xhy

your questions are long-standing and as yet unanswered
in general.

> 1. How to select the lag class and lag distance in
> order to obtain a more reasonable experimental
> variogram?
I always think of it as focussing a camera. Believe
there is a pattern in your data and our task is to
balance 'width of interval' versus 'number of pairs in
interval' to get the clearest picture.

One of the things I have found most useful with
irregularly spaced data is a 'nearest neighbour'
analysis. Take each sample and find the closest one to
it. Record the distance. Repeat for all samples. This
process takes twice as long as calculating the
semi-variogram but gives you an idea of the 'natural'
or model spacing between your samples. This can be
used to guide your choice of interval.

Check out our free tutorial downloads at
http://geoecosse.bizland.com/softwares

> 2. Is it reasonable to use an uneven set of lag
> (e.g. the lag increments are: 0-2.5m, 2.5-5.0m,
> 5.0-12.0m, 12.0-19.5m, 19.5-27.0m, 27.0-30.0m,
> 30.0-40m, 40-50m etc.) if a more stable variogram
> can be obtained?
I am not sure I have ever seen this done, but don't
see why not if you plot the point at the centre of
gravity of your interval (i.e. average distance of
pairs found).

Hope this helps
Isobel
http://geoecosse.bizland.com/books.htm





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Hi,

I am wondering if you have any thought on this interpretation:

Supposing i have 2 different data sets X and Y. I know that both
data sets are actually a combinations of 2 other data sets, a
background x1 and a contaminating x2 data sets for X and similar
for Y (with y1 and y2).

I encounter 2 cases: a) classical correlation is higher than robust
correlation and b) robust correlation is higher than classical
correlation.

How i am interpreting the 2 cases? I would like to be able to say
that in case a) the correlation between the contaminating values of
both X and Y increases the overall correlation coefficient, while
when using the robust correlation, the correlation between the
contaminating sets is lost.

Of corse, the reverse is true for case b). The contaminating sets
are not correlated so including them in the calculation of the
classical correlation will decrease this coefficient, while eliminating
these contaminating sets, the robust correlation characterizes the
background data.

I will really appreciate any thoughts about the interpretation above.

thanks,

Monica
Monica Palaseanu-Lovejoy
University of Manchester
School of Geography
Mansfield Cooper Bld. 3.21
Oxford Road
Manchester M13 9PL
England, UK
Tel: +44 (0) 275 8689
Email: monica.palaseanu-lovejoy@...

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Dear List:

I got a question about the uncertainty associated with the Conditional
Stochastic Simulated results, the question is:

I have soil C data for a sample area, within that area, there are woodland
and grassland. The variance of C data in woodland is higher than that in the
grassland. Now I want to use Conditional Stochastic Simulation to simulate a
bunch of images of soil C on the area. I will use one single variogram model
to do the simulation. The questions is: Do the estimated C data more
variable under woodland than those under grassland (simulated soil C data in
woodland have a larger variance than those in grassland)? or a single
variogram model means uniform or random variance of simulated data?

Thank you very much

Feng Liu
Graduate Student
Texas A&M University

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Hi Feng,

You are in the ideal situation where it would make sense
to separate the two populations and do the variography
and simulation separately for each stratum, since clearly
the pattern of variability of soil carbon is expected to be
different under these two landcovers (I am sure that besides the
variance, the range and nugget effect must be pretty different)
and also the average C concentration should vary a lot .
In most situations it is difficult to consider different populations since:
1. we might not have enough data within each stratum to compute a
reliable variogram,
2. the boundaries of the different strata might be fuzzy,
3. the multi strata approach creates discontinuities in the map
that are not physically realistic.
I don't know your data but I am pretty sure that none of these limitations
apply.

Now to answer your question, the use of a single semivariogram implies
an assumption of stationarity, hence the fact that the
variance/covariance does not depend on the location within the study area,
which is clearly not your case. Even wen using a single variogram,
the variability in your simulated map will be influenced to some
extent by your data (conditioning observations). Hence, I suspect
that more variability will appear on woodland anyways.
By analogy, the same happens when you use an isotropic semivariogram
in simulation/estimation, while the data display strong anisotropy.
The simulated/estimated map will display some anisotropy, which is
always reassuring.

