[Openspace] minimum distance sampling and spatial autocorrelation

[Victor Hugo Ramos WCS]

Julia:

First, thank you for your fast response.  I think that GEODA is a 
wonderful tool.

Second, I think that I found a workarond on the problems related to SAC 
and binary dependent variables.  What I did was to instead of using 
Deforested/Forested data, I extracted deforested surfaces from 25 pixel 
windows and then used that data in the SAC analysis.  The analysis was, 
apparently correct, and I built a correlogram with MORAN (Y) and 
distance between pairs of points (X) that shows that there is 
significant SAC until the distance reaches 60 km, and then goes to 
significant negative values to go up again to positive significant 
values until it reaches the upper limits of the maximun distance between 
points.  The distance where there is no significant SAC is useless for 
me in terms of sampling because my study area is relatively small and 
I´m not going to be able to make enough sampling units to develop my 
model.  I´m trying to access the sampling paper that you recomended me, 
but in between that, giving what I have explained before can you give 
some general advice on the following questions:

Is the workaround that I implemented correct?
Do you have any recomendations on sampling design giving the behavior of 
Moran as I explained before?  Can I send you the graph in order to 
better visualize the information?

Any help is greatly appreciated.

Victor Hugo Ramos
Wildlife Conservation Society
Guatemala, Central America

 this, I have a couple of questions:Julia Koschinsky wrote:

>Victor,
>
>You raise two issues that have to be addressed with software 
>other than GeoDa: 1) Controlling for spatial autocorrelation 
>through sampling design and 2) running spatial 
>autocorrelation tests with a binary dependent variable.
>
>1) On the first issue, an example of a recent reference is:
>
>Griffith, Daniel A. (2005). "Effective Geographic Sample 
>Size in the Presence of Spatial Autocorrelation," Annals of 
>the Association of American Geographers, Vol. 95, December, 
>pp. 740-. 
>
>2) On the 2nd issue, some of the references on our site on 
>spatial probit include:
>
>http://sal.uiuc.edu/courses/se/pdf/w13_probit_notes.pdf
>http://sal.uiuc.edu/courses/se/pdf/w13_probit_out.pdf
>http://sal.uiuc.edu/users/anselin/papers/hood.pdf (pp. 8-9)
>
>You might be able to find experimental code on spatial 
>probit in R or Python.
>
>Julia
>
>---- Original message ----
>  
>
>>Date: Tue, 06 Jun 2006 08:39:57 -0600
>>From: Victor Hugo Ramos WCS <vhramos at wcs.org>  
>>Subject: [Openspace] minimum distance sampling and spatial 
>>    
>>
>autocorrelation  
>  
>
>>To: openspace at sal.uiuc.edu
>>
>>How do I use GEODA in order to lay out a sampling design 
>>    
>>
>that avoids 
>  
>
>>spatial autocorrelation giving the following conditions:
>>
>>
>>
>>-         I want to build a logistic regression model that 
>>    
>>
>is going to 
>  
>
>>use deforestation as dependent variable and a number of 
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>environmental 
>  
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>>and physical layers as independent variables
>>
>>-         Deforestation is coded 1 for deforested and 0 for 
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>not deforested
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>>-         I just want to test spatial autocorrelation on 
>>    
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>deforestation
>  
>
>>-         The main value coming from the test for spatial 
>>autocorrelation should be a minimum distance between samples
>>
>>
>>
>>Thanks in advance for your help
>>
>>Victor Ramos
>>_______________________________________________
>>Openspace mailing list
>>Openspace at sal.uiuc.edu
>>http://sal.uiuc.edu/mailman/listinfo/openspace
>>    
>>
>
>  
>