So, first I downloaded the zipcode database at Mapping Hacks, and unpacked the zipfile in my working directory.

Then, I loaded the data into R

So, now I have an R frame containing a lot of US cities, their geographical coordinates, and their zip codes. So we can start playing with the plot command! After rooting around a bit, I ended up settling on the smallest footprint plot dot I could make R produce, by setting the option pch=20 in the plot options. Hence, I ended up with a command basically like this:

where the +1 after the modulus is to make even 0-values plot, and the cex parameter sets the point size to something small and pretty.

We can continue this, tweaking the divisor to extract all the other digits of the zip code, and we end up getting:

and the result

and the result

and the result

and finally

and the result

And then, of course, we can zoom in on data too. So we can do things like extracting Californian zip codes

to get

or, we could extract New York zip codes:

or even extract, say, the zip codes starting with 10 or 11, covering New York City and surroundings and take a closer look

We’ll start by generating a dataset. Essentially, I’ll take the trefolium, sample points on the curve, and then perturb each point ever so slightly.

As a result, we get a dataset that looks like this:

So, let’s pick a sample from the dataset. What I’d really want to do now would be to do the witness complex construction, but I haven’t figured enough out about how R ticks to do quite that. So we’ll pick a sample and then build the 1-skeleton of the Rips-Vietoris complex using Euclidean distance between points. This means, we’ll draw a graph on the dataset with an edge between two sample points whenever they are within ε from each other.

So we pick a sample from this sample. Every 31 points might be good. (number arrived at by guessing wildly, and drawing the resulting images until they looked pretty enough)

We’d get, from this, the following result:

Now, we’ll want to build the corresponding skeleton. Let’s do it for a few different εs to demonstrate the difference.

The result is:

We notice that as the radius we observe grows, we connect all the loops, but by the time the loops are completely connected, there are also cross connections forming toward the middle. However, with any luck, these cross connections will be so short-lived, in terms of the radii we use, so that the homology classes we extract from the Rips-Vietoris complexes are noticably more persistent.

Doing the corresponding computation relies on me figuring enough out to use the Plex software suite, or writing my own, and thus will be subject of a much later blog post. This one was mainly “Look – I use R” and “Look – pretty pictures”. Enjoy.