Cheers,

Pierre
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Dr. Pierre Goovaerts
President of PGeostat, LLC
Chief Scientist with Biomedware Inc.
710 Ridgemont Lane
Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail:  goovaert@...
Phone:   (734) 668-9900
Fax:     (734) 668-7788
http://alumni.engin.umich.edu/~goovaert/

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

On Sun, 10 Oct 2004, Feng Liu wrote:

> Dear List:
>
> I got a question about the uncertainty associated with the Conditional
> Stochastic Simulated results, the question is:
>
> I have soil C data for a sample area, within that area, there are woodland
> and grassland. The variance of C data in woodland is higher than that in the
> grassland. Now I want to use Conditional Stochastic Simulation to simulate a
> bunch of images of soil C on the area. I will use one single variogram model
> to do the simulation. The questions is: Do the estimated C data more
> variable under woodland than those under grassland (simulated soil C data in
> woodland have a larger variance than those in grassland)? or a single
> variogram model means uniform or random variance of simulated data?
>
> Thank you very much
>
> Feng Liu
> Graduate Student
> Texas A&M University
>
>
>
>

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Hi,
I have been trying to compare the autocorrelation of a dataset, using both spatial distance and other non spatial metrics. Yes, non spatial metrics (I'm a demographer).

1) I have a first problem when I want to compute the distance using more than two non spatial variables (ie, coordinates): I don't know of any software that would let me compute autocorrelation or a semivariogram over various distance ranges when the distance is to be computed from more than two coordinates. I'd rather avoid computing distances and autocorrelation with my usual statistical software.
Any suggestion ?

2) Once I do that, I have a further problem comparing spatial and non spatial autocorrelation: distances are follow different metrics and distributions are differently shaped. The idea is to contrast spatial proximity with "proximity" measured with other variables. To compare these distances, should I sort my pairs of observations into distance quantiles (the first 100 pairs, etc.), into standardized distance (with maximum range =100 or average distance=100)?
Any idea on that?

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Dear Christophe,
Maybe you could use Mantel statistics. You first need a relevant multivariate
distance measure (see e.g. Legendre & Legendre 198? "Numerical Ecology"), then
you create a distance matrix with distances in simillarity (m x n). Finally the
geographical distance matrix (m x n) is correlated with your multivariate
distance matrix through Monte Carlo randomisations. Another reference here is
'isolation by distance' in Sokal & Rohlf 199? "Biometry".

Jakob

Quoting Christophe Z Guilmoto <guilmoto@...>:

> Hi,
> I have been trying to compare the autocorrelation of a dataset, using both
> spatial distance and other non spatial metrics. Yes, non spatial metrics
> (I'm a demographer).
>
> 1) I have a first problem when I want to compute the distance using more
> than two non spatial variables (ie, coordinates): I don't know of any
> software that would let me compute autocorrelation or a semivariogram over
> various distance ranges when the distance is to be computed from more than
> two coordinates. I'd rather avoid computing distances and autocorrelation
> with my usual statistical software.
> Any suggestion ?
>
> 2) Once I do that, I have a further problem comparing spatial and non
> spatial autocorrelation: distances are follow different metrics and
> distributions are differently shaped. The idea is to contrast spatial
> proximity with "proximity" measured with other variables. To compare these
> distances, should I sort my pairs of observations into distance quantiles
> (the first 100 pairs, etc.), into standardized distance (with maximum range
> =100 or average distance=100)?
> Any idea on that?
>
> Thanks
>
> CZG
>
>
>
>
> Christophe Z. Guilmoto
> Demographe, IRD
> CEIAS-EHESS
> 54, Boulevard Raspail
> 75006 Paris  France
> Tél.: 06 67 19 87 10


--
Jakob Petersen
Research Technician
School of Biological Sciences
M. Trimmer laboratory (1.05)
Queen Mary
University of London
Mile End
London
E1 4NS
United Kingdom

Tel +44 (0)20 7882 3200
Fax +44 (0)20 8983 0973

Directions:
http://www.qmul.ac.uk/contact/directions.shtml

Map:
http://www.qmul.ac.uk/contact/mileend.shtml
We are in lab 1.05 on the 1st floor in building 24. The main entrance is to the
East.

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Dear ai-geostats Members,

Are there appropriate statistical tests for determining whether empirical
variograms are statistically different (maybe something akin to a
Kolmogorov-Smirnoff or chi-square test)?

