Latest Contributions by JuanLaguna

The Spatial Analyst extension introduces new and enhanced capabilities in ArcGIS Pro 3.5. Highlights in this release include a new tool for surface analysis and some significant performance gains. Along with those comes several other improvements in capability. Read on to learn more Where do I get it? We released ArcGIS Pro 3.5 on May 13, 2025. Download and install it. For an overview of the changes made for this release, have a look at the Your ArcGIS Pro Update (May 2025) blog post. What’s changed for Spatial Analyst? Here are the primary areas of improvements over the last release: Distance analysis Generalization analysis Overlay analysis Segmentation and Classification analysis Surface analysis Zonal analysis Cell Size analysis environment Suitability Modeler Data conversion Read on to learn more about the specific changes in various areas. 1. Distance analysis For the Distance Accumulation and Distance Allocation tools, performance in some scenarios that use the Vertical Factor or Horizontal Factor parameters improved in ArcGIS Pro 3.5. In previous releases, while many scenarios performed well, certain input data characteristics could result in slow processing times. The changes we implemented in this release to address the issue resulted in those scenarios running up to 50 times faster. The same updates made for the geoprocessing tools also apply to the corresponding raster functions. 2. Generalization analysis The generalization tools Expand and Shrink have a small improvement that minimizes instances of cut-off cells appearing in the results for some narrow, wedge-shaped zones. 3. Overlay analysis The Locate Regions tool now now runs on average twice as fast as before, and in some cases up to seven times faster. A primary application of this tool is to find the best locations from a suitability map that meets a subject's specific spatial requirements. However, this tool is CPU intensive, particularly as the number of seed locations and the resolution of growth parameters increase. For one input raster, the time taken to produce a result went from 28 minutes down to 4 minutes. 4. Segmentation and Classification analysis A new parameter is available for the Create Accuracy Assessment Points tool, which you can use to create randomly sampled points for post classification accuracy assessment. The Minimum Point Distance parameter allows you to set a minimum distance between the reference points.   5. Surface analysis   Multiscale surface analysis A new tool is available for the collection of tools that identify specific landscape characteristics of a surface raster at multiple scales. The Multiscale Surface Deviation tool calculates for each input cell the maximum surface deviation from the mean value across a range of spatial scales. The outputs from the tool record what this value is, and the other the scale at which it occurs. This new tool can also take advantage of GPU hardware, and could produce an output up to twice as fast compared to only using the CPU for calculations. The following graphic illustrates the results from this new tool. The panel on the left is the input elevation surface raster. The middle panel is the output deviation raster. The ridges (darker blue shades) show larger positive deviations from the mean, while the valleys (darker brown shades) show larger negative deviation values. The panel on the right is the scale output. The white areas show locations of larger deviation values found at the larger scales. The darker areas show where larger deviation values are found at smaller scales. You can use these deviations and scale raster to validate hydrologic features from your surface data. Learn more in the Analyze terrain with the new Multiscale Surface tools in ArcGIS blog post by Sydney Walker. Geomorphons The Geomorphon Landforms surface tool is a very effective way to analyze terrain for applications from hydrologic studies to assessing landslide susceptibility. A new How Geomorphon Landforms works help topic provides more information to help you better understand how it calculates the output values and how to correlate those to common landform types. 6. Zonal analysis For the Zonal Statistics and Zonal Statistics as Table tools, the default cell size behavior was updated. These tools have an input that defines the shape of the zones, and an input that defines the values that will be measured within those zones. By default, these tools now apply the cell size of the value raster to a raster zone input. This follows a change made for feature zone inputs in an earlier release. Since the value raster is the primary raster to drive the output results, this update will make the analysis more accurate. To modify the default behavior, use the Cell Size environment to specify a number, a raster dataset, or the Maximum of Inputs or Minimum of Inputs options. 7. Cell Size analysis environment When doing raster analysis, it is important to pay attention to how the environment affects the analysis. In ArcGIS Pro 3.5, tools that honor the Cell Size environment support an empty option in both tool dialog boxes and the Python environment. This environment sets the output raster resolution in which the analysis will be performed. It can be set to match another raster dataset, a numeric value, or to apply the maximum (coarsest) or minimum (finest) resolution of the inputs. The new default behavior enables tools that use Maximum of Inputs or another special setting as the default to continue to do so. 8. Suitability Modeler While there is no new functionality specific to the Suitability Modeler, a new capability is available in ArcGIS Pro that you can apply to suitability analysis. Use the new Assign Weights by Pairwise Comparison tool in the Analysis toolbox to calculate weights for a series of input variables more objectively. You can then enter them as the weight criteria when preparing the suitability map.   9. Data conversion While not a Spatial Analyst tool specifically, the Raster to Polygon tool is commonly used in workflows that produce an output as a polygon feature layer. This tool has enhanced performance when vectorizing large rasters. Depending on the data and the settings used, the conversion typically completes in less than half the time as before. In some cases, it can be dramatically faster. For example, the time the tool took to convert one raster went from 2 hours down to about 6 minutes. An upcoming blog post will cover this improvement in more detail.   Summary We hope that you try out the new and improved things you can do with the Spatial Analyst toolbox for ArcGIS Pro 3.5. If you have more questions, join in on the discussion on the Community page for Spatial Analyst here: https://community.esri.com/t5/arcgis-spatial-analyst-questions/bd-p/arcgis-spatial-analyst-questions   To learn more about Spatial Analyst, here are some starting points: Learn more about the Spatial Analyst Extension product What is the Spatial Analyst Extension The Spatial Analyst toolbox in ArcGIS Pro   The original blog was first published in the ArcGIS Blog, and can be found here: https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/whats-new-for-spatial-analyst-in-arcgis-pro-3-5 ... View more

The Spatial Analyst extension introduces new and enhanced capabilities in ArcGIS Pro 3.4. This release gives you new tools for surface and zonal analysis as well as improved capabilities for density and distance analysis. Several tools for hydrology analysis have improved performance. Some new help content is available for solar analysis. Read on to learn more.   What’s changed for Spatial Analyst? Listed here are the main functional areas with improvements over the last release: Density analysis Distance analysis Hydrology analysis Solar analysis Surface analysis Zonal analysis   1. Density analysis The Space Time Kernel Density tool can now create an output voxel layer. This output represents the density of the input points as magnitude per-unit-area across multidimensional space and time. What does that mean? You can now interactively explore the results as a 3D volumetric visualization! Have a look at the following screenshot for an example of what you can do with this new capability.   The output must be a netCDF raster, which you create by specifying ".nc" as the filename extension for the Output Raster parameter. Some new Usages help guide you through the process of creating the voxel layer and adding it to a scene. We also improved some of the parameter descriptions for the tool, as well as the How Space Time Kernel Density works help topic.   2. Distance analysis   Around the world distance Do you perform distance analysis at a global extent? Several tools can now calculate least cost paths around the entire world! The Distance Accumulation, Distance Allocation, Optimal Path as Line, and Optimal Path as Raster tools no longer treat the edge of the map as a barrier. This improvement is available when the Distance Method parameter is set to Geodesic, and the data is in either a geographic coordinate system or a cylindrical projection.   Zero or negative values for Cost Distance A usability improvement for distance tools that can use a Cost raster as an optional input. Those tools are: Distance Accumulation, Distance Allocation, Optimal Corridor Connections, and Optimal Region Connections.  