Dear Syed, et al.,
I did much of what you described in the GRASS GIS a while back. (GRASS
is public domain, not commercial, but it is a very good GIS.) The title
of the paper is "Visualizing Spatial Data Uncertainty Using Animation"
and a copy of it is located at:
http://www.geo.hunter.cuny.edu/
Dear all,
Thanks for so many interesting replies and thoughtful discussion. This is
not a summary yet, as I am expecting more to come.
Just to express my feeling about Indicator Kriging. To produce a probability
map, IK might be one of the choices. However, I always feel that too much
informatio
>From: "McKenna, Sean A" <[EMAIL PROTECTED]>
>
>1) When trying to explain the concepts of spatial variability and
>uncertainty, we have found that showing example realizations of what the
>possible distribution of contaminants could look like provides the groups
>involved to get a more intuitive
MS 0735
Albuquerque, NM 87185-0735
ph: 505 844-2450
-Original Message-
From: Chaosheng Zhang [mailto:[EMAIL PROTECTED]]
Sent: Monday, April 29, 2002 3:57 AM
To: Pierre Goovaerts
Cc: [EMAIL PROTECTED]; Dave McGrath
Subject: Re: AI-GEOSTATS: Risk Assessment with Gaussian Simulation?
Pierre,
Hi Brian,
One hundred realizations are typically generated
mainly for CPU reasons.
You are perfectly right that this number is
too small when looking at small probabilities
like 0.05 or 0.01. It's why I wouldn't recommend
using stochastic simulation to derive probability of occurrence
of events a
I am curious about the use of 100 realizations to generate a probability
map. is this a standard approach? if so, is a "small" p-value (such as
.05) used? if so, it would seem like 100 iterations might be a smallish
sample size for distinguishing, say, .05 (ie 5 outcomes out of 100) from,
say,
erts" <[EMAIL PROTECTED]>
To: "Chaosheng Zhang" <[EMAIL PROTECTED]>
Cc: <[EMAIL PROTECTED]>; "Dave McGrath" <[EMAIL PROTECTED]>
Sent: Saturday, April 27, 2002 4:53 PM
Subject: Re: AI-GEOSTATS: Risk Assessment with Gaussian Simulation?
> Hello,
Hello,
In the past few years stochastic simulation has
been increasingly used to produce probability maps.
To my opinion it's generally a waste of CPU time since
similar information can be retrieved using kriging,
either in a multiGaussian framework or applied to
indicator transforms.
The issue o