Is there a test for comparing variogram model parameters?

If not, why?  If so, what assumptions are required?

Does anyone know of a published reference where such comparisons have been made?

Thank you,
Randy Cutter.

-------------------------------------
University of New Hampshire
Center for Coastal and Ocean Mapping
Chase Ocean Engineering Lab,
24 Colovos Rd., Durham, NH  03824
Phone 603-862-0564  Fax 603-862-0839
email - gcutter@...
CCOM website: http://www.ccom.unh.edu
-------------------------------------

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Hello mail list

I have a set of profile of GPR data in format *.rad and *.rd3

where I can download a freeware for convert in e grid?

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Try Lark, R.M. 2000. A comparison of some robust estimators of the variogram for
use in soil survey. European Journal of Soil Science 51, 137-157. This paper
gives a method of testing wether a variogram is 'correct' or not - ie not
overestimated due to outliers etc.

Pat Bellamy


Mrs Pat Bellamy B.Sc. M.Sc.
Statistician/Computer Analyst
National Soil Resources Institute (NSRI),
Cranfield University at Silsoe,
Silsoe,Bedfordshire,
MK45 4DT,
UK
-------------------------------------------------------------------
Tel.: +44.(0)1525.863260 (direct)
Fax.: +44.(0)1525.863253
Email: p.bellamy@...
http://www.cranfield.ac.uk/nsri/



-----Original Message-----
From: George R Cutter [mailto:gcutter@...]
Sent: 15 October 2004 18:42
To: ai-geostats@...
Subject: [ai-geostats] comparing variograms?


Dear ai-geostats Members,

Are there appropriate statistical tests for determining whether empirical
variograms are statistically different (maybe something akin to a
Kolmogorov-Smirnoff or chi-square test)?

Is there a test for comparing variogram model parameters?

If not, why?  If so, what assumptions are required?

Does anyone know of a published reference where such comparisons have been made?

Thank you,
Randy Cutter.

-------------------------------------
University of New Hampshire
Center for Coastal and Ocean Mapping
Chase Ocean Engineering Lab,
24 Colovos Rd., Durham, NH  03824
Phone 603-862-0564  Fax 603-862-0839
email - gcutter@...
CCOM website: http://www.ccom.unh.edu
-------------------------------------

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Mark

We have about 13 data sets available on our free
download site, ranging from mining data to fisheries,
agriculture and environmental stuff. Number of data
ranges from 27 to 20,000.

Download from http://geoecosse.bizland.com/softwares
and find details and references for most of them at
http://geoecosse.bizland.com/bookbits/Chapter1_PG2000.pdf

Isobel





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Hello,
Try:
SCHABENBERGER O & PIERCE FJ (2002) Contemporary statistical models for
the plant and soil sciences. CRC Press, Boca Raton, Florida.

They compare theoretical semivariograms between treatments through non
linear regression. They use something like to the extra sum of squares.
Details on this one on:
RATKOWSKY DA (1983) Nonlinear Regression Modeling: a Unified Practical
Approach. M. Dekker, New York.

However, it does not convince me. From my point of view, comparisson of
spatial structures through non linear regression is a kind of artifact.

And remember:
“He uses statistics as a drunken man uses lamp-posts... for support
rather than illumination." - Andrew Lang
or
"Statistics are like bikinis. What they reveal is suggestive, but what
they conceal is vital" - Aaron Levenstein
or
"Torture numbers, and they'll confess to anything" - Gregg Easterbrook



George R Cutter wrote:

>Dear ai-geostats Members,
>
>Are there appropriate statistical tests for determining whether empirical
>variograms are statistically different (maybe something akin to a
>Kolmogorov-Smirnoff or chi-square test)?
>
>Is there a test for comparing variogram model parameters?
>
>If not, why?  If so, what assumptions are required?
>
>Does anyone know of a published reference where such comparisons have been
made?
>
>Thank you,
>Randy Cutter.
>
>-------------------------------------
>University of New Hampshire
>Center for Coastal and Ocean Mapping
>Chase Ocean Engineering Lab,
>24 Colovos Rd., Durham, NH  03824
>Phone 603-862-0564  Fax 603-862-0839
>email - gcutter@...
>CCOM website: http://www.ccom.unh.edu
>-------------------------------------
>
>
>

--
José Manuel Blanco Moreno
Ph.D Student

---------------------------------------
José-Manuel Blanco-Moreno

Dept. de Biologia Vegetal (Botànica)
Universitat de Barcelona
Av. Diagonal 645
08028 Barcelona
SPAIN
---------------------------------------

phone: (+34)93.402.1471
fax: (+34)93.411.2842
e-mail: jmblanco@...

