Recently I’ve set up both a PostgreSQL and MySQL server to host databases related to some of our projects in the Ward Lab. I should note that I have no idea what I’m doing, and this is the first time I’ve dealt with databases and how to get them working. It’s been a very humbling experience, although in the end, we now have two different databases that can be accessed remotely from a laptop through R or other tools like Quantum GIS:
# setup connection to database library(rgdal) dsn <- "PG: dbname='db' host='someIP' port='5432' user='me' password='guest'" # Load Afghanistan boundary (source: GADM) state <- readOGR(dsn, layer="afg_adm0") plot(state)
I’ve been working with SIGACT data (military significant activities reports) all day as part of a research project. When you are dealing with thousands of them, it is easy to trivialize and forget the amount of human suffering encapsulated in each event (and those we don’t hear about).
Yesterday, in Afghanistan, 2nd Lieutenant Justin Sisson was among the dead in a suicide bombing that killed another NATO soldier and 9 children. We went to ROTC and Iraq together, and he was an impressive and amazing person. It is sad and unexpected that his life was cut short.
Maybe a few years from now researchers will have access to the SIGACT report associated with his death. I hope that something comes out of it that will prevent horrible things like this from happening.
Will Moore, Kentaro Fukumoto, and I have been working on a random walk negative binomial model for time-series of counts, based on earlier work by Kentaro on a negative binomial integrated (NB I(1)) model. We just presented a related poster in which we look at monthly civilian deaths in Iraq at Peace Science in Savannah, Georgia. Here is the actual pdf poster (it’s a big file, be warned), but the basic point is that ARIMA or classical count-models are not a good way to deal with time-series of counts, like monthly deaths in a conflict, and that we have a tested model for non-stationary counts that has some attractive features.
We are working on a draft paper, so I don’t want to go through the whole story, but if you’d like to try it out yourself and know how to use JAGS, all the R and JAGS code is available on github.