San Diego Foursome
| CODE: |
GitHub |
| DATATYPE: |
image slices |
| SOURCE: |
DigitalGlobe, Google Streetview, Twitter, Flickr |
| PLACE: |
downtown San Diego |
| YEARS: |
1994-2016 |
| SIZE: |
1 million slices per plot |
| VIZTYPE: |
slice histogram multi |
| FEATURES: |
saturation, hue |
| FEAT SRC: |
scikit-image |
| BINS: |
1440 |
| MORE INFO: |
Software Studies Blog |
This is a slice histogram multi ("large multiples", perhaps). This format is what slice histograms are really made for: comparison. Each of the four plots is a saturation histogram, vertically sorted by hue. Each contains 1 million slices from some number of whole images, a number that varies by image types. In a slice histogram, I aim for uniformity in the average standard deviation of the color properties I'm interested in. After setting some slice size that satisfies this criterion, I then randomly sample 1 million slices (really, 1,048,576) and resize them to 16px on a side. The resulting plots are composed into what we see here. We see in this display that social media adds colors we see rarely in nature — bright, saturated blues and reds.