Since the cumulative cost algorithm is a multiplicative process, calculating accumulative cost correctly is problematic if any cost values are zero or negative. In earlier releases, the tools processed cells with these values as NoData. Now, the tools will treat these values as very small positive values. This allows the use of Cost rasters that contain cells of zero or negative values directly in workflows, without having to remove them in a pre-processing step.   Raster Functions: The same update to handle the edge of the projection at a global extent as the corresponding geoprocessing tools was applied to the raster functions: Distance Accumulation,  Distance Allocation, and Optimal Path As Raster. For the Distance Accumulation and Distance Allocation raster functions, the Vertical Factor parameter now includes the Hiking Time and Bidirectional Hiking Time options that were added to the geoprocessing tools in ArcGIS Pro 3.3.   3. Hydrology analysis For hydrology analysis, we made notable performance improvements to the Basin, Flow Length, Snap Pour Point, and Stream Order tools. A future blog post will show cover in more detail the types of gains achieved by these tools.   4. Solar analysis Do you use the Raster Solar Radiation and Feature Solar Radiation tools in Spatial Analyst? We added several new help topics to better explain how to use these tools. The Analyze solar radiation topic provides some general information on how solar radiation is calculated for a surface on the Earth or the Moon. To learn more about using these tools and how the calculations are performed, read the How Feature Solar Radiation works and How Raster Solar Radiation works topics.   5. Surface analysis Three new geoprocessing tools are available for performing surface analysis. One application for these tools is to extract and evaluate information about elevation-derived hydrography directly in ArcGIS Pro, for a more integrated and seamless workflow than was previously possible.   Feature Preserving Smoothing The Feature Preserving Smoothing tool smooths out a surface raster by removing small scale surface variation (noise), while preserving the meaningful landscape features. It has several parameters that gives control over the amount and type of smoothing you want to apply. Multiscale Surface Difference and Multiscale Surface Percentile Two new tools identify specific landscape characteristics of a surface raster at multiple scales. In this context, different scales represent different neighborhood distances from each input cell. The Multiscale Surface Difference tool identifies the maximum difference from the mean for each input cell across multiple scales. The Multiscale Surface Percentile tool identifies for each input cell the most extreme percentile value. For both tools, an optional output raster identifies the specific scale where that value occurred. You can also control how the increase in neighborhood distance behaves with parameters for the minimum and maximum values, as well as the increment between the scales. The following illustration shows the result of applying two different scale settings to a surface raster with the Multiscale Surface Percentile tool.   6. Zonal analysis The new Zonal Characterization tool summarizes the values of multiple input rasters for a zone input. Its primary output is a table where each record represents an input zone, and each field the is the value of the specified statistic for all the cells that fall within that zone. While this tool supports the same types of statistics as the Zonal Statistics as Table tool, it offers several advantages. In one run of the tool, it can calculate several different statistics for the same value raster, the same statistic for multiple value rasters, or any combination thereof. You can also choose to create a new output feature class that joins the output table to the input zone data. For example, say you have a workflow for water level conservation where you want to know the average slope, the total accumulation of snow, and the maximum snow depth for a soil type map. The images below show the inputs for the tool followed by the resulting output table.     The output table:   Summary That covers the new things you can do, and the improvements made to the Spatial Analyst toolbox for ArcGIS Pro 3.4. Download the update and try them out!   To keep up to date with new posts as they become available, bookmark this link: https://community.esri.com/t5/arcgis-spatial-analyst-blog/bg-p/arcgis-spatial-analyst-blog   For additional questions and discussion related to Spatial Analyst, see here: https://community.esri.com/t5/arcgis-spatial-analyst-questions/bd-p/arcgis-spatial-analyst-questions   Here are some starting points if you want to learn more about Spatial Analyst: Learn more about the Spatial Analyst Extension product What is the Spatial Analyst Extension The Spatial Analyst toolbox in ArcGIS Pro     The original blog was first published in the ArcGIS Blog, and can be found here: https://www.esri.com/arcgis-blog/products/spatial-analyst/analytics/whats-new-for-spatial-analyst-in-arcgis-pro-3-4/ ... View more

Rasters can have holes (also called voids, gaps, or NoData) in them. These areas can be large and very visible, or they can be individual or small groups of cells scattered throughout and not easily seen. Suppose you want to eliminate the holes? How can you replace them with meaningful values, while preserving the existing values? There is a variety of ways to do that. Here we will give some background, a few things to consider, and then show four common solutions. What is NoData?   A raster is a data structure that records information about a phenomenon, such as category, magnitude, height, or image reflectance, organized into a regular matrix of equally sized cells arranged into rows and columns. However, sometimes there is not enough information available to give a value to a cell, either from the data source or the output from an analytical operation. The concept of NoData is used to represent these cells. When displaying rasters, the renderers allow you to set the NoData cells to appear either with a color of your choosing, or to not display at all (transparent). For analysis, how input NoData cells are handled can vary based on the tool being used. For some tools, those locations will remain NoData in the output. Other tools calculate an output value based on other available values. The reference documentation commonly addresses the NoData behavior for a particular tool. However, the behavior may not be suitable for your analysis. You may want to identify these NoData cells so you can replace them with appropriate cell values. How can you do that?   Identify NoData cells   There is a tool that explicitly identifies NoData cells, the Is Null tool. It checks each cell and returns a value of 1 if a cell is NoData, and a value of 0 (zero) if a cell is any other value. If the resulting raster has values of both 0 and 1, then the input raster has NoData cells present. In the attribute table of the raster, the Count field tells you how many cells there are of each value. In the following example, the input raster is on the left with the NoData cells rendered in white. This raster has 9 rows of 10 columns, and thus has a total of 90 cells. There are four unique values (3, 9, 15, and 22). Summing up the cell counts of each value (11, 22, 25, and 10, respectively), gives a total of 68 cells. Taking 90 and subtracting 68 from it tells you there are 22 NoData cells in the input. The output from Is Null and its attribute table is on the right. The cell value of 1 represents NoData, and it matches the expected count of 22 cells. The Is Null tool identifies NoData cells in an input raster.     Factors to consider in the analysis   Before starting any analysis, first look closely at the problem you are trying to solve. To make sure you follow the right analytical path to get the appropriate answer, carefully consider the types of analysis you will perform. When it comes to picking the right solution for filling NoData areas, some factors to consider are: Where are the replacement values coming from? Is the raster discrete or continuous? Examples of discrete raster data include those recording land use classes or ranks. Examples of continuous rasters include elevation and concentration. What is the nature of the NoData area to fill? Is it only a few cells scattered throughout, or large blobs that are many cells across? Some solutions will work better on small areas a few cells in size, whereas others can handle larger areas.   What do you want to replace NoData cells with?   To choose the proper solution, first determine where the replacement values are coming from. Common sources of replacement values include: A specific numerical value Cell values from another raster The nearest cell A statistic of the surrounding values, such as the average or the largest Once you decide the source of the replacement values, then you just need to follow a specific workflow to achieve the result. There are of course many types of raster analysis that can be done, and different ways to go about doing it. This article does not cover all the scenarios, but will focus on some of the typical ones.   