I've finished the first three and have several more in production. This batch is derived from San Francisco Open Data sets.
This Instructable will give an overview of my creative and technical process for making these and I hope will encourage others to think about creative data visualization techniques.
At some point, I will likely share my code, but right now this project is too fresh and the code is too rough for public consumption.
Step 1: Find the Dataset
However, in most cases the data that I want is simply unavailable, so instead, I work with what I can get.
What is now becoming accessible is loads of data from city governments — San Francisco leads the way with its Open Data Portal with an API powered by Socrata. Since people have a daily relationship with their urban environment and connect to data patterns that reflect the city they live in, this municipal data can be compelling.
For the first 3 Data Crystals, I chose from the SF Open Data site: construction permit data, incidents of crime and SF Civic Art Collection.
Step 2: Extract and Parse
I wrote utility code in Java, which goes through each dataset and converts the CSV to a JSON format, keeping just the fields I want. At a minimum, I'm looking for some way to map (x, y, z) coordinates. These contain geo-spatial locations, which I translate to (x, y) values. The z-value is usually time.
I can also extract a dimension value — such as the number of units in a construction permit — and hence the "size" of each datum.
Step 3: What Does Data Look Like?
In the case of open data, I experimented with many shapes and came up with simple cubes, aligned on the same orientation. The white ones (VeroWhite resin) seemed to resonate both in my mind and those of colleagues when I showed it to them.
Step 4: Map Into 3D Space
The first image is the construction permit data — as you can see there is a lot of building in the southern part of the city, such as the Mission District. The lone dot is Candlestick Park, which is to be converted into housing units.
The second one is the SF Civic Art Collection data. Many art pieces are located in the same places such as City Hall, hence the vertical columns. And that column on it's own...that's the San Francisco Airport.
Step 5: Do Many, Many Samples
I kept returning to my central question of what does data look like as a guide and tweaked my code to give a better form to the data.
Step 6: Run Clustering Algorithms
Step 7: Patch for Structural Integrity
Using a combination of MeshLab (good for quick inspection) and 123D Design (good for adding material), I fix up any weak structural points. Usually there is no more than two spots of question, but the last thing I want is for the Data Crystal to break because it is too fragile.
Step 8: 3D Print and Clean
With all the nuances and contours of the Data Crystal, there is a lot of cleaning required. I soak it in water overnight, I pick away at it with dental tools and I use a high-pressure water jet to blast out the support gunk.
Step 9: Mount on Wood
I carefully drilled into the base of the 3D sculpture, which has to be done by hand and then I press-fit it onto the wire. I did the holes for the base on a drill press.
Step 10: Done!
- SF Civic Art Collection
- Development Pipeline (a.k.a. construction permits)
- Incidents of Crime (over a 3 month period).
I hope this was inspiring and an alternate approach to Data Visualization
For more Data Crystals and other projects, you can find me here: @kildall or www.kildall.com/blog