As I get older, a few of my buddies are starting to retire. And this makes me think about my own retirement (not that I'm anywhere near old enough to retire, mind you!) Therefore when I saw a list of the "Best & Worst Cities for Retiring" it caught my attention. The list of almost 400 cities was a bit cumbersome to wade through, therefore as a Graph Guy I felt obliged to do something graphical to help analyze the data...
I found the data in an article on the Advanced Dental Care website. They had summary tables of the 50 best and worst cities, such as ...
Their tables were loaded with color-coded graphical icons/glyphs, but these fancy tables didn't really help me get a handle on the data. Later in the article, they also had a simple text table of the data for all 381 cities in the study - I actually found this simple table easier to read.
Also, a simple text table was much easier to import into SAS (I was able to copy-n-paste the values into a text file, and read the tab-delimited text using a data step).
Geocoding the Data
Now that I've got the table of data into a SAS table, what should I do with it? I think this data is begging to be plotted on a map!
The city & state values (such as "Raleigh, NC") are in the table, but how can I plot that on a map? There's a process called geocoding which takes city/state/address information, and estimates the latitude/longitude coordinate. In SAS, Proc Geocode can do that ... but we'll need to do a bit of "data cleaning" first.
Proc Geocode can estimate the lat/long of a city - but your data observation must be a single city. And some of the locations in the table are a combination of cities (and sometimes a combination of states), such as "Youngstown-Warren-Boardman, OH-PA". Therefore I parse out the first city and the first state ("Youngstown, OH" in this case) to use for the geocoding.
After cleaning the data, I can easily run Proc Geocode, to estimate the lat/long of all the cities. For the cases where I just picked the first of multiple cities, the position will not be the exact centroid of the multiple cities ... but close enough when you're plotting the data on a map of the entire U.S.
proc geocode data=all_data out=all_data method=city
Mapping the Data
And now, I can use Proc SGmap to plot the data as colored markers on a map. (Here's a link to the complete SAS code, if you'd like to see all the details - the code utilizes features from the latest SAS release, Viya 3.5, so you'll need that to run it.)
proc sgmap plotdata=plot_data;
scatter x=long y=lat / colorresponse=rank
colormodel=(cx4dac26 yellow cxd7191c);
gradlegend / position=right;
According to this ranking, Florida, and the states along the Great Lakes, are the best places to retire.
Hmm ... what about the other variables in the table? Let's plot some of them! Here's the AARP Livability Index. Looks like they favor Wisconsin, Minnesota, Iowa.
What if you're more budget-minded, and just care about the cost of rent for retired people? Looks like the closer you get to the coast, the higher your rent (this is especially evident in California).
And, perhaps the biggest question - where did retired people actually pick to live? Looks like Florida is the winner, by far! What's the big draw in Florida - perhaps the weather?
And where's the #1 spot with the highest % retired people? ... That would be The Villages, in Florida!
Feel free to discuss these in the comments section:
- What factors in the maps do you agree/disagree with?
- What other factors could affect retirement decisions?
- Where do you plan to retire, and what are the major factors in your decision?
- If money were no object, where would you like to retire?
- Would you consider retiring in another country? (see my previous blog post on that topic)
And if you made it this far, here's a little something extra for you... This is a picture of my buddy Ed, sitting outside his motor home, grilling dinner at White Lake. Ed retired about a year ago, and he was the developer who wrote Proc Geocode and Proc SGmap (the two tools I used to create these maps). Therefore I think that makes it especially fitting to include his picture in this blog post about retirement! 🙂