[R-sig-Geo] algorthirm to join polygons based on population properties

[James Rooney]

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 to be called James!

James
________________________________________
From: Kuroda [kuroda.cpa at gmail.com]
Sent: 07 January 2014 11:19
To: James Rooney
Cc: r-sig-geo at 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 might help you below...

AMOEBA
http://cran.r-project.org/web/packages/AMOEBA/
(It looks like a clustering method that provides NON-exclusive clusters)

REDCAP
http://www.spatialdatamining.org/software/redcap
As far as I know, REDCAP has not implemented in R yet, unfortunately.

I hope this helps.

Sho Kuroda

--
Sho Kuroda (Mr.)
kuroda.cpa at gmail.com



2014/1/7 James Rooney <ROONEYJ4 at tcd.ie>:
> Ok thanks Roger I'll read up on that!
>
> Many thanks!
> James
> ________________________________________
> From: Roger Bivand [Roger.Bivand at nhh.no]
> Sent: 07 January 2014 10:12
> To: James Rooney
> Cc: r-sig-geo at r-project.org
> Subject: Re: [R-sig-Geo] algorthirm to join polygons based on population properties
>
> 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 disease. This is all pretty
>> much fixed there is nothing I can do about it. I have performed an
>> analysis based on about 3500 polygons and that works ok. However the
>> population data has bad maths properties. There I'm now working with
>> newer data using 18,000 polygons and the same cases. This population
>> data has better maths properties (i.e. population per polygon is more
>> symmetrically distributed). But there are too many polygons - most of
>> the polygons have no cases. So when I do Bayesian smoothing I just end
>> up with a uniform map of Relative Risk =1 everywhere as all the polygons
>> with cases are all surrounded by polygons with no cases.
>>
>> I figure to get around this I either fiddle with the spatial weighting
>> (seems unwise), or join polygons in some sensible fashion. My question
>> was really wondering are there algorithms to deduce a list of polygon
>> joins based on polygon properties. For example - I don't want to join
>> urban and rural polygons as I am interested in the association of
>> population density with incidence rate. I'm also interested in the
>> relationship with social deprivation - so I don't want to join an area
>> of high deprivation with and area of low deprivation. Basically I want
>> to know is there a package that will create me a join list based on such
>> rules ? I can of course write some code to do it but I was hoping not to
>> have to spend the time on it!
>
> Briefly, you have a regionalisation problem in addition to MAUP, so have a
> look at spdep::skater and the underlying paper:
>
> Assuncao, R. M, Neves, M. C., Camara, G. and Freitas, C. da C. (2006).
> Efficient regionalization techniques for socio-economic geographical units
> using minimum spanning trees. International Journal of Geographical
> Information Science Vol. 20, No. 7, August 2006, 797-811.
>
> However, different criteria and clustering variable subsets will give
> different output regional aggregates. You may like to check robustness by
> comparing summary statistics for the aggregates, and by comparing output
> risk values under different aggregations.
>
> The key functions in this approach now support parallel execution, look
> carefully at the examples using the Boston dataset at the foot of the help
> page, and note the differences between Windows and Linux/OSX.
>
> Hope this helps,
>
> Roger
>
>
>>
>> James
>> ________________________________________
>> From: Roger Bivand [Roger.Bivand at nhh.no]
>> Sent: 07 January 2014 08:28
>> To: James Rooney
>> Cc: r-sig-geo at r-project.org
>> Subject: Re: [R-sig-Geo] algorthirm to join polygons based on population properties
>>
>> 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 need to only combine polygons of similar population density
>>> and it would be best if the new polygons have a distribution of total
>>> population that was within a limited range/normally distributed.
>>
>> This is not clear. Do you mean density (count/area) or just count? If you
>> have "too many polygons", then probably you haven't thought through your
>> sampling design - you need polygons with the correct support for the data
>> collection protocol used. Are you looking at postcode polygons and sparse
>> geocoded cases, with many empty postcodes? Are postcodes the relevant
>> support?
>>
>> If you think through support first (Gotway & Young 2002), then ad hoc
>> aggregation (that's the easy part) may be replaced by appropriate
>> aggregation (postcodes by health agency, surgery, etc.). The aggregation
>> can be done with rgeos::gUnaryUnion, but you need a vector assigning
>> polygons to aggregates first, preferably coded so that the data can be
>> maptools::spCbind using well-matched row.names of the aggregated
>> SpatialPolygons and data.frame objects to key on observation IDs.
>>
>> First clarity on support, then aggregate polygons to appropriate support,
>> then merge. Otherwise you are ignoring the uncertainty introduced into
>> your Bayesian analysis by the aggregation (dfferent aggregations will give
>> different results). There are good chapters on this in the Handbook of
>> Spatial Statistics by Gelfand and Wakefield/Lyons.
>>
>> Hope this clarifies,
>>
>> Roger
>>
>>>
>>> I can of course come up with some way of doing this myself, but I'm not
>>> keen to reinvent the wheel and so I am wondering - are there any smart
>>> algorithms already out there for doing this kind of thing ?
>>>
>>> Thanks,
>>> James
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> R-sig-Geo at r-project.org
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>
>>
>> --
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N-5045 Bergen, Norway.
>> voice: +47 55 95 93 55; fax +47 55 95 95 43
>> e-mail: Roger.Bivand at nhh.no
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> R-sig-Geo at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; fax +47 55 95 95 43
> e-mail: Roger.Bivand at nhh.no
>
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