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Hi Tahir, Please read this topic, as it explains the differences between the classification of geostatistical layers and rasters: kriging results to raster Quick summary: The classification of geostatistical layers is based on the values of the field that was used in the interpolation. After converting to raster, the classifications are based on the histogram of the raster. Let me know if anything still is not clear.
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09-25-2014
03:15 PM
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Here are the steps to make a map package, in case you have never done it: http://resources.arcgis.com/en/help/main/10.1/index.html#//006600000403000000 Make sure to remove any basemaps or unrelated data before creating the mpk. Thanks.
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07-08-2014
03:52 PM
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Can you please make a map package (mpk) and send it to [email protected]? Make sure to have your geostatistical layer, the training points, and the test points in the package. Thanks.
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07-08-2014
02:49 PM
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** EDIT: This post contains some incorrect information. See later posts for correction ** The values of the Prediction Standard Error map and the Standard Error field from GA Layer to Points should match, as they are indeed calculating the same thing. I did a quick test, and they are matching for me. I suspect that the problem is in how you are querying the value of the standard error from the geostatistical layer. Converting to raster and extracting the value will introduce some error (because it is using the cell center, not the exact location of the point). The Identify tool can also be misleading if you are zoomed out too far (because you have to click exactly on the center of the point). I was able to confirm that the values match by zooming in as far as ArcMap would let me, then I used Identify to query the value of the geostatistical layer and of the output from GA Layer to Points. Try this with your data and let me know what happens.
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07-08-2014
01:21 PM
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Please read this topic for an explanation of what is happening: http://forums.arcgis.com/threads/77248-kriging-results-to-raster If you use a smooth searching neighborhood when creating the geostatistical layer, the exported raster usually looks much more like the geostatistical layer.
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06-20-2014
06:23 AM
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It depends on whether you are creating rasters or geostatistical layers. If you're creating rasters, you can use the Mask environmental setting when running the geoprocessing tool (starting at ArcGIS 10.1, all Geostatistical Analyst tools that output a raster will honor Mask). You can also use the Clip tool if you already have the rasters or do not have ArcGIS 10.1 or later. If you're creating geostatistical layers, they cannot be clipped directly. But you can use something called Dataframe Clipping to make the layer transparent over land (which, visually, will look just like a clip). To see an example of this with geostatistical layers, see steps 18-21 at the bottom of the first Geostatistical Analyst tutorial.
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05-12-2014
07:25 AM
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That's strange... Are you sure the toolbar didn't appear anywhere? Sometimes when you add the toolbar, it becomes free-floating (not pinned to the toolbar area), and it can appear almost anywhere on the screen (you can then drag it into the toolbar area to pin it there). I've even seen it on a completely different monitor when I use a dual monitor setup. Try disabling the toolbar and then re-enabling it.
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04-03-2014
11:20 AM
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I'm not a Python expert, but I think the problem is in this line: Output_geostatistical_layer = Input_features You're giving the output geostatistical layer the same name as the input features. Try giving it a unique name or expose it as a parameter in your script tool.
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03-06-2014
08:50 AM
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Thanks for sending the data. Luckily, it didn't take long to find the problem. The issue is the point with a value of 171 (FID = 44). This point is 40 meters north-east of a point with a value of 7 (FID = 75). This rapid change in the data values is a problem for Kernel Interpolation with Barriers because the tool works by chasing linear trends in the data values. In this area, it detected a rapid upwards trend because the data values changed rapidly from 7 to 171 in a distance of only 40 meters. Since there is no additional data north-east of the point with a value of 171 to calibrate the trend, the model assumed that this upwards trend would continue. That is why you are getting high predictions in the area to the north-east of these two points. If the value of 171 is a typo, it should be corrected. Otherwise, you should consider removing it from the dataset because it is clearly causing stability problems.
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03-06-2014
07:47 AM
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It's hard to tell what is happening from that picture. Is it possible to send your data to [email protected] so that I can take a closer look?
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03-06-2014
05:41 AM
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Trend removal is done by fitting a polynomial equation of the x- and y-coordinates. So, a first-order trend removal will remove linear trends. Similarly, a second-order polynomial will remove quadratic trends, and higher-order trends will use higher-order polynomials. The best mechanism for deciding which order is to use the Trend Analysis ESDA tool. Look at the graphic in that topic. When you select "Trend on Projection" you can change the order of polynomial. Try different orders to see what fits your data best, and use that same order as order for trend removal.
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02-20-2014
10:40 AM
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Sorry for the confusion. That text is referring to the output raster or output features, not the inputs.
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02-14-2014
10:28 AM
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Extent does not affect which data points are used in the model. Even points outside the Extent will be used in the calculations. Extent refers to the extent of the output raster or geostatistical layer. If you want to control which points are used to build the model, you can do that with Selection. Mask simply sets some areas of the output to NoData. Specifically, anything outside the Mask and within the Extent will be set to NoData. Note that Mask is only supported for raster output; this environment does not work with geostatistical layer outputs.
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02-14-2014
09:41 AM
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Please read through the documentation on Mask and Extent. They do a good job of explaining the concepts. If you still have questions after reading the topics, I'm happy to answer them. Mask: http://resources.arcgis.com/en/help/main/10.2/index.html#//001w0000001t000000 Extent: http://resources.arcgis.com/en/help/main/10.2/index.html#//001w00000009000000
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02-13-2014
12:21 PM
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Hi Steve thanks for replying. So the larger the bandwidth, the more calculation will be done right? Since it does not have a guidance value, how do I decide what order of magnitude to start with as the bandwidth? my p.s. to move this thread to other forum, is there a way other than posting it again? The default bandwidth is calculated such that it minimizes the root-mean-square crossvalidation error. However, any location that does not have any points within the bandwidth distance will be calculated as NoData. As you increase the bandwidth, the areas of NoData will get smaller, but the quality of the predictions will decline. Keeping these two issues in mind, you need to decide on a bandwidth that works for your needs.
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02-13-2014
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