Dataset
I chose the data on health conditions for children under the age of 18 in the US to learn more about how different health conditions affect certain age groups. I chose the dataset by viewing the U.S. Department of Human Health & Services website and choosing a data set that wasn’t too big to download and analyze. Therefore, the data set I chose is Health conditions among children under age 18, by selected characteristics: United States.
Data cleaning
The file consisted of various features for analyzing different health conditions. I chose rows of asthma attacks and asthma cases in relation to the features (columns) of estimates and standard error (SE) of the health conditions. I also removed the rows that had missing values in either or both columns.
Design Process
I used the health data to generate 3D forms by choosing specific sections of the data and scaling the data on the XYZ coordinates. In Figure 1, I chose the points 50 to 90. I multiplied the X-axis by 2 and the Z-axis by 10. In Figure 2, I selected points 280 to 320. I multiplied the Z-axis by 10. Finally, in Figure 3, I selected the points 320 to 360 and multiplied the X-axis by 3 and Z-axis by 10.
I chose these forms because I wanted to present the number of children with asthma, asthma attacks, and both. Figure 1 represents the children with asthma, Figure 2 represents the children with asthma and the children with asthma attacks, and Figure 3 represents children with asthma attacks. I also avoided forms that contained sharp curves that could challenge the printing process. Some forms were changed using different scaling factors while for others I decided to change the range of points. I also wanted to further analyze the difference between Figure 1 and Figure 3 by keeping their range similar- 40 data points.
Rhino Images
Clean File & Grasshopper Code
Grasshopper code: https://handandmachine.org/classes/computational_fabrication/wp-content/uploads/2023/10/RaneemRamadanLA3-1.gh
The process of creating objects via data was interesting as it allowed me to further understand the data I was looking at. I’ve learned that data can be formed into printable objects. I’ve also learned that it may need to be scaled to meet different perspectives. The unique meanings objects can have are the physical representations of the dataset itself. The physical figures of the data hint to me at the data’s background. For example, in Figure 1 is rather smooth (compared to the others) describing that many children have asthma. While Figure 2 has a sudden spike at the top of the object. This demonstrates the difference between the data of asthma in children and asthma attacks. As for Figure 3, it is rather spiky, this shows that the number of asthma attacks is less than those who have asthma.
Printing Process/Problems
Data Physicalization 3D Models
Hello Raneem, I really enjoyed your presentation. I think your objects are really interesting because all my brothers and I have asthma, but none of us had asthma attacks for the past 10 years. I think it’s really cool to see the difference between asthma diagnoses and the rate of asthma attacks, Great Job!
Hi Atah, thank you! Yes, it is interesting how asthma and asthma attacks rates differ. I have a friend with asthma and I wanted to share how not all people diagnosed with asthma have asthma attacks.
Hi Raneem, really good job on your data physicalization project. I think it was a great choice to do the asthma study. Two of my three children have asthma and it’s such an important field. Interestingly, my son is the one with asthma attacks. My daughter only has hard time during hard exercise. Your charts came out looking really neat. Good job persevering despite the troubles with your printer.
Hi Michaela, thank you! It is interesting how asthma effects people differently. With this study, I wanted to share how asthma effects people. I’m glad you enjoyed my project.