Are you comparing an actual vs. predicted road, is that what I understand?
If so, I don't think spatial autocorrelation measures are appropriate.
What is your question? Is the GIS model generating a road from any sort of
stochastic process? If it's COSTPATH, I'm assuming you're placing the
simulated road in the most "efficient" location? (e.g., least cost?)?
I would suggest creating some sort of map with predicted and acutal location
- if the COSTPATH road represents an idealized road, I would assign that
road a value of 0, then create another grid with increasing values for
terrain that is less suitable for a road. For example, if you're just
looking at topography, reclassify the grid according to some measure of
ruggedness (in however many categories you think apply), and then create a
grid of the acutal road (with some value that you know will stand out), and
then subtract the topography grid, then overlay the simulated road grid - so
that it has a value of 0 wherever you have a correct prediction, and
increasing negative values the less suitable the terrain.
Does that make sense? Plotting model results is something I and some
colleagues have been discussing for a while - summarizing model results
spatially to show accuracy.
Hope that was helpful - just a suggestion.
Darla
*******************************
Darla Munroe
Postdoctoral Fellow
Indiana University
Center for the Study of Institutions,
Population, and Environmental Change
[EMAIL PROTECTED]
-----Original Message-----
From: Ionut Aron
To: [EMAIL PROTECTED]
Sent: 7/5/2001 3:06 PM
Subject: AI-GEOSTATS: spatial correlation of forest roads
Hello everybody,
I am modelling forest roads in GIS and I' d like to find for a specific
road
how similar is this road with the one generated with the GIS model. I am
working with ArcINFO and the datasets are in raster format (generated
with
PATHDIST and COSTPATH functions). ArcINFO has a number of spatial
autocorrelation functions (Moran, Geary, Correlation). Are these
functions
appropriate for my purpose? Any other ideas?
Thank you very much.
Ionut
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