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When you use "default values" you aren't really kriging, you're just applying the kriging algorithm--which as you have found, is poor when used with these data. (I will step up on a soapbox for a brief rant: in my opinion, the fastest way to get bad results with a computer program is to accept its default parameters. ArcGIS is one of the richest, most ...


16

Introduction Because this issue (of discrepancies in standard deviations, variances, or other statistical summaries) comes up periodically, especially when a thoughtful and careful GIS analyst checks their work, I thought it would be good to share the "forensic analysis" of the discrepancy so that readers can carry out similar checks in their own ...


8

From the website of ESRI: ArcGIS Geostatistical Analyst complements Spatial Analyst. Most of the interpolation methods available in Spatial Analyst are represented in ArcGIS Geostatistical Analyst as well, but in Geostatistical Analyst, there are many more statistical models and tools, and all their parameters can be manipulated to derive optimum ...


5

in theory, ordinary kriging is exact. However, if you interpolate on a grid, the probability that the center of the pixel (where the interpolated value is computed) falls exactly on an observed point is very very small. Therefore, the interpolated pixel value will not likely be the same as the points that are under it. This difference will be more apparent ...


5

Unless i missunderstand you this should work: Intersect Fishnet and contours Calculate a column of area-weighted values (value*shapearea) Dissolve by fishnet ID and sum the area-weighted values Calculate total averages by Dividing with shape areas You can do this manually, with ModelBuilder or using the Python window and code below (change the four lines ...


4

To begin with, I would recommend raster-based analysis. Remote sensing data such as NLCD is notorious for being problematic in simple and multiple linear regression (e.g. data distribution, unrealistic p-values due to large n, etc). Alternatively, there are powerful nonparametric regression methods, such as randomForest that can be used with unruly remote ...


4

See this answer from ESRI stating kriging considered exact and this nice description from expert course material that also goes the same route. Generally, kriging is associated with exactness but according to ESRI: When semivariogram and covariance models have a nugget effect there is potential for a discontinuity in the predicted surface at the sample ...


3

Usually, it's not good practice to remove outliers, unless you know why they are outliers and have a good justification for the removal. For example, if you know these points were measured incorrectly (wrong equipment, wrong method, wrong annotation, etc) while the others were not, then it is ok. This is what the ESRI blog says in its introduction: ...


3

As simply as possible, Spatial Analyst interpolation performs matemathic interpolation based only on the value and distance (so to compute the value of altitude between two points, you can apply a simple method of linear interpolation), while Geostatistical Analyst perform interpolation based on statistical relations between the value and distance (so to ...


3

In Python, pointcoord = x, y creates a tuple while the Neighborhood tool calls for a string of two numbers seperated by one space. "689383.6885 3973775.2178" which worked does not equal the tuple (689383.6885, 3973775.2178) which is unacceptable. What you need it to concat the coordinates and add a space between them, and for that to work you need to cast ...


3

Having done this type of modeling for decades, I can say that, in how your problem is defined, Kriging is a very poor choice and, without understanding nonstationarity, you are probably violating the hell out of some important assumptions. You are likely getting your stated accuracy due to overfit and not actual prediction accuracy. If you were modeling ...


3

GA layers are a lower resolution and on-the-fly redrawn version of the interpolation results, that's why the output raster differs from the GA layer. As a result of this on-the-fly resampling, cell values from the GA layer can be different from the output raster's cells values. I advise you to read Esri's blog post Understanding Geosatistical Analyst Layers ...


3

Interesting finding, and thanks for the perfectly prepared reproducible example! Both variograms you mention are meant to prepare for linear kriging in the next step, and you may not want that. The robust variogram was (IIRC) deviced for normal data with some pollution (outliers), but not for count data. I would advice to look at model-based geostatistics, ...


3

Here is an R example that corresponds to the example in the manual you point to: > library(sp) > demo(meuse, ask=FALSE, echo=FALSE) > coef = lm(log(zinc)~sqrt(dist), meuse)$coef > coef (Intercept) sqrt(dist) 6.994379 -2.549200 > library(gstat) > k = krige(log(zinc)~sqrt(dist), meuse, meuse.grid, vgm(.6, "Sph", 900), beta = coef) [...


3

Blockquote Can my process/decision for interpolation be adequately justified for a paper, or should I be doing something else? Well, +1 for this one. Bear with me, I am not a (geo-)statistician at all, but I am always a bit stumped when I see people trying to interpolate datasets that simply aren't suitable for interpolation, even in the face of ...


