Playing around with that GIS optimization stuff was a chance for me to revisit some maze-making code I wrote a few years ago: http://healthyalgorithms.com/2011/01/07/piggies-and-mazes/ I wonder what age kids it will be good for.
A real, applied problem in spatial epidemiology crossed my desk last week, and it turns out that it is a super-fun combinatorial optimization challenge, too.
I don’t have time to play around with it a lot now, but I did try a little stochastic search, which makes me think that this will not be trivial to solve:
Is there a nice, simple reference for just what exactly these graphical model figures mean? I want more of them.
I wanted to include some old-fashioned statistics in a paper recently, and did some websearching on how to calculate R^2 in Python. It’s all very touchy, it seems. Here’s what I found:
I eventually went with this:
%load_ext rmagic x = np.array(1/df.J) y = np.array(df.conc_rand) %Rpush x y %R print(summary(lm(y ~ x + 0)))
I had an inspiration to make something a couple weeks ago for #MakeSomethingDay (the productive alternative to #BuyNothingDay). It is a finger-painting app that neighborhood kids have been enjoying. http://bl.ocks.org/aflaxman/a31763011f9da46fc6d2
I learned about a “big” data source for understanding air travel at the eScience incubator project talks last week, the DB1B database, aka the Airline Origin and Destination Survey. This is a 10% sample of all tickets for flights originating in the US, released quarterly since the 1993: http://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=289&DB_Short_Name=Origin%20and%20Destination%20Survey This must be good for something in global health.
Speaking of cool IHME seminars, last month we heard one on a social network analysis of the health policy actors involved in national-level policy change. So cool: http://www.healthdata.org/video/policy-development-integrated-community-case-management-iccm-national-and-global-levels-mixed