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Let's say that I want to see how the spatial correlation between different phenomena, which geostatistical application is best to use then? One variable is the habitat and the second distribution of different plants that were collected in an area. Do I make myself understood? Or let say like this ... I have rather quantified if the conditions are good for a plant, rainfall, soil type, microclimate, geochemical conditions and so on. and produced an, admittedly somewhat arbitrary value between 1 - 10. Now I want to see how these different plants varies with the previous quantification. This was probably fairly accurately described. I have discussed this with colleagues and some suggest geographically weighted regression, which I also have been thinking about but which would become the explanatory and dependent on the value of such an analysis. GWR is perhaps not even the best approach for such an analysis??? Grateful for answers.
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08-07-2013
12:56 PM
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Let's say that I want to see how the spatial correlation between different phenomena, which geostatistical application is best to use then? One variable is the habitat and the second distribution of different plants that were collected in an area. Do I make myself understood? Or let say like this ... I have rather quantified if the conditions are good for a plant, rainfall, soil type, microclimate, geochemical conditions and so on. and produced an, admittedly somewhat arbitrary value between 1 - 10. Now I want to see how these different plants varies with the previous quantification. This was probably fairly accurately described. I have discussed this with colleagues and some suggest geographically weighted regression, which I also have been thinking about but which would become the explanatory and dependent on the value of such an analysis. GWR is perhaps not even the best approach for such an analysis??? Grateful for answers.
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08-06-2013
01:40 PM
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Interpolation will estimate values between known values. Density will only account for the number of values within a given area. Block statistics will not use your polygons (unless you create a kernel file for an irregular neighborhood which would take a great deal of time) to calculate your statistic. Although it may sound cumbersome, the process I outlined earlier is pretty straightforward and only requires three steps: 1. Convert raster to points 2. Select/delete null values 3. Spatial join points to polygons Okay, but can I get mean then? It might on the other hand not be very hard to calculate this with the field calculator for each polygon? What I mean with null values �??�??is that not all of the polygon covered by the grid. I might not have to deal with these at all then because they represent water surfaces, built up areas, etc..
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05-16-2013
12:53 PM
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Hello Kim, I believe the most efficient way to do this would be to convert the raster to points and perform a spatial join between the points and the polygon shapefile. To address for your 'no data' values in your raster, after converting the raster to points, you can start an edit session, perform a selection by attribute to select all 'null' valued points then delete your selection. Saving your edits and then performing your spatial join will then only use points with values to calculate your statistics (which in this case, you would select MEAN). Hope this helps! Best, Chris B. Okay that's what I thought. It is not appropriate to interpolate the raster values �??�??within polygons then, somehow? Or use some density analysis? Or block statistics?
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05-16-2013
12:16 PM
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I have raster data of a forest age in an area. The area is divided into polygons because of its diverse nature. I would like to obtain a generalized mean forest age in each polygon. How can I most easily carry out such an operation. What I find is to convert raster to shap files and then do a spatial join ... But there must be a better approach? Some parts of the polygons, in many cases, large portions, consisting of no data on the matter. Grateful for answers.
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05-16-2013
07:39 AM
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