Ripley's k-function analysis

Anonymous User — Sat, 13 Oct 2012 17:34:25 GMT

Original User: anewlander

I have some questions regarding my project that I am hoping to get answered to make sure my methods are valid.
Here is a summary of my project and methods:
I have data from LiDAR that I made into a Vegetation Height map as determined by LiDAR. I then made those raster cells that were classified as 1-4 m tall plants into a point shapefile to represent tall plants across the landscape to assess global clustering.
The study area is fairly large. (See attachment) I then did a unweighted analysis seperately for 'above' and 'below' the road, in which the observed values showed clustering across all spatial scales, and the expected line falling above the confidence envelope, which I would have expected to fall within the envelope. Why would this happen?
I was then recommended to run a weighted analysis and then use weighted results combined with the unweighted CI and adjust the unweighted expected line to zero. I used a weight of 1, as weight represents the number of coincident features at each feature location. Since these points essentially represent a 1x1 m area covered by 1 m tall vegetation it is more an index of cover, and does not necessarily represent just 1 plant. Is this an accurate way to use the weighted function? Will using a value of 1 be acceptable in this study? My results are very different than those obtained from the unweighted observed values.(see figures for unweighted results and weighted observed with unweighted CI and exp).
I have also read in ArcGIS help that the largest diffk represents the distance where spatial processes promoting clustering is most pronounced. Is this still the case when I use unweighted CI and expectation and the weighted observed values?

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Ripley's k-function analysis