{"id":12840,"date":"2024-10-10T23:38:36","date_gmt":"2024-10-11T05:38:36","guid":{"rendered":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/?p=12840"},"modified":"2024-10-10T23:52:11","modified_gmt":"2024-10-11T05:52:11","slug":"data-physicalization-ricardo-ponce","status":"publish","type":"post","link":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/2024\/10\/10\/data-physicalization-ricardo-ponce\/","title":{"rendered":"Data Physicalization – Ricardo Ponce"},"content":{"rendered":"\n

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

There was two data sets that interest me and they were the traffic collisions and census data for Los Angeles. This is due to the fact I’m originally from Los Angeles and because of that, your kind of isolated from the world. Since moving to Albuquerque it has opened my eyes on what you can find on the outside. So to get a better understanding how on densely populated Los Angeles is and I always hated the traffic in LA, so seeing the amount of collisions makes it understandable on why travel times were so large. The data set for traffic collision is massive since its collected data from 2018 to 2024. So I only selected the column for Reporting district so we know the location of the accident and the Victim Age to see who gets affected. For the second data set it was much smaller but had reporting information for each zip code in LA. Thankfully the data set was already sorted but if it wasn’t it wouldn’t matter since I have a script that will sort it based on what variable I want to check against. For this dataset I wanted to create multiple figures so for one of them I used the Total population and the Average Household Size since it would give a good visualization on how the total is distributed. Then for the final object I used Total population and and median age. Depending on how large the data set was I only chunks of the data since fitting 500,000 rows could be challenging for grasshopper to process.<\/p>\n\n\n\n

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

I wanted the data to be the model where just by looking at the model you know what it represents. Meaning if I were to give you what the axis represent you will be able to collect data from the visualization. Almost like a 3D graph or chart where the curvature depicts that more people live in this sector because of how much it sticks out. In order to achieve this I had to mess around with what column should belong to which axis in order for it to make sense. For me it made the most sense to put the location of the data on the Z axis because as you move up you will be at a new location. Then the actual data will be on the x since the width will give a nice visual effect. Since LA is such a large area the data can get random which creates sharp edges. So sharp in fact that overhang becomes a concern and trying to print the model in that state will require supports. A solution I used was to take the modulus of the data points so I can skip every so points depending how aggressive the overhangs are in order to create smooth transitions. The third model needed additional filtering since there were some values that were zero which wasn’t an issue until it came to printing it. This is because at the zero points it would create weak points causing it to snap during the process. <\/p>\n\n\n\n

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Printing Process and Final Images<\/h2>\n\n\n\n
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Reflection<\/h2>\n\n\n\n

Designing models with data was interesting and challenging. This is because you had to find a data set that can be translated in a model. Then parsing through that data for any irregularities, too large of values, and garage data. Once the data is clean I have to figure out what had to go to which axis. As you can see from the result you get some interesting figures. I also didn’t want to make the figures too complex to the point where you can’t understand what the figure is suppose to be. Some of the data sets for Los Angeles are so large and compact you can get aggressive waves to the point you don’t know what to make out of it. My final models do a good job of illustrating the diverse and large scale of Los Angeles. <\/p>\n\n\n\n

3D objects can help people understand data because sometimes datasets can be so large it can be difficult to comprehend its meaning. So by creating a model representation of that data makes it more tangible. It also makes it more visually intuitive and easier to see patterns. By using data you are able to create models that tell a story. Each layer of the model has a meaning and that meaning is determined by the individual that creates it. For example, for my 3rd model as you go through the zip codes of Los Angels you see what the average household size. The curvature tells you that some areas have bigger families and where they are the same. Finally each object will be unique since it would be difficult to get the same data set. This assignment was the most challenging for me due to the amount failed prints I had. Supports were all over the place and had multiple prints snap during the printing process. Trying to figure out solutions without alternating the data set was challenging but rewarding to see the type models could come out of it. <\/p>\n\n\n\n

Data Set and Grasshopper Code <\/h2>\n\n\n\n

https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-content\/uploads\/2024\/10\/2010_Census_Populations_by_Zip_Code.csv<\/a><\/p>\n\n\n\n

https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-content\/uploads\/2024\/10\/Traffic_Collision_Data_from_2010_to_Present.csv<\/a><\/p>\n\n\n\n

https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-content\/uploads\/2024\/10\/RicardoPonceLA3.gh<\/a><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Dataset There was two data sets that interest me and they were the traffic collisions and census data for Los Angeles. This is due to the fact I’m originally from Los Angeles and because of that, your kind of isolated from the world. Since moving to Albuquerque it has opened my eyes on what you can find on the outside. […]<\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[57,46],"tags":[],"class_list":["post-12840","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\/12840","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\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/comments?post=12840"}],"version-history":[{"count":2,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/posts\/12840\/revisions"}],"predecessor-version":[{"id":13042,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/posts\/12840\/revisions\/13042"}],"wp:attachment":[{"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/media?parent=12840"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/categories?post=12840"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/handandmachine.org\/classes\/computational_fabrication\/wp-json\/wp\/v2\/tags?post=12840"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}