Hi All, I am new to this form and new to R, having just initiated the analysis of my first project using R. I have been working on a logistic model of land use change and am concerned about 1) measuring spatial autocorrelation and 2) including an autocovariate in my model. Here is what I think I need to do. My dependent variable takes on the value of either 1 or 0 depending on whether a particular pixel from a random sample of pixels transitioned from undeveloped to developed during the study?s time period. I?d like to measure spatial autocorrelation by first creating a distance matrix which includes the distances from each of my sample points to every other sample point using latitude and longitude coordinates. I would then like to divide these distances into bins (e.g. 0 to 500m, 501 to 1000m etc.). I have done this using both the dist() function in geoR and the earth.dist() function in fossil. Both seem to work. I would then like to calculate the number of similar states (i.e. joint counts) between pairs of sample points at various distances (i.e. bin size). I can then graph this as proportion of disconcordance in land use change versus distance (i.e. disconcordance is when one point of the pair transitions and the other does not) very similar to that done by McDonald and Urban (2006). I?d expect that as distance increases, the proportion disconcordance eventually reaches that of the entire sample or more correctly, that of a totally random spatial process. How can I assess the joint counts most easily? Are there other approaches folks would recommend? Remember, I am somewhat of a newbie. The last wrinkle is that I am using multi-level models, which with varying intercepts and/or slopes seems to account for some spatial heterogeneity. I am not sure how to think about the pairing of multilevel and autologistic approaches. Any insights? Sample data below Long Lat Trans -87.9424 47.46495 0 -88.0451 47.464 0 -82.7524 42.89477 1 -86.6905 45.5972 0 -87.7316 46.57988 0 -82.4769 43.05674 1 -83.4313 42.42828 0 -86.2598 44.37078 0 -86.2559 44.66841 0 -86.2467 44.67979 0 -- View this message in context: http://old.nabble.com/Joint-counts-and-spatial-autocorrelation---binary-data-tp26306008p26306008.html Sent from the R help mailing list archive at Nabble.com.