Workflows for replacing NoData   The following graphic illustrates these four workflows at a general level. The column on the left lists the scenarios according to where the replacement values will come from. The center column shows the basic workflow to use for each scenario. The rightmost column provides some information on the general applicability of the scenarios, considering the size of the NoData area and the type of data.   An outline of some common workflows for replacing NoData in a raster.   A: Replace NoData cells with a specific value   The easiest way to fill NoData is to replace those cells with a specific value. By using the same value for all instances, you can apply it to all the NoData cells without having to consider their size or distribution. This method is most suited for discrete data. As shown earlier, you can use the Is Null tool to create a raster that uses a value of 1 to indicate where a cell in an input raster is NoData, and a value of 0 for locations of any other value in the input raster. The Con tool evaluates each cell of an input raster based on a logical condition. You could take the output from the Is Null tool and use it in the Con tool as the input that identifies which of the input cells are NoData and thus will be replaced with the value specified in the true parameter. However, the Expression in the Con tool also has the capability to do an is null operation, as shown here:   An example of using the is null option on the Con tool dialog.   In the Con tool, do the following: Set the raster with NoData as the Input conditional raster. In the Expression, set the Where clause to Value and select the is null option from the list. This will use the Is Null tool internally to identify which input cells are NoData and which are not. Set the Input true raster or constant value to the replacement value you want to replace NoData cells with. Set the Input false raster or constant value to the original input raster, to preserve those values in the output. Set the Output raster location and name. Run the tool. The following illustration shows the NoData locations replaced with the new value of 2, while the other locations retained their original value.   An example of replacing NoData cells with a constant value of 2     B: Replace NoData cells with values from a different raster   Instead of using a constant value, another raster can provide the values to replace NoData cells with. If it makes logical sense to replace them all with cell values from another raster, there is no need to consider the size and distribution NoData areas. You can apply this method to both discrete and continuous data, but it is best to match the type of the replacement raster to the type of the raster you are updating. In the Con tool, do the following: Set the raster with NoData as the Input conditional raster. In the Expression, set the Where clause to Value and select the is null option from the list. Set the Input true raster or constant value to the raster that the replacement values will come from. Set the Input false raster or constant value to the original input raster, to preserve those values in the output. Set the Output raster location and name. Run the tool.   Note: If the raster providing the replacement values has different properties than the raster containing the NoData cells, such as extent, cell size, cell alignment, or coordinate system, remember to account for these differences. By default, the Con tool will use the union of the extents of the two input rasters, and the maximum cell size. To preserve the existing cell values that are not NoData and avoid them being resampled, be sure to set the Extent, Cell Size, and Snap raster environments to your original input raster.   The following illustration shows how values from the other raster replaced the NoData locations, while the other locations retained their original value.   An example of replacing NoData cells with values from another raster.   C: Replace NoData with the value of the nearest spatial neighbor   You may want to replace NoData cells with the value of the nearest (closest) cell. There is a tool that can do this: Nibble. It will replace the input values for a defined area with the nearest value that is outside that area. While the method can fill in large areas, it may be less logical for the replacement values to come from further away. Since the replacement values come from the same set of values as the input, this method is most suited to discrete data. The Nibble tool has two required inputs. The first input is the raster for which the values at selected locations will be replaced with the nearest value. The second input is a mask that identifies what those locations are. For this input, NoData cells represent locations that are within the mask, and cells with any other value are outside the mask. There are two additional parameters that give specific control over how NoData cells are handled. By setting them a particular way, NoData cells in the input raster can also define the mask area. This means that you can use the same raster for both required inputs. In the Nibble tool, do the following: Set the raster containing NoData as the Input raster. Set the same raster as the Input raster mask. Set the Output raster to the location and name. Uncheck the Use NoData values if they are the nearest neighbor parameter. The objective here is to only consider cells with valid values to replace NoData cells.  Check the Nibble NoData cells parameter. This will make the tool replace the NoData cells inside the masked area with the value of the nearest neighbor outside the masked area, instead of remaining as NoData. Leave the Input zone raster parameter blank. Run the tool. The following illustration shows how the value of the nearest input cell replaces the NoData locations. To make comparison easier, a dark red outline on the output raster identifies the NoData locations of the input raster. In the case of ties, where there are two or more input cells that are nearest to a NoData cell, the output will be the lowest of the tied values. In the part of the figure below the dashed line, the numbers show the distance (in cell units) from each NoData cell to the nearest cell outside the mask. For a portion of the NoData cells, small arrows identify the specific input cell that provides the replacement value.   An example of replacing NoData cells with values from the nearest neighbor.   D. Replace NoData with a statistic calculated from the surrounding cells   A statistic calculated from the surrounding cells can replace a NoData cell. This can be done by incorporating the neighborhood tool Focal Statistics into the analysis. For each input cell location, this tool calculates a statistic of the values within a specified neighborhood around it. You can specify a variety of neighborhood shapes, such as a rectangle, a circle, or a pie-shaped wedge, in whatever size you need. There are a variety of statistics to calculate, such as the average or minimum value.   The size of the areas of NoData is a consideration for this tool. For individual or small groups of NoData cells, the small default 3 by 3 cells neighborhood size can calculate the replacement value. To fill larger areas of NoData, either expand the size of the neighborhood, or run the process several times. For discrete data, the statistics that are most appropriate to use with this method are the maximum, minimum, most common, and least common. For continuous data, the mean statistic is typically the best one to use.   If run by itself, the Focal Statistics tool will calculate a statistic value for every cell in the input raster. To perform the calculations only on the NoData cells, we will apply the technique of using the Is Null tool within the Con tool to identify those locations. Then we will use the Focal Statistics tool to calculate a new value for those locations only.   To embed this tool in Con, it is necessary to create a complex expression in map algebra. In ArcGIS Pro, this can be done in the Raster Calculator tool or in the Python Window.   Apply the following workflow to run the Focal Statistics tool in the Python window: Open the Python window and import the necessary modules. Set the workspace to where your data is located. Begin to enter the map algebra expression to construct the statement for the Con tool. For the Input conditional raster parameter, enter "IsNull()" and specify the name of your input raster. For the Input true or constant value parameter, specify the necessary syntax for the Focal Statistics tool to calculate the output for the neighborhood and statistic of choice. Enter the name FocalStatistics, without a space. Set the Input raster to the raster you are processing. Set the Neighborhood parameter the shape and size of the neighborhood around the NoData cells you want to calculate the statistic for. Set the Statistics type parameter to the one you want to calculate. For the Input false or constant value, specify the original input raster again. Run the expression. As needed, set up and run the expression again to fill in large NoData areas. Since the result of the map algebra expression is a temporary raster object, use the Raster save method to persist the final output raster.   