AI-GEOSTATS: moran's i with a twist?

[Seth J Myers]

Hi everyone,

This is a bit long-winded, but I respect everyone's mind on this list and would 
like any criticism and suggestion, if your time allows.

I would like to include a spatially-lagged variable in logistic regression in 
order to decrease some autocorrelation problems in a land-use change model.  
The model is for the probability of a cell not developed in 1985 becoming 
developed by year 2006.  I would like to use Moran's I to estimate my 
spatially-varying weights.

The problem is this: I am modeling the probability of land becoming developed 
from 1985 to 2006 (to capture contemporary dynamics) but developed land 
pre-1985 likely has an influence, and, so, there is a mismatch between the 
areas having an influence and the response.  For background, the response is 
binary (0=undeveloped, 1=developed).

I could just calculate Moran's I for all areas, but this would also include the 
autocorrelation of cells developed pre-1985 with other cells developed pre-1985 
 (which is not of interest for several reasons including that the areas 
developed long ago were influenced by 'accidents' of history which are wholly 
unobservable, plus the decision-making process for land development changes 
over time).  I could just calculate Moran's I on the increment of growth from 
1985 to 2006 (masking out pre-1985 developed areas) but this would neglect a 
source of contagious effect.

My possible solution is this.  Allow, cross products in the numerator in the 
usual manner between observations in the recent growth increment (1985 to 2006) 
but add a restriction that cell values for pre-1985 growth (minus the mean) are 
only multipliled with the cell values of the current increment (0=not developed 
in 1985 or 2006, 1=not developed in 1985 but developed in 2006).  So, I am 
trying to get at how autocorrelated the new increment is with itself and the 
previous growth (but not previous growth with itself).  The mean for use in 
subtraction would possibly be the mean over all cells in the lattice.  This 
seems in a sense to be the cross-correlation between: 1) all developed cells 
and 2) cells potentially developed from 1985 to 2006 (the response).   When 
stated this way, it seems that possibly two means should be used (all 
development and current increment), ala' the usual covariance and cross 
correlation formulation.

Seth Myers
PhD Candidate
SUNY ESF

RE: AI-GEOSTATS: (1) Geostatistics in pain

[Seth J Myers]

I'm wondering what geostatistical software is best for handling very large data 
sets.  With the advent of GIS and remote sensing, having too much data is a 
problem.  Sampling of course is useful, but only to a point if a large study 
area is used.  I've read other places that among the commercial stat packages, 
SAS is best at handling large data sets.  Is this true?  Also, I've produced my 
own little routine in IDRISI that can create 'random' samples that are 
clustered by inverse distance, so that short lags are preferred.  Are there any 
software packages that can create a random sample of points that show a 
pre-specified clustering pattern in space? Thanks -Seth


From: owner-ai-geost...@jrc.ec.europa.eu [owner-ai-geost...@jrc.ec.europa.eu] 
on behalf of Edzer Pebesma [edzer.pebe...@uni-muenster.de]
Sent: Wednesday, January 06, 2010 2:52 PM
To: Younes Fadakar
Cc: ai-geostats@jrc.it
Subject: Re: AI-GEOSTATS: (1) Geostatistics in pain

Younes, thanks for the provocative message.

I believe you are looking for this free or cheap, all-capable package with a 
complete, friendly and robust graphical user interface with dynamic graphics. A 
problem is that such a thing is hard and expensive to develop, and unlikely to 
be arise as a side product of a research project. Look at the worlds of GIS or 
image analysis -- there's a lot of high quality things out there for free, but 
the thing you're looking for is very expensive.

In your list I missed at least:
9. ArcGIS + geostatistical analyst
10. SGEMS, the new Stanford software after GSLIB
11. other packages in R, such as gstat, randomFields, rsaga, and so on.
12. ... (I hope others will finish this list!)

I'm one of the many people active in the r-sig-geo community, and am constantly 
astonished about the growth of the activity around R; you can see some 
statistics on this in a paper in the latest issue of the R journal, e.g. fig 4 
in
http://journal.r-project.org/archive/2009-2/RJournal_2009-2_Fox.pdf

The special-interest-group on spatial data with R, r-sig-geo, undergoes a 
similar growth; it has now some 1400 subscribers and the development of mailing 
list activity is plotted in http://ifgi.uni-muenster.de/~epebe_01/r-sig-geo.png 
. A major part of this activity focuses around geostatistics.

Looking at these graphs, I have the impression that I'm not alone when thinking 
that although graphical, interactive exploratory data analysis is a very nice 
thing to have, a solid data analysis should start from the principle of 
reproducability, and therefore as little as possible depend on the reproduction 
of long sequences of mouse clicks.

Are users of this list aware of other communities and/or mailing lists where 
considerable activity around geostatistics and/or geostatistical software takes 
place?

Younes, could you be more precise about exactly which serious request [...] 
remained more than 20 years?

I hope your email gets many responses,
--
Edzer

Younes Fadakar wrote:

Hi there,

This is my first message as a test message checking the usage of the service, 
working with ai-geostats mailing list. I have many questions to ask you too.
To start:
The world of Geostatistics seriously needs a tool to present well to novices 
and professionals. Current availabilities have many of disadvantages. Some are 
too old, others not user-friendly and the rest more expensive.
1- GsLib seems powerful but too old (DOS-command line in 2010!)
2- WinGsLib is completely confusing despite of logging and automating! no 
direct input and output!!
3- Variowin is too weak in terms of GUI!
4- mGstat as a Matlab toolbox written too complex not handy program!
5- GeoR as an extention for R makes you to work with R such a command-line 
environment! what a development rather than GsLib!!
6- Isatis is more expensive; for what?!
7- Gs+ is in pain with weak performance of GUI!
8- Geoeas is something funny just to remember DOS graphics!
9- ...
So obviously a serious request remained for more than 20 years without suitable 
answer!
Why?


Younes


  
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--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763  http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics