40,60);
}
//--------------------------------------------------
function hypotro() {
// Hypotrochoid
// https://mathworld.wolfram.com/Hypotrochoid.html
push();
noFill();
stroke('magenta');
strokeWeight(1);
translate(width/2 , height/2);
var x = constrain(mouseX, 0, width);
var y = constrain(mouseY, 0, height);
var a = map(x, 0, width, 60, 160); //radius of still circle
var b = map(y, 0, height, 1, 8); // radius b of rolling circle inside still circle
var h = constrain(a/2, 100, 100); //
beginShape();
for (var i = 0; i < nPoints; i++) {
var t = map( i, 0, nPoints, 0, TWO_PI); // tangent?
//PARAMETRIC EQUATIONS
x = (a - b) * cos(t) + (h * cos(((a-b)/b)*t));
y = (a - b) * sin(t) - (h * sin(((a-b)/b)*t));
vertex(x,y); // connected to center vertex
}
endShape();
pop();
}
//--------------------------------------------------
function ranunculoid(xsize,ysize) {
// https://mathworld.wolfram.com/Ranunculoid.html
push();
stroke('green');
strokeWeight(1);
translate(width/2 , height/2);
var x = constrain(mouseX, 0, width);
var y = constrain(mouseY, 0, height);
var a = map(mouseX, 0, width, xsize, ysize);
beginShape();
for (var i = 0; i < nPoints/10; i++) {
var t = map(i, 0, nPoints/10, 0, TWO_PI);
x = a * (( 6 * cos(t)) - cos(6*t))
y = a * (( 6 * sin(t)) - sin(6*t))
vertex(x, y);
}
endShape();
pop();
}
Stefanie Posavec is an artist and designer experimenting and researching ways to visualize and communicate computational data to all ages and audiences. What I like and find most special about Stefanie’s work is that it is very colorful, fun, and visually appealing. Usually when I think of computational data, I don’t necessarily imagine something animated, cartoon-like, or age friendly. Stefanie’s visualizations deliver themselves like artwork in a children’s book. In her work, “Drawing the Dictionary,” Stefanie responds to a theme of collections including parts of speech, syllables, first letter of the words, and alphabet. The words in the collection are specifically the 1000 most frequently used words. And used 1000 different colored index cards for each word. The words paper colors were organized by part of speech, background shape by syllables, placement by how frequently they are used, pattern by part of speech, and the pen color by first letter in word. In her work, “Writing without words,” Stefanie visually showcased text in order to highlight the similarities and differences in writing styles when comparing different authors. She wanted to create a data visualization that was straight forward and one with a subject that was emotionally connecting to humans. To create this, she focused on a book called “On the Road,” by Jack Kerouac and its themes. She did not want to use expressive typography or imagery but rather a subjective representation of information. She wanted the graphics to be quantifiable. She gathered data related to the english language structure.She wanted to visualize the novel as a living element full of energy and thus the novel is split into chapters, paragraphs, sentences, and words like a plant structure. Shorter sentences mean a choppy text and stiff writing style while longer sentences mean they are more free and leisurely. She gathered all data by hand and used math and a calculator to find final sums. Finally she used a computer graphics program instead of a data information one to watch the data generate bit by bit.
I connected mouseX and mouseY to the mapping of t to visualize the continuous movement of the curves and the larger shapes they create. It is interesting as you can watch it wind itself, giving a Spirograph effect. I then connected mouseX to a of the ranunculoid which affects the scaling from left to right. It is interesting how both x and y on the canvas determines the complexity of the shapes created. I also had fun experimenting with the different ways the shapes could be shown and decided to have the hypocycloid made of small squares.