3

You have a classic zero inflation problem and this data is just not suitable for interpolation statistics. You may want to try a regression approach using a zero inflated model (ZIP), where the zeros are modeled independently as a binomial process. Commonly, the non-zero model is a Poisson regression but, that is not set in stone and could be any ...


3

I think I know at which point the problem arises. I have to choose "Neighboorhood type - SMOOTH" in Geostatistical Wizard - Searching Neighborhood what is going to use a sigmoidal function defined by the smoothing factor to adjust the weights. After that, I'll not get any breaks by exporting or converting.


3

Esri provides a good explanation on Understanding Cokriging and things to consider if using this approach. Cokriging uses information on several variable types. The main variable of interest is Z1, and both autocorrelation for Z1 and cross-correlations between Z1 and all other variable types are used to make better predictions. It is appealing to use ...


3

Package sf also provides sf::st_make_grid library(sf) library(gstat) data <- as.data.frame(data_to_analise) colnames(data)[1:3] <- c("longitude", "latitude", "z") st.sf <- st_as_sf(x = data, coords = c("longitude", "latitude"), crs=NA) colnames(st.sf)[1] <- "z" vgm1 <- gstat::variogram(z~1, st.sf) fit1 <- gstat::fit.variogram(vgm1, ...


2

I just tried it on a few sample shapefiles and it is working for me in 10.1 SP1, both with the .lyr file in TOC and referencing its location on disk. If either option still doesn't work for you, maybe the symbology is incompatible. Try a really simple symbology and go from there. If you are referencing it in the TOC: mxd = arcpy.mapping.MapDocument("...


2

I think the question has a geospatial element, although I have never done such a study. You could:- a) To work work out effect of turbine placement apply buffers of different distances from the property in question with different buffer distances from the property - simple route would be circular, more difficult but elegant and more realistic would be to ...


2

Kriging seems like a poor choice here. First, what is the role of spatial autocorrelation in predicting whether an area is forested or not? Since, at its core, kriging is a method that predicts based on distance, you should only use it if you have a good reason to believe distance plays a large role in why there is forest at a given location, or if you have ...


2

As far as I know, IDW is implemented as an exact interpolator in ArcGIS Geostatistical Analyst, as reflected in the Prediction Map results. This issue occurs only when you convert to raster, as you point out, and is intended. The raster resolution is finite, and the values of the cells are either taken as a value in their center or as a mean over their area ...


2

You could certainly do this with the Spatial Analyst tools in ArcGIS. I've not specifically worked with groundwater data, but I've used both Surfer and ArcGIS for contour mapping. First, make sure you have and have enabled the Spatial Analyst extension in ArcGIS (Customize menu -> Extensions -> check Spatial Analyst). Then add your well dataset as a layer; I'...


2

There is no single, national State Plane coordinate system; there is a different one for each state. What kind of geostatistics are you calculating? If you are doing area-based calculations you'd be best off using an Equal Area projection. Note that State Plane systems use Conformal projections, not equal area.


2

IDW does make assumptions about spatial autocorrelation. Any spatial smoothing does. The output of an IDW is a smoothed surface in 2d, and if you check that for spatial autocorrelation, it will have it. What IDW doesn't have is a statistical model for spatial autocorrelation - its a mechanical method for producing maps of the mean. Having no spatial ...


2

Thank you very much for your help. I solved problem in such way: library(gstat) library(sp) longitude_for_data = c(32,68,89,145,176, -14, -42) latitude_for_data = c(22, 8,21,13 , 16,- 34,-12) Z_for_data = c(10,20,30,40 , 50, 60, 70) data_together = cbind(longitude_for_data,latitude_for_data,Z_for_data) data_to_analise = as.data.frame(...


1

This is just how it works. Your extraction by mask is just fine, you merely need to change the symbology in the extracted raster. The mask is not doing what you (or I) think it should in the environments setting. It is more of an envelope when used here as opposed to a true "cookie-cutter" when you are using extract by mask. FYI this never used to happen in ...


1

Cokriging is a form of spatial interpolation and requires that you have a coordinate for every data point you want to include. A really good paper is "Cokriging model for estimation of water table elevation" (Hoeksema et al., 1989) - just the introduction can help to get a feel for what the algorithm is doing. Cokriging also requires that at least one data ...


1

The USGS phenology project might inspire you a little. http://phenology.cr.usgs.gov/other_resources.php


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