SF Days

San Francisco Demographics

The July/August 2009 issue of 7x7 magazine was the so-called neighborhood issue. In it is a decent catalog the major San Francisco neighborhoods: from the Haight to the Marina to the Castro. The articles are complete with the requisite, stereotypical neighborhood photos. But interestingly, they include some demographic data. I’m sure most folks who flipped through the issue glanced at the data briefly but moved on without thinking twice about them. I, however, was very intrigued by them. When Isaw them, I was convinced that if combined in a graphic manner they would reveal some elusive, inner secret that I’ve missed all these years.

Alas, after creating a spreadsheet of the data, I’m not sure if I’ve learned anything new about San Francisco. Maybe I’ve just lived here too long to have been surprised by this exercise.

Here is the product of this labor.

Figure 1: First is the most obvious result, which is that people with expensive houses tend to make more money than people with cheaper houses.

Figure 2: If you squint your eyes a bit it does look like folks in older neighborhoods make more money than folks in younger ones. The Marina looks to be an outlier in this one. Lots of young grads live there I suppose. The Tenderloin seems to be an outlier as well.

Figure 3: Assuming that the Tenderloin is an outlier in this next chart, is it surprising to find that the more money people make, the more likely they are to rent? Is this because SF is so expensive that folks who aren’t making a enormous of money but a good amount rent in nicer neighborhoods instead of buying in lesser ones?

Figure 4: As far as I see, there is no relationship between age and percentage of renters in various neighborhoods. Even if you toss out some outliers, it’s still a tough case to make. The R-squared value for this chart is .08, which means that there is almost no correlation between these variables.

Figure 5: At first, I was a little bit surprised by this last result as it looks like the more expensive neighborhoods have a higher rate of renters. But with an R-squared value of .01 that’s was a tough case to make. But if the Tenderloin is an outlier, there actually is some correlation between the two. Given the correlation between income and house prices, this chart is in some ways just a repeat of the one correlating renters to income.

Having said all that, one thing I did learn doing this is that I didn’t like any of the tools I tried to use to generate my charts: Numbers, Keynote and OmniGraphSketcher. From the start I knew I wasn’t going to use the charts from Excel, even though I bet that Excel would’ve technically been able to do everything I wanted. The problem with Excel is that its charts are just so visually mediocre. Numbers doesn’t do scatter plots as well as OmniGraphSketcher but it does allow the display of trendlines and the R-squared value. Also, the charts Numbers creates are visually more pleasing than the ones from OmniGraphSketcher. But OmniGraphSketcher has a much more natural user interface for playing with charts and that counts for a lot in my book. In the end, I decided that I didn’t need the R-squared values on my charts nor trendlines either but I did want titles next to the points on my scatter plots. So as a result, the charts below were generated using OmniGraphSketcher.