Latest Contributions by EricKrause

‎04-01-2024

Hi @JamalNUMAN, Requiring a Unique ID field is an older design pattern that is not used in more recent tools. In fact, the Generalized Linear Regression tool (with Gaussian model type) does the same thing as the OLS tool, and it does not require a Unique ID field. The idea behind the Unique ID field is that it gets copied to the output features, so you can join the output results back to the input (or vice versa). For example, if you have a selection, the output features will not have the same Object IDs as the input, so some other field needs to be used to match input/output. In more recent tools, each Object ID from the input is copied to a "Source ID" field of the output features. This serves the same purpose (being able to match output to input) but does not require that you provide a field.

‎02-14-2024

Hi @DOEEYANG, Can you clarify how you are performing kriging? I'm guessing the Kriging tool in the Spatial Analyst toolbox, but there are a few different versions. Without looking at your data, my guess is that these areas with no predictions are outside the neighborhood of your input points. Assuming you're using the tool above, check the "Search radius" parameter. If you are using a "Variable" neighborhood, check whether there is a "Maximum distance" value. If using a "Fixed" neighborhood, check the "Distance" value. If your cells with no predictions are further than this distance from any input point, the value cannot be interpolated. Using a sufficiently large distance should allow you to interpolate everywhere in your study area. Please let me know if this does not resolve the problem or if you're using any of the kriging methods in Geostatistical Analyst.

‎02-14-2024

GWR will not use the z-coordinate in any capacity. So if you have multiple points at the same (x, y) but different z, GWR will treat them as being at the same location. Splitting your dataset by floor and independently performing GWR is the only solution that immediately comes to mind. The problem of constant values of the explanatory/dependent variable is more difficult, as GWR will return an error if any neighborhood contains a constant value of any explanatory variable or the dependent variable. To calculate GWR results, you'll need to use neighborhoods large enough to ensure this never happens. However, if the neighborhoods are very large, GWR effectively turns into OLS. Hopefully there is some range of neighborhood that can estimate local effects but still never encounter neighborhoods with constant values.

‎02-14-2024

Hi @JamalNUMAN, I don't think it does any data splitting for the statistics in your images. Data splitting is not required in order to compute them, and in my experience, OLS, GWR, and other variants of the general linear model do not perform data exclusion to calculate them. In recent years, I've seen GWR used with data splitting (to make it more in line with machine learning workflows), but I do not think the GWR tool does this. Also, I'd suggest that you ask your GWR questions (and any other questions about the Spatial Statistics toolbox) in the Spatial Statistics Place. I know a lot about GWR as a theory, but I'm less knowledgeable about the specifics of the implementation of the GWR tool. For example, I do not know why those three statistics are calculated, but others (like MAPE) are not.

‎02-11-2024

Moved: https://community.esri.com/t5/arcgis-pro-questions/arcgis-pro-3-0-2-the-train-using-automl-tool-with/m-p/1380869#U1380869

‎02-08-2024

Hi @JamalNUMAN, While the error only talks about correlations between explanatory variables (which obviously will not be a problem for a single explanatory variable), a couple other things can also cause this error. GWR builds regression models using neighborhoods around each feature, and if any of these neighborhoods have a constant value for the dependent variable or any of the explanatory variables, you will also encounter this error. You should trying using different neighborhood settings (generally using larger neighborhoods), or attempt to locate the areas of constant value. The Neighborhood Summary Statistics tool can be used to find local standard deviations, which can help you identify areas with constant values of the variables. I hope this helps, and please let me know if you have any other questions.

‎02-01-2024

Hi @JillClogston, The message from the tool is an informational warning rather than an error. It does not mean that your analysis is invalid or that there is a problem with your data. CF Conventions are a set of standards for how to store and label data in a netCDF (NC) file. NC files are generic data containers and do not have to abide by these standards; however, some non-Esri software will only work correctly with CF-compliant netCDF files. If you intend to perform your analysis entirely within ArcGIS, this is not a problem, and you can ignore the warning. While I do not know which projection you are using, the warning indicates that it is not one that is part of the CF Conventions. You can likely resolve the warning (which, again, may not be required at all) by projecting your original points to a more common coordinate system.

‎12-01-2023

I should have been more clear about this, but the GWR model as a whole does not have a condition number. However, every local regression has one. It could be the case that some locations have large condition numbers (meaning that the coefficients in that area are unstable and unreliable) but have low condition numbers in another area, meaning that the coefficients are more reliable and precise. I'm also not completely clear what you mean by rerunning GWR multiple times. If you rerun it with the same data, you should get the same coefficients each time. The condition number is more related to whether you should trust the values of the coefficients.

‎11-30-2023

I've heard variations of that phrasing various times, and I don't think it's wrong, but I'd argue there are better ways to conceptualize the condition number. It's more about the stability of the estimated coefficients for a given set of explanatory variable values. The coefficient are estimated by inverting a matrix of data values, and the condition number measures how sensitive the coefficients are to small changes in the data values. For low condition numbers, you can alter/remove some of the data, and the coefficients should not drastically change (in other words, the estimated coefficients are stable). But for matrices with very large condition numbers, even small changes to the data values can wildly change the estimated coefficients (meaning that the estimated coefficients are not stable/reliable). This is a bit easier to understand using simple numbers rather than matrices. Inverting a matrix with a large condition number is equivalent to finding the inverse of a number that is very close to 0. For example, the inverse of 0.001 is 1,000, and the inverse of 0.0001 is 10,000. Even though 0.001 and 0.0001 are very close in absolute value (they're both close to 0), their inverses are very different in absolute value (1000 vs 10000). To put it another way, for values very close to 0, the inverse is very sensitive to small changes of the number. This stability of the inverse is what condition numbers measure for matrices rather than single numbers. I hope that helps, and let me know if any of that was not clear. There are also many resources available to learn about condition numbers, as they are usually taught in Linear Algebra courses rather than geography or statistics.