The following graphic shows an example of the syntax used to create a rectangular 3 by 3 cell focal neighborhood:   An example of a map algebra expression in Python that incorporates the Focal Statistics tool.   The following illustration shows how values from input raster replaced the NoData locations with the maximum value in a 3 by 3 cell neighborhood around them. In this case, the size of the NoData area is larger than the size of the focal processing window. This would cause some of the NoData locations to remain as NoData in the output from the focal operation, since there were no input values to do a calculation for. To resolve this, you can either run the operation again to replace the remaining NoData value, or use a larger neighborhood. This example ran the focal operation two times, with the output from the first pass used as input to the second.   An example of replacing NoData cells with the maximum value of the nearest cells. For a 3 by 3 cell focal neighborhood, the size of the NoData area required two passes of the tool.   The following is the syntax used in the Python command window to create the final output raster for this example: import arcpy from arcpy import env env.workspace = "C:\project1" OutFS1 = Con(IsNull('inRas.tif'),FocalStatistics('inRas.tif',NbrRectangle(3,3, 'CELL'), 'MAXIMUM'), 'inRas.tif') OutFS2 = Con(IsNull('OutFS1'),FocalStatistics('OutFS1',NbrRectangle(3,3, 'CELL'), 'MAXIMUM'), 'OutFS1') OutFS2.save("C:\Project1\outFmax.tif")   Summary   In this article, we touched on what NoData is, how to process it away, and some things to consider about the nature of your input. We then went over several solutions for replacing NoData cells in a raster using the functionality available in ArcGIS Spatial Analyst. There are other scenarios that can have a similar objective. One example is to use an interpolation tool replace NoData cells in an elevation raster, with the goal of maintaining the local trends in the surround surface cells. Another is to first classify the nature of the NoData areas and then apply different workflows to each category sequentially to get a final product. You can take the logic behind the workflows shown here and apply them to many applications in your own work.   Additional reading  To learn more about this aspect of raster analysis and specific tools used, start out by looking at the following help topics: NoData in raster datasets NoData and how it affects analysis Discrete versus continuous data Map algebra in Spatial Analyst Analysis environments and Spatial Analyst How to change NoData cells to a value Conditional evaluation with Con How Focal Statistics works    The original blog was first published in the ArcGIS Blog, and can be found here: https://www.esri.com/arcgis-blog/products/analytics/analytics/fill-nodata-holes-in-raster-data/ ... View more

The Spatial Analyst extension offers new and improved capabilities in ArcGIS Pro 3.3. You can further refine your analysis with the Suitability Modeler by excluding specific locations. Use the new output PDF report option to summarize and share key details about how the optimal locations were identified. Take advantage of expanded capabilities, better performance, and improved display in Density, Distance, Solar, and Surface analysis. Read on to learn more. Where to get it? ArcGIS Pro 3.3 was released on May 7, 2024. Download from here For a complete summary of all the changes that have been made for this release, have a look at the What's New in ArcGIS Pro 3.3 blog post.   What’s changed for Spatial Analyst? Listed here are the main functional areas with improvements over the last release: 1. Suitability Modeling 2. Density analysis 3. Distance analysis 4. Solar analysis 5. Surface analysis   1. Suitability Modeler The Suitability Modeler adds the capability to exclude locations from analysis. You can now easily create reports to share with others. The color schemes have been updated, a new accessibility option has been added, and the user interface enhanced.   Exclude restricted locations Often when doing suitability analysis there are certain locations you wish to prevent from being considered in the analysis. The Suitability Modeler now has the capability to define those restricted locations. Examples of the types of locations you might want to restrict include land cover classes representing water bodies, cells that are within a distance buffer around environmentally protected land, or areas that are too far away from existing facilities networks. You can build up a query as a single or several clauses to define the restricted area, and display it as a map layer. See the elements outlined in cyan in the following screenshot. Learn more in the Exclude restricted locations help topic. Identify restricted locations in the Suitability Modeler in ArcGIS Spatial Analyst 3.3 Reports You can now Generate a suitability model report as a PDF that presents the key decisions made when creating the model. You can share the report with stakeholders and decision makers or use it to better understand the model. The report consists of summary information, a flow diagram of the model, details on the criterion transformations, the weights that were applied to the criteria, the final suitability map, and optionally a summary of located regions. Just click the Generate Report button on the Suitability Modeler ribbon to start the process. An example of the types of content that appear in a report is shown in the following graphic. Example of content that appears in reports from the Suitability Modeler in ArcGIS Spatial Analyst 3.3 Appearance, color display, and accessibility The interface for the Suitability Modeler has been improved in appearance and ease of use. The color schemes used in the maps, legends, and plots have been enhanced by increasing the color saturation, which improves the ability to discern changes in value across the full range of values. To support accessibility for individuals with the primary forms of color vision deficiency (CVD), an option is available that enables color schemes optimized for those with deuteranopia (green-blindness), protanopia (red-blindness), and tritanopia (blue-blindness).   2. Density analysis The Space Time Kernel Density tool had some algorithmic updates to improve the quality of the output results. Several targeted improvements were made that help with incorporating the tool in analytical workflows. The tool now always creates a multidimensional output, irrespective of the number of slices. There is now no restriction on the number of slices allowed by the tool. The range allowed for the minimum and maximum elevation parameters was expanded, improving support for negative elevation values.  A new help topic was added that provides more details on the calculations used by the tool, how the tool compares to other density tools, and some common applications. How Space Time Kernel Density works   3. Distance analysis The Distance Accumulation and Distance Allocation tools have two new options for analyzing the amount of vertical effort it takes to move over the landscape. Tobler’s hiking function is an empirical model that accounts for the adjustment in walking speed based on the slope of the surface in the direction of travel. The new vertical factor option Hiking Time can be applied to determine the time it would take to hike from any a source to any location using the most optimal route. The Bidirectional Hiking Time option finds the average time to hike in one direction on an out-and-back hiking trail along the most optimal route. Several optimizations were also made that improves the performance of these tools if the Distance Method parameter is set to the Geodesic option, or if either of Vertical Factor or Horizontal Factor parameters are set.   4. Solar analysis For the Feature Solar Radiation and Raster Solar Radiation tools, a new parameter is available that you can use to control the speed and accuracy of the computations for solar analysis. When the Sun Grid Map Level parameter is set to a lower value, larger sun map areas are used in the solar calculations. Since fewer sun maps are created, the tools run more quickly. Using higher values may increase the accuracy of the results, at the expense of increased processing time. Several improvements were made to performance, scalability, and support for GPU processing. The Raster Solar Radiation tool has increased performance and supports finer time intervals for analysis on the Moon. The Feature Solar Radiation tool has been optimized to better take advantage of GPU resources. Both tools can better support large input surface rasters and more input features. A CUDA compute capability of version 7.0 or later is recommended to take full advantage of additional GPU performance improvements.   5. Surface analysis For the Surface Parameters tool output raster, a customized default renderer is now applied to make the results from various settings of the Parameter type more visually distinctive. The following maps show the result of applying the Slope, Aspect, and Casorati curvature settings to an input DEM raster. Example output of updated renderers applied to the Surface Parameters tool output in ArcGIS Spatial Analyst 3.3 Summary While these are the main changes made for ArcGIS Pro 3.3, we here on the Spatial Analyst team made many other improvements to the functionality and performance. Be sure to update your install to the latest version and try it out. Watch this space! We will have other blog posts coming soon that cover some of the changes in more detail. The following link is updated with new posts as they become available. https://community.esri.com/t5/arcgis-spatial-analyst-blog/bg-p/arcgis-spatial-analyst-blog   See here for questions and discussion related to Spatial Analyst: https://community.esri.com/t5/arcgis-spatial-analyst-questions/bd-p/arcgis-spatial-analyst-questions   Here are some starting points if you want to learn more about Spatial Analyst: Learn more about the Spatial Analyst Extension product What is the Spatial Analyst Extension The Spatial Analyst toolbox in ArcGIS Pro   For more information about this release, please visit the ArcGIS Pro page or the What’s New documentation and post your questions in the ArcGIS Pro board in Esri Community.   ... View more

