[ai-geostats] Comparison of two point datasets
- Dear listers,
My question is about comparing two value lists mapped onto the same
We have calculated a kind of economical potential estimate from data
collected for 576 towns from gravitation modell using linear distances
between towns first, then using the minimum time required to reach one
town from the other via road network. So, we have two values for the
same 576 points which differ from each other with some magnitude and in
spatial distribution, too. We would like to spatially compare two data
to find areas where the two methods gives the most different results.
Our idea was to calculate a (simple) linear regression for the two
values, and then map the residual values, looking for the continuous sub
areas where residuals are autocorrelated. We are not interested in
estimation, but in finding the anomaleous places, where one modell
locally significally over- or underestimates the other one.
I am interested in your opinion about this approach, I would be thankful
to get ideas about probably better (standard?) methods for comparing two
mapped value sets.
- Dear all,
I have difficulties to find information's about the following problem.
I have a lot of spatially scattered measurements. These measurements
have - resulting from different measurement methods - different
measurement errors, which are known.
For example some have an total error of 5%, some of 10% and a third
group of 20%.
I want to give these values a quality-weight in the range from 0.0 to
1.0. (In this case three different weights.)
How can I do this?
Simple is a weight = 0 which is a value so bad I don't want to use it,
and a weight = 1 which could be the value for the group with the best
measurements (in this case error = 5%).
Is there a statistically firmed way to quantify the weights.
Any suggestions will be very welcome.
Is there any literature that discusses this matter?
Thanks in advance.