On Tue, 7 Jan 2014, James Rooney wrote:
Dear all,
I have dataset with very many more polygons than cases. I wish to apply
Bayesian smoothing to areal disease rates, however I have too many
polygons and need a smart way to combine them so that there are less
overall polygons.
Bascially I
Hi Roger,
Thanks for your reply. Coding the joins is not a problem I've already done that
on a smaller scale in a different project.
No postcodes in my country. I have polygon data from the census and I have
geocoded cases for every case of a rare disease. This is all pretty much fixed
there
On Tue, 7 Jan 2014, James Rooney wrote:
Hi Roger,
Thanks for your reply. Coding the joins is not a problem I've already
done that on a smaller scale in a different project.
No postcodes in my country. I have polygon data from the census and I
have geocoded cases for every case of a rare
Ok thanks Roger I'll read up on that!
Many thanks!
James
From: Roger Bivand [roger.biv...@nhh.no]
Sent: 07 January 2014 10:12
To: James Rooney
Cc: r-sig-geo@r-project.org
Subject: Re: [R-sig-Geo] algorthirm to join polygons based on population
properties
Professor Rooney:
As professor Bivand remarked, key words are regionalization,
region building, zoning design, or constrained spatial clustering.
Cf. Duque, J. C., Ramos, R. and Surinach, J. (2007) Supervised
regionalization methods: International Regional Science Review, 30
(3), 195-220.
It
Dear Mr Kuroda,
Many thanks for the information. I will have to take some time to digest the
various links I've been sent, and learn the new lingo!
And I appreciate the honorary promotion, but it is not 'Professor' Rooney at
all!
Dr Rooney if you like titles - though I personally much prefer
Doh, sent the wrong examples. Swap in rasterEngine for focal_hpc
(generally, avoid using focal_hpc)
install.packages(spatial.tools, repos=http://R-Forge.R-project.org;)
library(spatial.tools)
# sfQuickInit() # Uncomment out to run in parallel
tahoe_highrez -
Dear James,
In addition to what others have suggested, you may want to try a
different modelling approach using zero-inflated models. If you are
working on a rare disease, a zero-inflated model can accommodate the
high number of zeroes better than standard models.
Best wishes,
Virgilio
On mar,
Hi-
I need to count the number of observations in each of 8 polygons. I'm
using the raster package for this, but am having trouble getting what I
need. Why am I getting a count of 1 in each of the polygons defined below?
Sample data can be downloaded here:
Tim,
To count the number of points (obsdeep) in each polygon, you can do:
table( over(SpatialPoints(obsdeep), poly) )
There is no role for rasterizing polygons here. But to answer your
question: you are getting a count of 1 because there is never more
than 1 polygon that matches a raster cell
Thanks Robert, much appreciated.
On Tue, Jan 7, 2014 at 1:57 PM, Robert J. Hijmans r.hijm...@gmail.comwrote:
Tim,
To count the number of points (obsdeep) in each polygon, you can do:
table( over(SpatialPoints(obsdeep), poly) )
There is no role for rasterizing polygons here. But to answer
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