The Spatial Analyst extension introduces new and enhanced capabilities in ArcGIS Pro 3.2. This release gives you capabilities for evaluating the quality of suitability models. New tools are available for Density, Distance, and Solar analysis. Zonal analysis has a new option to improve workflows, as well as some new statistic types.   Where do I get it? ArcGIS Pro 3.2 was released on November 7, 2023. Download from here For a complete summary of all the changes that have been made for this release, have a look at the What's New in ArcGIS Pro 3.2 blog post.   What’s changed for Spatial Analyst? Listed here are the main functional areas with improvements over the last release: 1. Suitability Modeling 2. Density 3. Distance 4. Solar 5. Zonal   1. Suitability Modeler The new Evaluate capability of Suitability Modeler provides an integrated environment to evaluate the quality of a model. In the Evaluate tab, the maps, panes, plots, and content update dynamically with changes to the model criteria, weights, and transformations. You can use this immediate feedback to better understand your model. In the Evaluate pane, you can explore the composition of the weighted transformed criteria values at locations that have equally high suitability so you can make better choices between them. You can also explore the criteria interactions in several ways, including by individual cells, within parcels, relative to known observations, or in the final regions identified from a Locate operation. See the following topics for more information: Evaluate environment in Suitability Modeler Evaluate workflow and suitability modeling constraints Evaluate tab in Suitability Modeler Evaluate Pane in Suitability Modeler   Evaluate in Suitability Modeler in ArcGIS Spatial Analyst 3.2   2. Density analysis The new Space Time Kernel Density tool allows you to analyze density for other dimensions than relative position and magnitude in density calculations, such as time and depth. Some example of other dimensions and example applications of this capability include the following: Calculating the density of an attribute on a 2D surface in different time intervals. For example, identifying clusters of monthly crime in Kyoto from 2003 to 2005. Calculating the density of an attribute in a 3D cube using z as the additional dimension. For example, calculating the salinity based on depth in a certain area of ocean, or the PM2.5 particulate matter concentration in the air above a city. Calculating the density of an attribute in a 3D cube in different time intervals. For example, calculating the salinity in a certain area of ocean in different weeks, or the PM2.5 concentration for different hours of the day. 3. Distance analysis Use the new Optimal Corridor Connections tool to create an optimal network of corridors between multiple locations based on a constant width that you specify. Some applications this tool is well suited for are planning power lines, pipelines, railroads, other right-of-way corridors or transport systems.   4. Solar analysis The solar toolset has two new tools that offer significantly enhanced capability for solar analysis over the original tools. The Raster Solar Radiation tool calculates the solar insolation per unit area for every raster cell of a digital surface model (DSM). The Feature Solar Radiation tool calculates the incoming solar insolation for input points or polygon features relative to the surface (ground). Key improvements from the new tools include the following: The new algorithms support geodesic calculations and analysis over larger geographic areas. These tools achieve enhanced performance by taking advantage of multithreaded, parallel processing, and support the use of graphics processing unit (GPU) processing. To improve raster analysis workflows, you can save time by supplying precalculated input slope and aspect rasters, which is particularly helpful if repeating analysis for a large area of interest. You can also include a mask to constrain the analysis to defined analysis areas or locations. For feature analysis, you have the ability to specify size and orientation of the locations that receive solar radiation based on attribute information. When calculating solar radiation for a time interval, such as weekly or monthly, the results are returned as a time series raster (multidimensional raster) or a feature table. One thing to keep in mind is that calculating insolation can be computationally intensive for large data extents and when calculating many time intervals. This may require a substantial amount of computing power, memory, and hard disk space. Systems with high-end GPUs will see significant reductions in processing time compared to systems that are limited to performing calculations only on the CPU. Along with the many improvements, one of the most exciting enhancements is that we now support solar analysis on the Moon! This was done to help science and research by organizations such as NASA-JPL and the Canadian Space Agency to support future lunar exploration missions.   Examples of solar analysis output   5. Zonal analysis You can now join the output table from the Zonal Statistics as Table tool directly to the input zone data with the new Output Join Layer parameter. This eliminates the step where you had to add the table to the input data with a separate tool before doing further analysis. Several new zonal statistics options were added. For the Zonal Statistics tool, the Zonal Statistics raster function, and the Zonal Statistics as Table tool, the four new statistics options are Majority count, Majority percentage, Minority count, and Minority percentage. The Zonal Statistics as Table tool has two additional options, one being Majority value, count, and percentage, with the other being Minority value, count, and percentage.   Summary That covers the notable new capabilities the Spatial Analyst extension makes available to you for ArcGIS Pro 3.2. Be sure to check back here, as we will add links to new blogs that cover aspects of these improvements in more detail. See the following links for notifications of new resources as we add them. https://www.esri.com/arcgis-blog/products/analytics/analytics/spatial-analyst-resources/ https://community.esri.com/t5/arcgis-spatial-analyst/ct-p/arcgis-spatial-analyst   ... View more

The Spatial Analyst extension in ArcGIS Pro 3.1 has new capabilities, as well as improvements in several key areas.  This includes an exciting new tool for identifying surface landforms. A new tool that makes finding corridors easier. Improvements for several tools in how they leverage parallel processing and GPUs for better performance.  The Suitability Modeler has some internal improvements. Many tools that perform per-cell raster mathematical operations have enhanced support for multidimensional input and output.   What’s changed for Spatial Analyst? Listed here are the main functional areas that we have improved over the last release: Suitability Modeling Distance Hydrology Neighborhood Reclassification Surface Multidimensional Deprecated functionality   1. Suitability Modeler The Suitability Modeler has under-the-hood improvements in several key areas. When the Auto Calculate option in the Suitability Analysis portion of the ribbon is selected, the recalculation of the model when changes to the transformation or weight of a criterion are applied is improved. Querying, sharing, and saving of models has been enhanced. Some internal changes were made in preparation for a future release, where additional interface elements will give you the capability to evaluate the quality of your suitability model.   2. Distance analysis Least Cost Corridor is a new geoprocessing tool that is used for optimally connecting locations with corridors. This tool leverages recent improvements in distance analysis by incorporating the distortion-free algorithm. It also simplifies the corridor creation process by allowing you to apply a threshold directly, either as a percentage or a specified accumulative cost value, without having to use any other tools.  