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Greetings,

I am coding some basic geostatistical estimators, including the
variogram, and would like to find some sample data sets for testing.
Can someone point me to any online datasets?

Thanks,

-Mark

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Hi Andrew,

A critical piece of information is the objective function
you want to optimize. I have worked in the field of optimization of
sampling design applied to mechanical engineering and ergonomics,
see papers by Sasena et al. that you can download from my webpage.
I believe it has great application in the field of design of
network of environmental sensors and I am open to collaborations
on this topic.

Regards,

Pierre


<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Dr. Pierre Goovaerts
President of PGeostat, LLC
Chief Scientist with Biomedware Inc.
710 Ridgemont Lane
Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail:  goovaert@...
Phone:   (734) 668-9900
Fax:     (734) 668-7788
http://alumni.engin.umich.edu/~goovaert/

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

On Thu, 21 Oct 2004, Andrew Baek wrote:

> Greetings!
>
> I am looking for an efficient geometric design for installing bunch of
> robots in a field. These sensors(or sensor networks) are not static, in
> the sense that they constantly move and sample data(light, temperature,
> humidity etc.).
> Basically, I'd like to find different schemes - not a square/triangle
> shape from Yfantis et al(1987)- for my problem. Also, this is different
> from space filling since order matters. I mean, the cost will be quite
> different whether robot starts from edge or center. My guess is that
> curve (roulette shape?) will be more favorable in this case, but I
> don't know... Can someone refer me to some references or suggestions?
>
> THX,
> Andrew

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Samuel

Practical Geostatistics (1979) Chapter 3. Get it for
free at
http://uk.geocities.com/drisobelclark/practica.htm

Isobel
http://geoecosse.bizland.com/books.htm

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Hi Samuel,

I have dealt with similar problems when analyzing the spatial
distribution of dioxin and other heavy metals in river sediments.
Core lengths can strongly fluctuate from one sampling point to the
next. The empirical approach I used was to weigh each sample
proportionally to its length both in the computation of semivariograms
(use of weighted semivariogram estimators) and in the kriging
procedure (rescaling of kriging weights to account for core length).
There was no publication on this approach and reports are confidential.
These days I would use a less empirical approach and capitalize on the
analogy with the treatment of cancer rates, where the reliability of rates
is a function of the population size. You could still use weighted
semivariogram estimator, but use a "kriging with measurement error"
approach, whereby an error variance term (here inversely proportional
to the length of the core) is added to the diagonal elemnts of the
kriging matrix.

Here is just a suggestion but I am sure that some mining geostaticians
will come up with a more elegant solution.. I also think that Jayme
Gomez presented a paper on this issue (and the downscaling or
disaggregation problem in general) at the last geostat congress in
Banff, but since I only caught the last part of his presentation I
might be wrong.

Pierre
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Dr. Pierre Goovaerts
President of PGeostat, LLC
Chief Scientist with Biomedware Inc.
710 Ridgemont Lane
Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail:  goovaert@...
Phone:   (734) 668-9900
Fax:     (734) 668-7788
http://alumni.engin.umich.edu/~goovaert/

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

On Tue, 26 Oct 2004, samuel verstraete wrote:

> Hi,
>
> I have a 3D data set that has been sampled by a private company. They
> lacked a complete knowledge of geostatistics so there is no sampling
> "strategy" involved. Another thing is that the support of the samples is
> strongly fluctuating. Horizontally the sampling support is constant and
> can be considered as a point (about 70cm^2 compared to a few hectares)
> Vertically the sampling support is not stable and rather "huge" in
> comparison with the vertical scale... (sampling can be 0.10 to 1 meter
> and maximum depth would be 5 to 6 meter or even less)
>
> I've read in the literature that there is a possibility to correct for
> such a things, through regularization. But none of the literature seems
> to discuss the possibility that the samples themself do not always have
> the same support, as stated before samples can have a support that is 10
> times bigger than the smallest sample.
>
> Question is... Is there any other literature that discusses this matter
> and even more importantly is there any software out there that can take
> this sampling support into consideration when I'm calculating the
> variogram or when I start with estimation/simulation of the field.
>
>
> Thanks in advance,
>
> --
> Samuel Verstraete
> Ghent University
> Faculty of Bioscience Engineering
> Dept. of Soil Management and Soil Care
> Coupure Links 653, B-9000 Gent, Belgium
>
>
>
>

