New answers tagged spatial-statistics
The "high-high" cases in the Moran scatterplot are the values that represent "Counties with a high value of A tend to locate in regions with a high value of B". O'Sullivan and Unwin (2010, p. 222) describe this situation almost verbatim: "For example, "high-high" cases are ones where the value of i is high and neighboring values are also high." You ...
I would use the Points To Line tool first to create lines for each route from your GPS points. I would then Buffer these lines by a distance that you experiment with - it will be related to how much variation there is in your GPS accuracy. The last step will be to Intersect your buffers to see where they (i.e. your car routes) overlap.
I think a good choice would be Bivariate Moran's I or Bivariate Local Moran's I (also called Bivariate LISA for Local Indicators of Spatial Association), both of which can be implemented in GeoDa. The tutorial is "old", but except for some menu renaming is still pretty usefl, and of course the statistical concepts are still applicable. The basics are as ...
With ArcGIS, you can 1) convert your land cover image to polygons 2) dissolve all polygons based on the land cover field 3) generate 50 random point for each land cover polygon (select your land cover feature class as "constraining feature class") 4) Extract multiple values to points (this will give you the 70 band values as fields in the point feature ...
It's been a while since this question was asked, but I saw it by chance today and since I already had a similar problem, I thought I'd throw in my 2 cents. You might be interested in an algorithm called Mapcurves, which gives you a goodness-of-fit measure based on spatial overlap. The algorithm is described in this paper and an implementation in R and ...
You might want to look into the urban network analysis tool developed by MIT, you can identify the reach of specific features in a network. http://cityform.mit.edu/projects/urban-network-analysis.html
spatial analysis in macroecology might be a good solution. http://www.ecoevol.ufg.br/sam/
Maybe this answer comes 2 years too late, but anyway. To my knowledge, spatial clustering requires a defined neighborhood to which the clustering is constrained, at least at the beginning. The kulldorf function in the SpatialEpi package allows for spatial clustering based on aggregated neighborhoods. further the DBSCAN statistic available from the fpc ...
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