I hope everyone had a wonderful Thanksgiving. I’ve been extremely busy this month with school and college applications, so I’m taking this opportunity to collect data for a future post. I set up a survey about college admissions because I want to see how my peers feel! (Note to self: do not post the link to Tumblr again.)

Here’s the link to the survey!


Moving Statistics (Light Post)

Hello! It’s been a hectic month; we’ve been preparing to move to our new home (not too far from here, it’s just better than our current house). In the meantime, I decided to research data regarding the number of new houses sold and their ft^2 of area.

New Single-Family Houses and ft^2 in the United States

The house we’re moving to has more living space than our current one. I was wondering if the demand for new, larger houses would increase over time. This would be shown by the number of large houses increasing. I got the data* from here; I found that website through data.gov, which is amazing if you need any kind of data, by the way. The code can be found here.

Here’s the number of new houses constructed each year. The second graph shows subgroups for different ranges of living space.


Unsurprisingly, the number of new houses constructed each year has decreased. As more homes are built, the available land decreases and becomes more expensive. The rate of population growth is also decreasing in the US. (Take a look at population pyramids for post-industrialized nations!) Medium-size houses seem to consistently be the most popular. The demand for larger houses has increased very slightly, as shown by how the curves for 3000+ square feet are higher relative to the other curves.

Western US

Since I live in sunny California, I wanted to see how the western United States is different from the country as a whole.


It seems that there was a higher demand for houses in the west before the first dip. I’m willing to bet that the bulk of this was for California’s nice weather, or maybe for the west coast in general. The housing bubble probably caused the sharp raise then decline between 2004 and 2008.

My findings were interesting. I knew that there was a huge decline in house sales after the bubble burst, but I never would have guessed that it was this sharp. It seems that although the number of new homes is starting to rise slightly, it still isn’t near what it was at its peak.


*NOTE: The data must be transposed because it is in columns, not rows. Here’s how I did it:

areaData = Import[fileName][[1]];
areaDataUS = Transpose[areaData[[10 ;; 26]]][[2 ;; -1]];
areaDataW = Transpose[areaData[[93 ;; 109]]][[2 ;; -1]];

Update: New Pages and Polar Coordinates

Hello! I have been busy this month with science fair and with making new pages for this blog. I have added a page for the project my partner, Azja Czajkowski, and I brought to the Greater San Diego Science and Engineering Fair. I also added a page for my (actual) experiments. Although there currently is not much on that page, I will be uploading more files there soon. Please check out those pages by following the links above!

I also have something else that was too short to be worthy of its own post.

Polar vs. Cartesian Coordinates

When I was learning Calculus BC, I was confused by polar coordinates. Although I understood how to plot points, trigonometric functions confused me. How could a sine function become a circle? How did shifting the sine function up or down in Cartesian coordinates cause the polar graph to turn into an odd loop shape? This program (link to code) helped me understand how polar coordinates worked. The reason why a loop shape comes out of a vertically shifted sine function is because a normal sine function actually drew the circle twice. Shifting the function up or down would shift where the point was drawn (the radius). I could also understand the parallels between polar and Cartesian coordinates, and it helped me realize how easy polar coordinates were.

Here is a video demonstration of the program (link).polar

Quick Post

I’ve created a short survey using Google Docs for the holidays. I will analyze the results in a little while! Feel free to take the survey here.


I am visiting Yellowstone right now, and there’s a lot of snow. (Living in Southern California, I’ve heard legends of water falling from the sky, but this?) The snow reminded me of the Koch snowflake (Koch curve) and other fractals. Apparently, there is are variations of the Koch snowflake made out of spheres. Because spheres don’t have sides, I imagine that the “branches” of the “snowflake” could be placed in many different configurations.


It’s been a while!

I’m currently at a summer internship, making a program to detect brain tumors given MRI images. The program will allow the user to draw boundaries on the inside and outside of the tumor, and will then use those boundaries to find the tumor, save the shape of the tumor (mask), and build it in 3d. Data can then be exported as an Excel spreadsheet and the masks as a folder with .png files in it.

A .mx file converter should also be ready. The reason why .mx files are used is because .dat, .txt, .png, or most other file types run very slowly. The converter will not take long to run (less than 1 min).

I’m in the process of packaging and debugging my programs, making them easier to use.