I have a set of non-regular point locations (x,y), where for each location I have a set of attributes (a,b). I am trying to check if the ATTRIBUTES are random or not. I am trying to do this by using two descriptors: a correlelogram and a normalized cumulative spectrum (from Jenkins and Watts, 1968), where for each descriptor confidence intervals are computed. In some cases both tests show that the attributes are random (that is not significant in respect to the confidence intervals), where in other cases the correlelogram is significant while the normalized cumulative spectrum shows no significance.
Following this my questions are:
a) Is it justified in this case to use a spectral analysis method here ?.
b) Which test is more reliable for checking randomness in case one of the descriptors contradicts the other?. I did not find any reference to this question except one, where it was mentioned that correlations may occur even in a completely random data set and therefore spectral methods should be more reliable. Is there any reference where an analysis of reliability is available?
c) Would a marked point processes analysis be a more appropriate framework for my problem?, does it depend whether the attributes are assumed to be continuous, or is it a matter of concepts.
Thank you for your kind assistance and for your patience,
Arie Croitoru, Ph.D. candidate
Technion - Israel Institute of Technology
Faculty of Civil Engineering,
Department of Geodetic Engineering
Technion City, Haifa 32000, ISRAEL
Tel: +972-4-8292663, Fax: +972-4-8234757
[Non-text portions of this message have been removed]