// Rachel Legg / rlegg / Section C
//# of point of each shape
var nPoints = 150;
function setup() {
createCanvas(480, 480);
frameRate(10);
}
function draw() {
//draw the ranunculoid & astroid curve
background(0, 38, 38); //dark green-blue
fill(255);
push();
translate(width/2, height/2);
fill(121, 33, 250); //purple
drawRanunculoid();
noFill();
drawHypocycloid();
scale(1/12);
fill(105, 20, 235); //darker purple
drawRanunculoid();
pop();
}
function drawRanunculoid() {
//Ranunculoid : https://mathworld.wolfram.com/Ranunculoid.html
var x;
var y;
var a = 30;
stroke(149, 198, 35); //green
strokeWeight(1);
beginShape();
for(var i = 0; i < nPoints; i++) {
//map points to mouseX & mouseY for continuous flow
var t = map(i, 0, nPoints, mouseX, mouseY);
a += mouseX / 500; //scale when mouseX changes
//adjust to 7 to increase petals to 6
x = (a * (7 * cos(t) - cos(7 * t)));
y = (a * (7 * sin(t) - sin(7 * t)));
vertex(x, y); //vertex gets spinograph effect
//line(x - 5, y - 5, 10, 10); //visually different. shows lines to center
}
endShape(CLOSE);
}
function drawHypocycloid() {
//https://mathworld.wolfram.com/Hypocycloid.html
var x;
var y;
var a = 110;
var b = 30;
noFill();
stroke(212, 223, 158); //light green
strokeWeight(1);
for(var i = 0; i < nPoints; i++){
//map points to mouseX & mouseY for continuous change
var t = map(i, 0, nPoints, mouseX, mouseY);
x = ((a - b) * cos(t)) - (b * cos(t * ((a - b) / b)));
y = ((a - b) * sin(t)) - (b * sin(t * ((a - b) / b)));
//rectangle shapes to show patterns
rect(x - 5, y - 5, 10, 10);
}
}
Project Ukko visualizes wind patterns and helps predict future patterns, creating a tool for clean wind energy industry and the fight against climate change.
Project Ukko visualizes and helps predict future wind patterns which can be helpful in clean energy endeavors in the wind farm industry. Moritz Stefaner, a designer and researcher who focuses on data visualization, encoded prediction skills into visuals in Project Ukko to make this data accessible to others. Opacity represents precision accuracy, wind strength connects to line thickness, and trends of wind speed change with line tilt and color. The color, visual language, and design is really well done and very user-centered, making it easy for anyone to begin understanding and deciphering patterns. I appreciate that this gives experts a practical tool, along with sharing new techniques into how to share data. This browser allows people to interact and find out how the wind will change over the next few months. I really admire this project because it makes information more accessible to people, along with considering how code can be used in the fight against climate change.
A particular computation information visualization project that I found and admire is Periscopic’s “U.S. Gun Deaths” visualization. This project was overseen by Kim Rees, who is a co-founder of Periscopic. This project was created in 2013, and has been continually updated through 2018 data about gun deaths. This project uses data from U.S. Law Enforcement that voluntarily reported gun homicide deaths. The data does not include suicide deaths by guns. What I admire most about this project is the simplicity of it. It isn’t a hard graph of data to look at visually, nor is it hard to grasp what it is showing. The data is shown prominently with the total people killed on one side counting up, and the years that were stolen on the other side. I liked showing the years stolen as it is a very large number and really highlights the impact guns have on people in the US, specifically male black Americans. The graph also shows what age each person was killed at, and the predicted age they could have lived to. The last figure is interesting as it highlights how many years they could have had to live. They calculated that data based on the morality rate data from the WHO. I am not sure what algorithms they used to generate the work, but I think it was mostly just loops for each set of data they had, and then a counter for each value they wanted to keep track of. The creator’s artistic sensibilities of illustrating what potential was lost due to gun deaths, is very prominent and apparent in the final form. The colors highlight the flames of life that were extinguished by guns, and how far they could have gone with the arcs of life. It is a very easy graph to view as well and to understand, making it easily digestible to the regular audience.
U.S. Gun Deathsin 2018 by Periscopic, created in 2013
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