‎10-25-2023

Part of the confusion is that in principle, GWR doesn't require the weights to be assigned in any particular way. So textbooks usually just give generic formulas that can apply to any weighting scheme you want. Though as the name Geographically Weighted Regression suggests, the weight is almost always some function of geographic distance between the prediction location and the neighboring features (where closer neighbors get higher weights and, thus, more influence on the model). Kernel functions are the most common way to assign these weights, where the weight decreases with distance according to one of many possible kernels: https://en.wikipedia.org/wiki/Kernel_(statistics) In ArcGIS Pro, the "Local Weighting Scheme" parameter lets you choose between Bisquare and Gaussian kernel functions. In the very last image you posted, the blue cone around the prediction location is visualization of the kernel. Imagine the height of that cone being the weight assigned to a neighbor. Features close to the middle get the highest weight, and it decreases to zero after a certain radius around the prediction location.

‎09-18-2023

It might be possible to use geostatistics, but I suspect it would be better to use a classification workflow. Please look into the "Forest-based Classification and Regression" tool.

‎05-03-2023

@jyothisril Many of the datasets do have undefined coordinate systems, and I unfortunately do not know the original spatial references. Many are likely custom coordinate systems of small study areas.

‎04-03-2023

@giancarlociotoli This is a very good question that the Geostatistical Analyst team spent quite a lot of time thinking about and debating. We came to the conclusion that this tool should only be used for predictive purposes, and it was not suitable for explanatory purposes. This is why explanatory variable coefficients and PCA loadings are not provided by the tool. Without going into too much detail, problems arise with EBKRP's subset mixing methodology. Different subsets will perform PCA independently, and their loadings are often wildly different, even for the same explanatory variables. Within a single subset, this is not a problem, but there is no clear way to meaningfully aggregate different loadings in areas of transition between different subsets. EBKRP mixes only the final predictive distributions across subsets produces, and this produces stable predictions. However, that does not imply that mixing individual components of the models produces stable estimates of a mixed component. In our experimentation, we found that attempting to mix components produced unstable coefficients and uninterpretable loadings, while still leaving stable predictions. Because of this, we only recommend it for predictive purposes, not explanatory purposes (this is also why the word "Prediction" is explicitly in the name of the tool). - Eric Krause

‎03-13-2023

@RyanSnead Sorry for being very late with this reply, but this can be accomplished with the Neighborhood Summary Statistics tool in the Spatial Statistics toolbox.

‎01-31-2023

@brghtwk The main idea behind the tool is to investigate the impact of slightly changing the semivariogram parameters of an existing model (often called a "sensitivity analysis"). Since you must choose specific values for semivariogram parameters, it is reassuring if you get nearly the same results by using slightly different semivariogram parameters. If the predictions change a lot for small changes of the semivariogram parameters, then your results may only reflect the arbitrary parameter choices, rather than an accurate representation of the underlying process. The tool tests this by adding random noise to the semivariogram parameters (range, nugget, sill, etc) and recomputing predictions. You must provide the initial model (a geostatistical layer) using the Geostatistical Wizard. You'll define the semivariogram model (Spherical, Exponential, etc) along with initial parameter values. The Semivariogram Sensitivity tool then adds the noise to the parameters.

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Latest Contributions by EricKrause

Re: ArcGIS Pro 3.0.2: What is the function of the “unique ID field” while working with OLS?

Re: Non-interpolated area by Kriging Interpolation

Re: ArcGIS Pro 3.0.2: The GWR tool encounters a "coincident features" error due to exceeding the "minimum number of neighbors (30).&quo

Re: ArcGIS Pro 3.0.2: Which data splitting method is used in the GWR when calculating the statistical metrics for performance evaluation?

ArcGIS Pro 3.0.2: The “train using autoML” tool with XGBoost algorithm ends up with “shapeType” error,

Re: ArcGIS Pro 3.0.2: The GRW generates “multicollinearity” error despite the fact that only one explanatory variable is used,

Re: Warning 110067: Your spatial reference not compatible with CF Conventions

Re: How to interpret condition numbers from GWR/MGWR analysis?

Re: How to interpret condition numbers from GWR/MGWR analysis?

Re: ArcGIS Pro 3.0.2: How the weight is calculated in the Geographically Weighted Regression (GWR)?

Re: EBK or indicator Kriging computed and modeled the spatial prediction to predict a positive and negative (0,1) parameter using 4 explanatory raste

Re: "Spatial Statistical Data Analysis for GIS Users" available free for download

Re: Ideas in ArcGIS Pro 3.0

Re: Calculate Average of Neighbor Values?

Re: Semivariogram sensitivity tool

Re: Access geostatistical layer created using ArcP...

Re: Why exporting EBK 3D to voxel layer change org...

Re: K-Bessel Semivariogram Equation

Re: Kriging Model Types

Re: Geographically Weighted Regression difference ...

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