This capability can also be accessed with the Least Cost Corridor raster function. Results from the Least Cost Corridor tool (green cells) connecting two parks You can learn more about this new tool in the following blog post: Create a wildlife corridor with the new Least Cost Corridor tool in ArcGIS Pro 3.1  For the Distance Accumulation and Distance Allocation tools, the algorithm used for applying horizontal and vertical factors was rewritten to produce non-distorted results instead of being 8-directional. The performance was enhanced, and space optimizations were made when calculating straight-line distance. These changes are also available in the Distance Accumulation and Distance Allocation raster functions.   3. Hydrology analysis The Derive Stream As Line tool was introduced in Pro 3.0 and is useful for easily generating streamline features from an elevation surface without having to fill in the sinks or other depressions beforehand.  The tool now uses the Douglas-Peucker algorithm as the default method to simplify the results. It produces smoother lines by retaining critical points while identifying and removing relatively redundant vertices.   4. Neighborhood analysis For the Focal Statistics and Filter tools, multiband input is now supported directly in the geoprocessing tool dialog box. (Previously, multiband input was fully supported only when the tools were run in Python scripts.)   5. Reclassification For the Rescale by Function tool, a new help topic is available that provides more detailed explanations of the formulas used for transformation functions. The Slice tool now supports the Parallel Processing Factor environment, which can improve performance on large datasets.   6. Surface analysis Do you love doing raster surface analysis? Have we got an exciting new tool for you! The new Geomorphon Landforms tool can help you identify and classify landscape features into several common types, such as peaks, ridges, spurs, and foot slopes, among others. Ridge and valley landform types in the northeastern part of Switzerland, close to the Alte Aare river, identified using the Geomorphon Landforms tool in ArcGIS Pro 3.1. You can learn more about this new tool in the following blog post: Classify terrain with the new Geomorphon Landforms tool   For several releases, the Aspect, Geodesic Viewshed, and Slope tools have been able to be GPU accelerated for enhanced performance, provided that your system is equipped with a compatible GPU device. With this release, you can use the new Target device for analysis parameter to specify whether to use the GPU or the CPU, or let the system determine which to use. With the Aspect and Slope tools, for the Method parameter, the default Planar setting now also supports GPU processing. The  Geodesic Viewshed tool now estimates the amount of temporary space required to complete the calculations, and reports that value at run time as tool messages. Also specified is the directory where the temporary data is written.    The Surface Parameters tool has a new Input analysis mask parameter. You can use it to limit the analysis to locations of interest within the input surface raster.  The Parallel Processing Factor environment is now supported, offering enhanced performance. The Surface Parameters raster function also supports the ability to limit the locations analysis will occur with the Analysis mask parameter.   7. Multidimensional analysis Many Spatial Analyst tools that perform local raster operations now support multidimensional data as input and can create multidimensional output. The following list identifies the improved tools: All tools in the Math toolset, except for the three combinatorial tools (Combinatorial And, Combinatorial Or, and Combinatorial XOr) All tools in the Local toolset, except for Combine The Con tool The Raster Calculator tool A new Usage note was added to the help for each of these tools, providing additional details.   8. Deprecated functionality As new tools are developed, or existing tools are updated to provide equivalent functionality in a better way, some tools will be put on a pathway to be deprecated.  Typically, this means that initially, a notice will be added to a tool, and alternatives methods to migrate to are identified.  The tool will continue to be available and operate in the same way for one to three subsequent releases. After this initial deprecation state, the tool will be moved to a fully deprecated state. It will continue to be installed with ArcGIS Pro, so that your existing models and scripts will continue to work, but the tool will no longer be directly accessible. For more details on the deprecation process, please see the following topics: Deprecated tools Deprecated raster functions   The following tools and functions are still available but have been put on the deprecation path at this release. The alternative functionality that replaces them, by offering improved functionality or performance, is identified. The Distance (Legacy) geoprocessing toolset.  See the Migrating from legacy distance tools to distortion free distance tools topic for additional details and a table that maps the tools being deprecated to their replacements. The Distance (Legacy) category or raster functions is similarly being deprecated.  See the Migrating from legacy distance functions to distortion-free distance functions topic for details on the replacement functions.  The Extract by Points and Extract by Polygon tools. The capabilities of these tools is replaced by the Extract by Mask tool, which was enhanced in a previous release with new parameters for controlling where the extraction takes place (Extraction Area) and the extent of the output raster (Analysis extent).   Summary We hope your work will benefit from the new and improved capabilities that come to the Spatial Analyst extension in ArcGIS Pro 3.1. We will also have some additional blogs coming with additional details. Remember to drop by the Spatial Analyst Communities page where lots of good questions are asked and answered!      https://community.esri.com/t5/arcgis-spatial-analyst/ct-p/arcgis-spatial-analyst ... View more

ArcGIS Pro 3.0 makes some exciting new capabilities available to you for the Spatial Analyst extension. In the Suitability Modeler it is now easier to create more complex models, and there is an option to process large raster data efficiently with Raster Analytics. If you do water flow modelling, there are some very powerful new tools for hydrology analysis. There is a new tool to calculate a spatial relative risk surface. You can now perform zonal analysis with circular statistics.  Read on for details on these changes, and more.   Where to get it? ArcGIS Pro 3.0 was released on June 23, 2022. Download from here A selection of the changes in this release can be found in the What's New in ArcGIS Pro 3.0 blog post and video. For a more comprehensive list, see What's New in ArcGIS Pro 3.0 in the online Help. Because this is a major release, the information in the Migration from ArcGIS Pro 2.x to 3.0 help topic will help guide you through the transition.   What’s changed for Spatial Analyst? Listed here are the main functional areas that have been improved over the last release: Suitability Modeling Density Extraction Hydrology Neighborhood Segmentation and classification Statistics Surface Zonal ArcPy   1. Suitability Modeler The Suitability Modeler in ArcGIS Pro 3.0 sees continued improvements in its capability, usability and ability to work on large tasks. The suitability modeler now has the ability to split up complicated models into component sub-models.  Breaking down the analysis into logical groupings improves the ability for domain experts to collaborate within their areas of expertise.  A central planner can then combine those specialized models to come up with more comprehensive overall plans to make better decisions. You can now share and run suitability models on servers using ArcGIS Pro as a client. By running the models using Raster Analytics, you can take advantage of the power of distributed processing to perform analysis on larger datasets more efficiently than before. The process is pretty straight forward.  First, the suitability model is shared as a portal item.  When the model is run, the processing occurs on the servers, with the output being created as web imagery layers in the active portal.   2. Density analysis The new Calculate Kernel Density Ratio tool uses two input feature datasets to calculate a spatial relative risk surface.  This is useful when the phenomenon being analyzed requires a control. One application of this tool could be an epidemiologist who is studying the occurrences of a disease to determine if high prevalence's in certain areas could be linked to environmental factors. The density ratio is calculated using the disease occurrences as the numerator and the total population as the denominator. The result surface shows the density of disease occurrence normalized by population density, which makes it possible to determine where the disease occurrences are higher than expected. In comparison to the Kernel Density tool, the output from this new tool is normalized, meaning the resulting values are proportional.   3. Extraction analysis The Extract By Mask  tool has been updated with two new optional parameters.  