These are the slides from a presentation today at the European conference of the IMA, held in Maastricht, 23rd to 24th October, 2014.
Microsimulation, as its name suggests, is about modelling things at the individual-level. In practice, this usually means estimating the characteristics of people using statistical or econometric techniques. Microsimulation, as represented by the International Microsimulation Association is a niche area at the interface between public policy evaluation and academia. Clearly, this is a subject area that depends on performant software, a common language to communicate complex ideas and transparency. R excels in each of these areas, yet seems to be relatively little used in field, despite seeming ideal for the job. Below the fold I reflect on why this may be, its impacts and a call to ‘open source’ spatial microsimulation.
I recently attended a week-long R course in Newcastle, taught by Colin Gillespie. It went from “An Introduction to R” to “Advanced Graphics” via a day each on modelling, efficiency and programming. Suffice to say it was an intense 5 days!
Overall it was the best R course I’ve been on so far. I’d recommend it to others, from advanced users to adventurous novices. Below I explain why, with a brief description of each day and an emphasis on day 2.
I was recently invited to write a book review for Applied Spatial Analysis and Policy (ASAP). The book, I conclude, “is the authoritative resource on R’s spatial capabilities” and should be of interest to many R users.
Below is a preprint of the full review, now published on ASAP’s website.
Today I presented my talk at the RSAI British and Irish Section. The talk is about reproducibility. Why are reproducible methods and results important? How reproducible is Regional Science? What can be done to increase the reproducibility of your work? These are the types of questions tackled in the talk, which also be seen on the Rpubs website.
This miniature vignette shows how to clip spatial data based on different spatial objects in R and a ‘bounding box’. Spatial overlays are common in GIS applications and R users are fortunate that the clipping and spatial subsetting functions are mature and fairly fast. We’ll also write a new function called
gClip(), that will make clipping by bounding boxes easier.
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The internet is awash with detritus. In the name of navigating the maze, these links highlight some more enlightened online content.
George Monbiot: investigative journalism at its best
R-Bloggers: a site endevouring to make statistics accessible and fun
Tom's bike trip: there are many 'bike trip' sites out there; this is one of the best
TGRG website: the Transport Geography group I'm involved with
MASS profile: academic profile at Leeds
Powerstar Youtube channel: check out my videos
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