{"id":12681,"date":"2024-10-09T16:54:53","date_gmt":"2024-10-09T22:54:53","guid":{"rendered":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/?p=12681"},"modified":"2024-10-09T16:54:53","modified_gmt":"2024-10-09T22:54:53","slug":"large-assignment-3-data-physicalization-6","status":"publish","type":"post","link":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/2024\/10\/09\/large-assignment-3-data-physicalization-6\/","title":{"rendered":"Large Assignment 3: Data Physicalization"},"content":{"rendered":"\n

Dataset<\/h2>\n\n\n\n

For this assignment, I had a little trouble deciding on a dataset to use for my project. I explored the links that were provided and others I found independently. I felt overwhelmed at some point because there is a massive amount of data on many subjects that it’s hard to focus on a single thing. In the end, I found two datasets that were interesting to me, one related to college graduates characteristics in this website: https:\/\/ncses.nsf.gov\/surveys\/national-survey-college-graduates\/2021#data<\/a>.
There were many interesting tables with various categories such as median salaries, graduation rates based on sex, ethnicity, etc. This dataset, however, I felt was still too big. I ended settling on a data set related to the number of motor vehicles registered over time since 1900. The dataset can be found here: https:\/\/catalog.data.gov\/dataset\/motor-vehicle-registrations-dashboard-data<\/a>.
In the aforementioned dataset, there were four categories for the collection of data: autos, trucks, buses, and motorcycles. The data was collected separately for each year, and for each state within the categories mentioned. The reason I picked this dataset was because it felt more manageable with fewer categories of data. To use the data, I processed it to include the sum of the totals for every state, every 10 years. This way, I have a dataset that can be interpreted more broadly. Admittedly, this leaves out much detail, but the original source file is freely available for review. I made a small python script (outside Grasshopper) to process the source file:<\/p>\n\n\n\n

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The resulting file was a reduction from 6k+ rows from the source to this:<\/p>\n\n\n\n

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Design Process<\/h2>\n\n\n\n

The models I generated from the dataset are rather basic; I decided to make physical 3D plots from the data, since I believed that this would be a good way to interpret it. The first model consists of a bar graph in three dimensions. Vehicle categories on the x-axis, years on the y-axis, and cumulative number of vehicles on the z-axis.<\/p>\n\n\n\n

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For the second model, I used the same source file, but made a surface graph instead. <\/p>\n\n\n\n

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For the third model, I really wanted to “print a mountain” after watching the demonstration in class. I knew exactly which mountain I wanted to print: the Shiprock geological formation located on northwest New Mexico. This model was much more involved than the previous two. Due to the relatively small scale of the formation, I could not use the bitmaps found in the USGS map generator website as we saw in class. There just wasn’t enough detail to make a good model. I scanned the area using the “Elevation Source Data (3DEP) – Lidar, IfSAR” option and found much smaller scale sections. This, however, provided me with a “.laz” file that I had to process with some LAStools software that I was not familiar with. After processing the .laz file, I loaded the resulting file on the QGIS software, then I generated a “rasterized” image from the source file. The resulting file was then fed to the python script as a bitmap to generate the model.<\/p>\n\n\n\n

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Rasterized image used for model<\/figcaption><\/figure>\n\n\n\n
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Reflection on designing with data<\/h2>\n\n\n\n

In making the models for this project, I realized many things related to data interpretation. It is really important to include scales, measures, units, etc. for audiences to get a good understanding of the data representation. In my plots, it was not possible for me to make the important values on a 1:1 scale, given the range of the values. I had to use a logarithmic scale for my models so that they would fit under my printer volume. This sort of illustrates my point: data can be manipulated to fit the needs of anyone. Without the scales, units, etc. mentioned before, no interpretation of the data would be accurate.
For the surface graph the situation worsens. Due to the interpolation of the point mesh, the details of the data are muddled even further, as the soft curves hide much detail. Additionally, I scaled the model to make it better suited for my printer and to comply with the size requirements, worsening the accuracy of the data representation.
On a lighter note, the process of physicalizing data was really fun. I especially enjoyed printing my own mountain. Again, it is not to scale, since I had to manipulate it’s dimensions to preserve detail. This manipulation also introduced some small artifacts that were probably introduced during the various processing stages, but overall I was very satisfied with the final product.<\/p>\n\n\n\n

Printing Process and Final Models<\/h2>\n\n\n\n
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Grasshopper code used in this assignment:<\/p>\n\n\n\n

GermanBejaranoLA3<\/a>Download<\/a><\/div>\n\n\n\n

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Dataset For this assignment, I had a little trouble deciding on a dataset to use for my project. I explored the links that were provided and others I found independently. I felt overwhelmed at some point because there is a massive amount of data on many subjects that it’s hard to focus on a single thing. In the end, I […]<\/p>\n","protected":false},"author":47,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[57,46],"tags":[],"class_list":["post-12681","post","type-post","status-publish","format-standard","hentry","category-large-assignment-3-data","category-studentwork24"],"_links":{"self":[{"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/posts\/12681","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/users\/47"}],"replies":[{"embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/comments?post=12681"}],"version-history":[{"count":5,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/posts\/12681\/revisions"}],"predecessor-version":[{"id":12821,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/posts\/12681\/revisions\/12821"}],"wp:attachment":[{"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/media?parent=12681"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/categories?post=12681"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/tags?post=12681"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}