With the Extraction Area parameter, you can now extract the areas outside the mask, as well as inside it. This brings the tool in line with other tools in the extraction toolset. The Analysis Extent parameter gives you more control over the extent of the output raster. You can define the output analysis area explicitly in several ways, either by typing values, choosing the display extent, selecting a layer, or browsing for an input dataset. The default analysis extent will be the intersection of the input raster and the input feature or mask data.   4. Hydrology analysis A hallmark feature for this release is the new Derive Continuous Flow tool, which creates consistent flow direction and flow accumulation rasters directly, in one step, regardless of whether sinks have been treated or not.  Accompanying this are two new tools for extracting streams from elevations surfaces directly, Derive Stream as Line  and Derive Stream As Raster.  All of these tools support the ability to specify a dataset that delineates real depressions in the elevation surface, giving you more control over landscape features that dictate water flow. If you've done hydrology analysis in the past, you'll know one of the requirements was to spend time creating what is called a hydrologically conditioned DEM to use as the elevation surface.  This typically meant identifying sinks (low points) or depressions in the data, especially artificial ones caused by artifacts in the source data or previous processing steps.  Once identified, additional work had to be done to smooth them over, with the end goal of producing an elevation surface over which water will flow in the expected direction. With these new tools, you can get to the fun part of analysis and modelling right away!   5. Neighborhood analysis For the Block Statistics tool, Focal Statistics tool, and Focal Statistics raster function, when the Weight neighborhood type is selected, the calculations used for the Mean and Standard deviation statistics has been improved.  The denominator in the equation is now the sum of the weight values applied to the kernel, instead of the number of cells in the kernel neighborhood.  One thing to note for both the weighted mean and standard deviation is that the weights must be positive values.  For more details, see the help content for the weighted neighborhood calculations. The calculation for the weighted Sum statistic is unchanged. As a part of this work, for the Block Statistics tool when the Weight neighborhood is selected, the Median and Minority options are no longer available.  The available choices of statistics now match those of Focal Statistics.   6. Segmentation and classification For the Compute Confusion Matrix raster tool, the Intersection over Union (IoU) mean value is now computed for each class. IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union between the predicted segmentation and the ground truth. The Export Training Data for Deep Learning tool has three new optional input parameters: Instance Feature Class, Instance Class Value Field, and Minimum Polygon Overlap Ratio. For the Metadata Format parameter,  a new Panoptic_Segmentation metadata format option is available. This tool can now also take advantage of parallel processing for improved performance.   7. Statistics for majority and minority ties Several Spatial Analyst tools calculate various statistics on rasters based on particular sets of input values.  Some of those statistics are Majority and Minority, which calculate the most frequently occurring and the least frequently occurring values in those sets, respectively.  There can be cases where is a tie, where there are multiple values that occur with the same highest or lowest frequency. For the Zonal Statistics tool, the logic applied to this scenario is to select the lowest of the tied values.  For example, if the list of cell values in a zone were 1, 1, 2, 2, 2, 5, 5, 5, and 6, for the majority there is a tie between values 2 and 5, which each have a frequency of 3.  The tool will return a value of 2 for the zone, since it is the lowest of the tied vales. For the Cell Statistics, Block Statistics and Focal Statistics tools, as well as the Cell Statistics and Focal Statistics raster functions, the logic that had been applied historically when there was a tie was to return a NoData value. This often lead to having more areas of NoData in the output raster than may have been expected.  To avoid this, the logic for these tools was updated in Pro 3.0 to match that used by Zonal Statistics, and so now also return the lowest of the tied values. For the Focal Statistics tool, a slightly different approach is used in order to retain the significance of the processing cell itself.  Here, the lowest of the tied values will be returned, unless the processing cell itself is one of the tied values.  In that scenario, the value returned for that set will be that of the processing cell itself   8. Surface analysis Have you been using the advanced capabilities for geodesic surface analysis offered by the Surface Parameters geoprocessing tool?  With this release, these capabilities are now available to you in two additional ways.  One is with the Surface Parameters raster function. The other is with the Surface Parameters raster analysis portal tool, which will be available when you are signed in to a suitably configured ArcGIS Enterprise portal.   9. Zonal This release sees the introduction of circular calculations when performing zonal statistics. What does this mean?  Let's consider an abstract example where the statistic you want to calculate is the mean (average).  Say you have two input cell values A and B for a particular zone, and those values represent measures of 0 degrees and 360 degrees. If you do a regular arithmetic calculation for the mean [(valueA + valueB) / 2 = (0 + 360) / 2 = 180], the result is probably not what you would have intended.  How can the average of two values that represent due North be due South?  By applying circular calculations, the mean value of these two values would actually be 0.  See the following table for some additional example comparing the arithmetic mean to the circular mean for different angle inputs. Examples: Input angles Arithmetic mean Circular mean 0, 360 180 0 0, 90, 180, 270 135 129.6 0, 90, 180, 270, 360 180 0   In order to calculate circular statistics correctly, two new parameters are available. Use the Calculate Circular Statistics parameter to indicate to the tool whether to calculate ordinary linear statistics or cyclical statistics.  An additional parameter, Circular Wrap Value, is used to range of a given circular statistic.  Other examples of cyclical quantities than compass direction in degrees (0 to 360) include hours of a day (0 to 24 hours), or fractional parts of real numbers. The option to perform circular statistics applies only to the following statistics: Mean, Majority, Minority, Standard Deviation, and Variety. Circular statistics is available from the following functionalities: • Spatial Analyst geoprocessing tools: Zonal Statistics, Zonal Statistics as Table • Raster Analysis geoprocessing tools for performing raster analysis on data in your portal: Summarize Raster Within, Zonal Statistics as Table • Raster functions: Zonal Statistics   10. ArcPy In the ArcPy Spatial Analyst module, you can now use the Render function to apply symbology to a raster object.  The symbology can be a rendering rule or a color map. This is particularly useful for displaying data in a Jupyter notebooks.   Summary We hope that you will find the new and updated functionality available in ArcGIS Pro 3.0 to be useful for your work. We have some other blogs to come that go into more detail on some of this functionality, so be on the lookout for them. ... View more

For ArcGIS Pro 2.9, we made a number of improvements to the Spatial Analyst extension. Key amongst them is an exciting new capability for the Suitability Modeler. We added new functionality for calculating statistics for a moving window across multidimensional raster data. Read on for more details on these and other new and improved capabilities available with this release.   Where do I get it? ArcGIS Pro 2.9 was released on November  11, 2021. Download from here For a complete summary of all the changes in this release, have a look at the What’s New in ArcGIS Pro 2.9 blog post and video.   