Madeleine Crouch
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I. Seattle City Sweater
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II. Cellular Automata for Urban Planning
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III. Selected Sketchbook Works
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Seattle Center Sweater
Inspiration This piece explores the tension between two of the most iconic buildings in Seattle: the Space Needle and MoPOP, previously known as the EMP Museum. I varied fit, shape, texture, and color to evoke both the symmetrical, exploratory, flourished angles of the Needle, and the asymmetrical, patchwork, radically abstract organic forms of MoPOP. The emergent concept is a patchwork of abstract organic shapes, unified by the long, columnar legs of the Needle.
I created the above swatches to test the different stitches, yarns, and textures I incorporated into the sweater. Analogous to building models, swatches let me determine at a small-scale level if it is necessary to swap out any materials, choose different colors, or use a bigger or smaller knitting needle to obtain a different gauge. Below are different fit concepts and a study of one of the legs of the Needle.
The basketweave around the base of the sweater represents Seattle's many dark, rectangular buildings, while the broken cables worked in blue up the wearer's right sleeve were inspired to the broken guitar frets on the roof of MoPOP. The variegated color bands on the wearer's left sleeve and the competing red strands that form the neck drape panel expand on MoPOP's iridescent finishes, reminding viewers of both pieces that music and culture are in constant flux, and that impressions of either vary with physical, intellectual, or emotional separation from the subject matter.
Urban Planning with Cellular Automata
This project explores the application of a modified set of cellular automaton rules to cells based on Seattle's 2010 census blocks. The seed, or starting position, is the population of each block as of 2010 (right; shown capped at 200 people/block). My goal is to examine the growth of a simplified model of the city, including which areas gained or lost population. In its classic form, Conway's game of life is a programmable cellular automaton played out upon an 8-connected grid of live and dead cells. Interactions with the game are confined to setting an initial configuration and a set of rules, then observing how the configuration changes over time. This means that the output at any particular time depends entirely upon the initial input. Classic rules (abbreviated to B3/S23 in automata notation): - Live cells with less than 2 or more than 3 live neighbors die - Live cells with either 2 or 3 live neighbors live - Dead cells with 3 live neighbors live An example using these rules can be found below. Starting from a simple seed (leftmost image), each arrow represents one application of these rules to the entire neighborhood. The rules are applied four times before the configuration resolves to a "still life" (fifth image from left), and no further changes occur. Conway's game of life also holds a personal significance for me, because it was the first substantial program I ever implemented.
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I was the most interested in how the downtown census blocks would evolve under the modified rules, since these blocks have the highest population density. The left images depict a case study of successive applications of the following rules: - Live cells whose total neighbor population density >35.0 lose 5% of their population - Live cells whose neighbor density is <7.6 gain 10% of their existing population - Dead cells, or those with a population of 0, stay dead The density thresholds were chosen experimentally to produce gradual, realistic changes. One departure from the classical rules is that dead cells stay dead, which I deemed necessary since cells with zero population are likely industrial areas, highways, or other uninhabitable areas. An interesting side-effect of this change is detailed on the next page. The other modification is that the population of live cells is adjusted rather than zeroed, which was necessary since the classical rules only allow one occupant per cell. Modeling each resident of Seattle as their own cell was not feasible due to computational restrictions. Past about step 60, it is difficult to see any changes in the population density heatmaps. This is because population increases are confined to the blocks that already have densities of greater than 200 people/block in the later stages of the program. Nevertheless, the population of the area continues to increase until around step 112, as can be seen in the above graph, when the seed resolves to a still life. The below image shows which blocks lost population (red) and which gained population (green).
An interesting artifact of the modified rules can be seen above. In lower-density suburbs of Seattle, the rules appear to concentrate large populations into alternate squares in a checkerboard pattern. This likely occurs because the code only checks the density of all neighboring blocks before deciding whether to change the population. The rule capping the neighbor density decreases the population of the less-dense blocks, while the rule checking for sparse neighboring density increases the population of the already densely-populated blocks. This eventually results in the checkerboard pattern of super-dense blocks interspersed with blocks with zero population. This artifact could be eliminated by capping the total density allowed in each block. It is my suspicion that this does not occur in the downtown region because there are a large number of blocks with zero population that break up the pattern. Unlike in the case study of the downtown region, this pattern does not resolve to a still life after 112 iterations, and the population continues to increase (below).
Selected Sketchbook Works
Left: portrait of David Tennant as Kilgrave in Jessica Jones; above: sea otter anatomy studies; below: freehand study of Seattle's Pacific Tower
Thank you. Contact: madeleinehcrouch@gmail.com (720) 352-2089 2950 N Green Valley Parkway Apt 2531 Henderson, NV 89014