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Two relevant publications mentioned by Carol Gotway during her
geoENV keynote (I happen to have them both on my desk right
now, but haven't read them yet):

Gotway & Young, 2002, Combining incompatible spatial data,
JASA (I don't have the issue and page numbers) -- this paper
deals with block kriging when the data are observed on blocks
with varying size

A Mockus, 1998, Estimating dependencies from spatial averages.
Journal of computational and graphical statistics 7:4, 501-513. --
this paper explains how to estimate the point support variogram
(probably up to the nugget) from blocks of varying size.

If you find software for either of the issues in these papers, please let
the list know.
--
Edzer


samuel verstraete wrote:

>Hi,
>
>I have a 3D data set that has been sampled by a private company. They
>lacked a complete knowledge of geostatistics so there is no sampling
>"strategy" involved. Another thing is that the support of the samples is
>strongly fluctuating. Horizontally the sampling support is constant and
>can be considered as a point (about 70cm^2 compared to a few hectares)
>Vertically the sampling support is not stable and rather "huge" in
>comparison with the vertical scale... (sampling can be 0.10 to 1 meter
>and maximum depth would be 5 to 6 meter or even less)
>
>I've read in the literature that there is a possibility to correct for
>such a things, through regularization. But none of the literature seems
>to discuss the possibility that the samples themself do not always have
>the same support, as stated before samples can have a support that is 10
>times bigger than the smallest sample.
>
>Question is... Is there any other literature that discusses this matter
>and even more importantly is there any software out there that can take
>this sampling support into consideration when I'm calculating the
>variogram or when I start with estimation/simulation of the field.
>
>
>Thanks in advance,
>
>
>
>------------------------------------------------------------------------
>
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>

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Dear forum members,



We are using nested model with geometrical anizotropy
to remove artificial imprints connected to high
frequency measurement errors along the lines of
seismic survey.
So we are using model of one isotropic component with
big range and one anizotropic component with small
range.
The question:
Does it have any sense to fit the model as above to
the isotropic raw variogram? Or shall we fit
separately for each component?

many thanks in advance,

Gali Sirkis





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Greetings!

I am looking for an efficient geometric design for installing bunch of
robots in a field. These sensors(or sensor networks) are not static, in
the sense that they constantly move and sample data(light, temperature,
humidity etc.).
Basically, I'd like to find different schemes - not a square/triangle
shape from Yfantis et al(1987)- for my problem. Also, this is different
from space filling since order matters. I mean, the cost will be quite
different whether robot starts from edge or center. My guess is that
curve (roulette shape?) will be more favorable in this case, but I
don't know... Can someone refer me to some references or suggestions?

THX,
Andrew

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Dean

Couple of possible explanations. Try these for size:

(1) you may have points which are very close together
if not with identical co-ordinates. Bear in mind that
'close together' depends on the precision of your
software. Most software works to around 8 significant
figures. If you co-ordinates are in the millions, the
computer will not be able to see the difference
between two samples at a distance less than (say) 1.
First check your data for duplicate samples (most
common occrrence!).

(2) you may be using one of the semi-variogram models
which is not terribly stable, like the 'hole effect'
or the gaussian. The above problem intensifies if you
use a gaussian, because 'too close' now means the
whole of the initial curve on the model.

(3) you may have an equation solving routine which
loses precision very fast and be using too many
samples.

If none of these does it for you, I'll try to think of
others!

