[R-sig-Geo] Choosing the right nb2listw-style

[Roger Bivand]

On Sun, 22 Aug 2010, Breitbach, Nils wrote:

> Dear Community,
>
> to be able to evaluate the spatial autocorrelation within may data I am 
> forced with the question of how to correctly choose the neighbours for 
> my data. My study plots (points) are not evenly distributed over my 
> study area (approx. 30 x 30 km in size) and also the land-use type is 
> not evenly distributed over the study area and therefore I want to 
> evaluate the spatial autocorrelation for this data set. To finally 
> calculate the Moran's I or plot variograms/correlograms I now need to 
> calculate the neighbourhood relationships of my study plots. For the 
> given characteristic of my data (especially their non-even distribution) 
> I am now somewhat uncertain about the right style (W, B, C, U or S) of 
> the nb2listw() object that suits best for my kind of data.
>
> Can anyone recommend the "right" style for my kind of data?

Typically, varying sub-discipline communities have different prefered 
flavours, both of the neighbour list object, for using general weights (or 
not) - including inverse distance weighting, and for using 
row-standardisation (W), raw (binary or general - B), or standardised raw 
(C - sum to n, U - sum to 1). There isn't a tradition dor using variance 
stabilising (S) although there probably should be. It seems sensible to 
see what others in your field use, and choose among those. The same for 
schemes for finding the neighbours to start with. Using an approach which 
is unusual in your field will attract referees' attention to your choice - 
they will want to know why you are doing something different. Since you 
are in ecology, look at papers using weights there, and unless you can see 
that the modal scheme is suboptimal for you, go with the stream.

Note however that some of the graph-based neighbour schemes advanced early 
on by Sokal in ecology are little used, and probably deserve more 
exposure, especially when the distances between observations differ a lot 
- leading to observations in dense parts of the study area having many 
neighbours in schemes using a distance threshold. Try to think about the 
plausibility of the science in the implied spatial process - could 
observations realistically influence each other at that distance? It may 
not matter if the weights are only "mopping up" unwanted spatial 
autocorrelation, but if the dependencies have a substantive 
interpretation, it isn't wise to imply mutual dependence that isn't 
scientifically plausible (think of natural boundaries that organisms 
cannot "cross" as well as distances). But no, no "right" scheme as such - 
it's up to you! Pay attention to the inhomogeneity of your setting too, as 
it may induce apparent dependency if not modelled.

Hope this helps,

Roger

>
> Thanks for help!
> Regards,
>
> Nils
>
> _________________________________________________________
>
> Nils Breitbach, Dipl.-Biol.
> Institut für Zoologie, Abt. 5: Ökologie
> J.-J.-Becher-Weg 13
> Johannes Gutenberg-Universität
> 55128 Mainz
> Germany
>
> phone: +49 6131 39-22718
> fax:   +49 6131 39-23731
> WWW: www.community-ecology.uni-mainz.de/126_ENG_HTML.php
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-- 
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, 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