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0

Take a look at http://geostat-course.org/node - they have lots of courses, great resources. I would recommend taking a look at a couple of very good books: Elementary Statistics for Geographers, Third Edition Practical Statistics for Geographers and Earth Scientists An Introduction to Applied Geostatistics Geostatistics: Modeling Spatial Uncertainty ...


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Coursera has a lot of great courses. I know that they had a geostatistics course in the past. Maybe something will appear soon. https://www.coursera.org/courses?query=statistics


4

Gi* doesn't care about distance, it cares about weights (which you could set to inverse distance if you want...) so you need to think about how to form weights between raster cells on the whole globe. This is tricky, because at the poles, your cells are a different shape and area to those on the equator, and have a different adjacency relationship with ...


2

I'm currently struggling with similar issues, so feel free to wait for a more authoritative answer. However, I can't recommend an equidistant projection. It sounds great, but keep in mind that the distances are true only along specific lines. Measured along other directions, the distances will not be true. I doubt that you want to use a single projected ...


0

While agreeing with @ChrisW about the question being too vague; here are few pointers to get you started. It sounds that Krigging is a good option, and in particular the probabilities map. Note that any question which seek to know literally: "what is...or... how to perform krigging?" is much too broad. Regarding the differece map. You started well and you ...


3

It is just a synonym for the raster data you want to analyse. Unlike vector features, which are built from a geometry attribute (i.e. coordinates) and an attribute table, rasters are both geometry and attributes. A raster is a grid of cells (pixels), each containing a VALUE (hence 'raster value'). The value can stand for whatever attribute one wishes, yet ...


14

My guess is that you coordinate transformations have introduced tiny rounding errors (see an example below). As there is no way to set the tolerance in ST_Equals, this is causing ST_Equals to return false for some geometries that only differ in the nth decimal place, as the geometries have to be identical in every respect -- see the intersection matrix ...


2

Did you run ST_IsValid check on your geometries? If they are invalid, all bets are off. ST_Intersects and the other family of GEOS spatial relationship functions will often just return false because the area is not well-defined from an intersection matrix point of view. The reason doing ST_Buffer probably works is because it's converting your invalid ...


2

One possibility underlying the poor kriging performance could be the field plots (location and sampling intensity) failing to capture the spatial autocorrelation (or spatial dependence) in the data. It would be necessary to observe how well the theoretical semivariogram fitted to the data (experimental semivariogram) (Figure 1). If it is the case, one ...


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Check geosphere package distance function or fossil deg.dist function. You have data in degrees and need to translate it into meters or feet before doing clustering.



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