Isobel
http://geoecosse.bizland.com/softwares

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Hi,

I have a 3D data set that has been sampled by a private company. They
lacked a complete knowledge of geostatistics so there is no sampling
"strategy" involved. Another thing is that the support of the samples is
strongly fluctuating. Horizontally the sampling support is constant and
can be considered as a point (about 70cm^2 compared to a few hectares)
Vertically the sampling support is not stable and rather "huge" in
comparison with the vertical scale... (sampling can be 0.10 to 1 meter
and maximum depth would be 5 to 6 meter or even less)

I've read in the literature that there is a possibility to correct for
such a things, through regularization. But none of the literature seems
to discuss the possibility that the samples themself do not always have
the same support, as stated before samples can have a support that is 10
times bigger than the smallest sample.

Question is... Is there any other literature that discusses this matter
and even more importantly is there any software out there that can take
this sampling support into consideration when I'm calculating the
variogram or when I start with estimation/simulation of the field.


Thanks in advance,

--
Samuel Verstraete
Ghent University
Faculty of Bioscience Engineering
Dept. of Soil Management and Soil Care
Coupure Links 653, B-9000 Gent, Belgium

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Dear all,
I created a few minutes ago by means of yahoo's facilities a discussion group called AI-GEOSTATS lounge. See http://groups.yahoo.com/group/ai-geostats-lounge/

AI-GEOSTATS lounge should be an informal meeting place for the subscribers of ai-geostats, that is for anyone working in the field of spatial statistics and spatial data analysis. You could use this virtual lounge to  meet colleagues, propose appointments and visits while travelling (e.g. I'm going to Singapore and Cambodia next month). It may also help when looking for data, project partners or for fundings for projects related to spatial statistics. Whatever you have in mind really that is not supposed to be sent to ai-geostats but still remains indirectly related to spatial data analysis and to the community of ai-geostats could be discussed there.

Tolerance remains of course essential and people making sarcastic comments or even insulting other subscribers will immediately be kicked out of the list without warning and with no possibility to subscribe again.

You can post messages to : ai-geostats-lounge@yahoogroups.com

http://groups.yahoo.com/group/ai-geostats-lounge/

Hope this will make life easier and more pleasant.

The group is at the moment not moderated.

Best wishes,

Gregoire (owner of ai-geostats)

PS: I didn’t duplicate the list, people who want to join the lounge should therefore subscribe
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Email: gregoire.dubois@...
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Hi-

It has been my habit to fit a variogram models as NUG + SPH + SPH
omnidirectionally to get Nugget and two partial sills and then use these
values it other directions and vary the two ranges in each direction. Can
gstat accept values for nug and two partial sills and then fit just the
ranges?

Thanx

Robert (Bob) L. Sandefur PE
Senior Geostatistician / Reserve Analyst
CAM
200 Union Suite G-13
Lakewood, Co
80228

rsandefur@...

303 472-3240 (cell) <-best  choice

303 716-1617 ext 14

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Dear list members,

I'm trying to build a spatial simulation model of population dynamics and
wanted to ask for your input and suggestions on how to accomplish this. The
characteristics of the model are as follows:
1) The model consists of 100 (or more) discrete populations at randomly
picked locations on a regular grid. I want to have enough populations to
later do spatial statistics such as variograms on the populations.
2) Each population has the basic population dynamics of birth (birth rate
limited by carrying capacity), death, emigration as a fraction of the
population size, and immigration as whatever comes from other populations.
3) The populations differ in their carrying capacities to simulate different
habitat qualities. I would like to distribute carrying capacities over the
grid so that they are randomly distributed with a certain degree of spatial
autocorrelation.
4) All populations are connected via emigration and immigration. The closer
together they are, the stronger the connection (i.e., the higher the
dispersal rate between them). If a certain number of individuals emigrate
from a population as determined by a fixed fraction of the population size,
I could either distribute them in a stochastic manner to other populations
with the probability of drawing an individual inversely related to the
distance or distribute fractions of individuals deterministically according
to the distance of the neighbors.

Do you have suggestions on how to accomplish this? I'm fairly proficient in
Splus, STELLA (that's what I built the population dynamics model in) and
ArcView (but not Avenue or ArcInfo).  I would not be able to program this
from scratch in C or a similar programming language.

Thank you.

Volker

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Volker Bahn

Dept. of Wildlife Ecology - Rm. 210
University of Maine
5755 Nutting Hall
Orono, Maine
04469-5755, USA
Tel. (207) 581 2799
Fax: (207) 581 2858
volker.bahn@...
http://www.wle.umaine.edu/used_text%20files/Volker%20Bahn/home.htm

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