Category: data sculptures

Data Source

This data sculpture uses the global air quality dataset (https://aqicn.org/data-platform/register/), showing daily air quality readings in cities around the world. In particular, high concentrations of PM2.5, or small particles less than 2.5 microns in diameter, are known to have a number of health effects and are a common indicator of air quality.

Audience

The intended audience would be for a group of kids or students, either in a classroom setting or somewhere public (like in a museum) where many students would be able to interact with the exhibit.

Concept

In our previous participatory sketch using the air quality data set, we wanted the audience to be able to place themselves in the data by picking where they live or a location of their choice to compare air quality data. In this sketch, we take this concept to a physical level by representing air quality on a physical globe.

Air quality is represented by the color of various fingerprint mediums ranging from white to black. The mediums are simply a mixture powders (cornstarch and graphite powder) to create various shades of grey, with darker colors representing worse air quality. For this sketch, I was only able to create three different shades, but a more fully developed idea could use more granular shades and possibly ink instead of powder. The idea is that students would look up the yearly average air quality for a specific location (e.g. their hometown), and then create a fingerprint on the globe using the corresponding color/shade.

If there is a large group of students or other audience members, they would choose various locations around the world, with some overlap. As the fingerprints grow, there is an effect of ‘smog’ covering the areas with poorer air quality, whereas locations with better air quality would not see much of a change. In an actual sculpture we would likely use a globe with less coloring so that the fingerprints stand out.

The eventual gray shading of areas with poorer air quality is an impactful effect and would hopefully help visualize the global impact of pollution. In addition, there is the effect of students feeling like they participated and left a part of themselves on the sculpture, so hopefully they feel that they may be able to make an impact on the solution.

I wrote a medium post because I couldn’t post images here (file size was either too big or there was problems). Please see the post there.

Link to Post: https://medium.com/@rvunabandi/cms-631-sketch-4-6d09aff8ddaa

Link to Images: https://drive.google.com/drive/folders/124E35alBbfw_sLXhgvOKZofbh3A7tcHT?usp=sharing

For my data sculpture I looked back at my project 3, in which we explored the honeybee data set. I was particularly struck by the “loss data,” which mapped the percentage of colonies that died off per year in each state. From our project I knew that over time the amount of honeybees dying every year has steadily increased, so I checked around the data to find a state that showed a particularly drastic increase in colony loss while also hosting a large amount of colonies. Lo and behold, Texas met these criteria.

I had a bottle of honey lying around (from a beekeeper friend back in Boston!) so I used the amount of honey left in the bottle to represent the amount of bees that survived each year. While this particular metric is fairly ambiguous and can’t necessarily be gleaned just from looking at the graphic, I feel like the overall narrative that bees are dying at an alarming rate is still conveyed.

After taking the three pictures of the honey I just threw them in Photoshop. I’m not particularly happy with the background and labels but I struggled to find ways to improve them without making the picture needlessly complicated. For instance, I thought about putting the actual loss percentages on each bottle along with the year, but without a text explanation of what the percentage means it was not very powerful. In the end I decided this simple design worked best.

Using data for vehicles from the U.S. Department of Energy’s Fuel Economy measurements, I created a data sculpture to show the effect of air pollution on people’s vehicles.

In order to present this data, I used different masks to restrict breathing that aligned with a vehicle’s air pollution score. The worse the car’s air pollution, the more restrictive the mask would be. The rating is on a scale from 1-10, (with the higher rating meaning less air pollution from the vehicle) so a rating of 10 would have no mask at all as compared to a 1 having a heavy duty mask. The reason why the mask is used to represent the air pollution score is so that way people would have more trouble breathing naturally, which would happen with more and more air pollution.

This would ideally be part of an exhibit, where people who take part are given a mask that corresponds with the rating of the vehicle they arrived in to the exhibit. They would have to walk around the exhibit, constantly aware of the toll their car is having on the environment. For a less active experience, this sculpture could be more static in an exhibit, showing what different masks would look like next to various types of vehicles.

I chose to make a sculpture of my group’s analysis on the differential effects of selected climate factors on grape harvest date of French wines. In my sculpture, I represent grapes as wine bottles, precipitation as glasses of water, and temperature as candles. I map the data by varying the precipitation with the number of glasses of water uses and varying the temperature with the number of candle wicks used. My sculpture includes a timeline of grape harvest dates (January-June) and (July-December).

I try to show that between 1901 and 1980, both precipitation and temperature had statistically significant effects on changes in grape harvest dates. In particular, higher precipitation corresponded to later grape harvest dates (shift wine bottles to the right) and higher temperatures corresponded to earlier grape harvest dates (shift wine bottles to the left).

I then try to show that between 1980 and 2007, only temperature had a statistically significant effect on changes in grape harvest dates. Increasing the precipitation does not shift the wine bottles, but higher temperatures yield a strong and significant leftward shift of the wine bottles.

Data:

Grape  Harvest Dates

NOAA National Centers for Environmental Information

Climate Factors (Temperature, Precipitation, and Palmer Drought Severity Index)

Center for Environmental Data Analysis