I’m thoroughly enjoying gathering data and writing the scripts for the maps I’m going to share in my upcoming Women in Tech Summit workshop. I’m starting with world-level maps, then will move to US and then more local. A main challenge is that there are lots of different ways to draw maps in R. My personal favorite is to use ggplot2. I have seen there is a choroplethr package, but I haven’t tried that yet.
Here’s my latest map:
It’s based on the wrld_simpl SpatialPolygonsDataFrame, and I merged in data from the World Bank Development Indicators. Next up: gender differences in economic and education outcomes across the US. Whee!
This map plots grocery stores with at least 30 employees, as of 2008. The map begins with a black and white Google terrain map and plots locations by latitude and longitude coordinates. I am working on getting more current data, but I can say that the food deserts haven’t changed much.
Here’s the code I used. This requires the ggmap library.
Philadelphia is in a construction boom, but many long-time residents worry about the negative effects of all this development. In gentrifying neighborhoods property assessments have increased exponentially, yielding property tax bills that fixed-income earners find difficult to pay.
I’m starting to work with real estate data to explore gentrification patterns in the city. Point Breeze is an up-and-coming neighborhood (at least in its north end), with all sorts of new buildings going up. But has that development ‘rubbed off’ on the rest of the neighborhood? With this snapshot, that doesn’t seem to be the case. Controlling for home size, price increases are almost entirely tied to condos, not single-family units. On the one hand, this suggests that long-time residents should not be facing steeply rising property taxes. But on the other, the financial benefits from these developments (through higher sales prices) appear to accrue to developers, not to home-owners.