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In terms of Principal Components Analysis or Multiple Discriminant Analysis, I have used dummy variables to represent a group of like-features that are located in the same geographic region. For example, in the analysis of two metropolitan cities, the census tracts that make up city one are classified as 0 and city two as 1. This binary classification groups the census tracts into two regions.

I am wondering how I could store geographic coordinates (centroid?) of these census tracts so that I could introduce a spatial component to the multivariate analysis. This is dissimilar to the approach mentioned above since I am not grouping any features; but rather, I am interested in determining whether or not spatiality is a statistically significant independent variable (in terms of regression), or if spatiality is a significant part of a component/factor (in terms of PCA/FA).

This would be simple enough if the variable was a single measured value; but alas, we have x and y coordinates to think about.

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closed as not a real question by Michael Markieta, Get Spatial, iant, R.K., Chad Cooper Nov 23 '12 at 21:39

It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center.If this question can be reworded to fit the rules in the help center, please edit the question.

This doesn't seem to be a GIS question. If you're going to do PCA with spatial locations, then you have to include x and y as separate variables: there's really no other option. You can also include other spatial variables such as distances to specified features, mean slopes from a DEM, or whatever. –  whuber Nov 19 '12 at 16:21
Thank you for clearing that up, moving along. –  Michael Markieta Nov 19 '12 at 16:39

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