What’s changed for Spatial Analyst? Listed here are the main functional areas that saw improvements over the last release: Suitability Modeling Interpolation analysis Local raster analysis Multidimensional analysis Raster reclassification Segmentation and classification Surface  analysis   1. Suitability Modeler The Suitability Modeler in ArcGIS Pro 2.9 sees a significant advance in technical capability for working with servers. We also made a number of general enhancements and improvements in quality. Suitability modeler on servers With this release, the Suitability Modeler can now be also run on servers using ArcGIS Pro as a client. The suitability and locate maps you create can be shared on the server. To get access to these capabilities, you need to be signed in to an ArcGIS Enterprise portal that has ArcGIS Image Server configured for raster analytics. A new Output type parameter on the Suitability and Locate tabs allow you to select between running the suitability modeling workflow locally or on servers. Enhancements and improvements Use the new Sources tab to: see the sources for each criteria, identify which criteria in the model are transformed, change the source for a criterion, and remove a criterion from the model. When changing the source for a criterion, a series of warning and error messages ensure the new source and the transformation being applied are appropriate. You can use this tab to convert local criteria into web imagery layers, which are the required input type to run the analysis on servers. For the Range of Classes tab in the Transformation pane, you can now enter suitability values directly into the transformation table. Use the Classify button to change the number of classes. You can select from seven different classification methods. We refined the interaction for multiple map views. You can remove criteria from the model simply by clicking a remove button next to the criterion. All messages from the underlying geoprocessing tools are now displayed.   2. Interpolation analysis If you have used the Natural Neighbor tool to interpolate raster surfaces, perhaps you ran into difficulties with increasing numbers of input points? You will be happy to know we enhanced the tool to support very large inputs, up to approximately two billion points.   3. Local analysis A new Percentile statistic option is available for the Cell Statistics GP tool and  Cell Statistics raster function. When that option is selected, a new Percentile Value parameter is enabled with which to specify the particular percentile (between 0 and 100) to calculate. When the statistics operation is Median or Percentile, a new Percentile Interpolation Type parameter is enabled. Use this parameter to select the method of interpolation when the specified percentile value lies between two input cell values.   4. Multidimension analysis You can now calculates statistics over a moving window on multidimensional data along a specified dimension with the new Dimensional Moving Statistics tool and Dimensional Moving Statistics raster function. Moving statistics is also known as moving window statistics, rolling statistics, or running statistics. Essentially, a predefined window around each dimension value is used to calculate various statistics before moving to the next. This capability helps in workflows such as smoothing out noise or anomalies across dimensions. An exciting new statistic type is available with this tool. The Circular Mean calculates the mean for angles or other cyclic quantities, such as compass direction in, or months in a year. This setting enables a Circular Wrap Value parameter for  designating a value to wrap around to calculate the circular mean. In angle calculations, for example, the parameter should be set to 360 (degrees). This means the value 360 will be wrapped to 0, the value 370 will be wrapped to 10, and so on. Another application is for time calculations based on months in a year, where the circular wrap value should be 12. In this case, an input value of 13 will be wrapped to 1. A new method is available for how to handle NoData values in the statistics calculation. In addition to the typical Data and NoData choices, the Fill NoData option will replace NoData values in the input with the result of applying the selected statistic on the values within the defined window. The Aggregate Multidimensional raster function has a new Percentile statistics setting. This option enables a new Percentile Value parameter. A new Percentile Interpolation Type parameter becomes available when the statistics operation is Median or Percentile.   5. Raster reclassification The Slice tool has four new methods available for reclassifying rasters. For the new Defined interval method and the two new Standard deviation methods, the new Interval size parameter determines the number of zones in the output raster. For the new Geometric interval method, the setting of the Number of output zones parameter determines the number of zones. The new Change NoData to value for output parameter makes replacing NoData cells in the input raster to a value of your choice easier. One small detail to note is that the parameter labeled as Base zone for output in ArcGIS Pro 2.8 and earlier is now labeled Starting value for output. Since the name for this parameter in Python remains the same, your scripts will continue to run as-is.   6. Segmentation and classification The new Train K-Nearest Neighbor Classifier tool generates an Esri classifier definition file (.ecd) using the K-Nearest Neighbor (KNN) classification method. Support for multidimensional raster input was added to the Create Accuracy Assessment Points and Update Accuracy Assessment Points tools, as well as commensurate parameters to select the dimension field. For the Export Training Data For Deep Learning tool, the Metadata Format parameter has a new Imagenet keyword option for object detection labeling and object tracking. The RCNN Masks keyword can now be used for object tracking, not just object detection. The Crop Mode parameter is now applicable when the ImageNet keyword is set.   7. Surface analysis For the Surface Parameters tool, four new Parameter type choices are available. Those are: Plan (projected contour) curvature, Contour geodesic torsion, Gaussian curvature, and Casorati curvature. See the Usage notes and the parameter table to learn about these options, but be sure to read the How Surface Parameters works topic for additional information.   Summary We hope that you enjoy the updates and improvements that are available for you in Spatial Analyst for ArcGIS Pro 2.9. ... View more

A primary focus for the ArcGIS Pro 2.8 release is to improve performance and productivity. But that didn't prevent us from adding some new and enhanced capabilities to the Spatial Analyst extension! We also completely revamped the help content for Distance analysis. Read on to learn more.   Where do I get it? ArcGIS Pro 2.8 was released on May 13, 2021. Download from here See What’s New for ArcGIS Pro 2.8 for a complete summary of all the changes that were made for this release.   What’s changed? The main areas of improvement are: Suitability Modeler Distance Multidimensional Reclassification Segmentation and classification Surface Zonal ArcPy   1. Suitability Modeler The interface of the Suitability Modeler was enhanced, and performance improved throughout. The Unique Categories and Range of Classes transformation methods now utilize bar charts.   2. Distance analysis Many new conceptual help topics for distance analysis were added for ArcGIS Pro 2.8.  Read through them to learn more about the various kinds of analysis you can perform with the Spatial Analyst extension. There are also plenty of graphics, examples, and use cases. The geodesic accuracy and performance was improved for the following: Geoprocessing tools: Distance Accumulation, Distance Allocation, and Optimal Region Connections Raster functions: Distance Accumulation and Distance Allocation Legacy tools: Euclidean Allocation, Euclidean Back Direction, Euclidean Direction and Euclidean Distance Legacy raster functions: Euclidean Allocation, Euclidean Back Direction, Euclidean Direction and Euclidean Distance The handling of units of the vertical coordinate system was improved for the following: Geoprocessing tools: Distance Accumulation and Distance Allocation Raster functions: Distance Accumulation and Distance Allocation The Least Cost Path raster function was relocated to the Legacy group.   3. Multidimension analysis The Aggregate Multidimensional Raster tool has a new Percentile aggregation method. New parameters are available to set the percentile value and the interpolation method to use. Use the new Dimensionless parameter to specify whether the input layer has dimension values.   4. Reclassification For the Spatial Analyst Reclassify tool, the Reclassification parameter now allows you to generate a remap table based on the values of the input raster. Use the Classify option to select the Data classification method and the number of classes to use.   5. Segmentation and classification analysis The Export Training Data For Deep Learning tool has a new Additional Input Raster parameter. Use it to set an additional input imagery source for image translation methods.   6. Surface analysis To better represents its capabilities, the Viewshed 2 tool dialog was renamed to Geodesic Viewshed. In Python, the tool name remains as Viewshed2, so your existing scripts will continue to work as-is.   7. Zonal analysis Performance for calculating the Median and Percentile statistics has been improved when float rasters are used as input to the Zonal Statistics tool, Zonal Statistics as Table tool, or the Zonal Statistics raster function.   8. Geoprocessing functions In the Spatial Analyst module, Aggregate and Slope now create a function raster output when executed from ArcPy.   Summary While the main driver for this release was improving the quality, we here on the Spatial Analyst team also put in some other additional improvements in capabilities that we hope you will appreciate.  As always, we welcome your feedback, so let us know what is working well for you, what needs some attention, and what new things you would like to see. ... View more