{"id":249682,"date":"2018-07-02T12:31:53","date_gmt":"2018-07-02T19:31:53","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=249682"},"modified":"2020-08-24T13:31:38","modified_gmt":"2020-08-24T20:31:38","slug":"getting-the-most-out-of-zonal-statistics","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/getting-the-most-out-of-zonal-statistics","title":{"rendered":"Getting the most out of Zonal Statistics"},"author":7091,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[39421,23391],"industry":[],"product":[36561,37031,36991],"class_list":["post-249682","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-spatial-analyst","tag-spatial-analytics","product-arcgis-pro","product-spatial-analyst","product-arcgis-desktop"],"acf":{"short_description":"Understand how feature zones work in zonal statistics.","flexible_content":[{"acf_fc_layout":"content","content":"<p>When working with Zonal tools in the Spatial Analyst toolbox, have you occasionally gotten results that you didn&#8217;t quite expect? Here we&#8217;ll cover a few scenarios where and why you might have run into some issues, how to work around them, and how things have changed in the latest release to avoid them in the first place. The <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/zonal-statistics.htm\">Zonal Statistics<\/a> tool calculates statistics on values of a raster within the zones defined by another dataset. To learn more, see <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/how-zonal-statistics-works.htm\">How the zonal statistics tools work<\/a>.<\/p>\n<p>The zone input can either be a feature or a raster. If the zones are defined by features, an internal feature to raster conversion will occur. The internal conversion for a polygon zone uses the <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/conversion\/how-polygon-to-raster-works.htm\">cell center method<\/a> to rasterize the input. With an analysis extent calculation method of intersection-of or union-of, the origin of the internal raster may be determined entirely by the feature class. Snapping is then performed relative to that origin: the internal raster origins at the lower-left corner of the analysis extent and its upper-right corner is adjusted by the cell size. The cells of the internal zone raster and the value raster may not align, which will trigger a resampling during the zonal operation. Resampling will also occur if the input zone is a raster with different cell size and\/or alignment. Often times, we end up getting an unexpected result due to the feature to raster internal conversion or misalignment of the zone and value raster. Let&#8217;s look at some of these scenarios in detail:<\/p>\n<p><strong>1. Unexpected statistics values<\/strong><\/p>\n<p>The common source of unexpected statistics values is the misalignment of the cells of the zone and value rasters.<\/p>\n<p>In this example in figure 1a below, you may expect the Zonal Statistics tool to compute the statistics based on the cells of the value raster whose cell center falls within the feature zone, which are values 79, 81 and 27. However, due to the way the default internal conversion will be performed based on the origin and extent, how the feature zone gets rasterized will create an unexpected output. As you can see in figure 1b the rasterization grid used for the internal conversion does not align with the value raster. Therefore, using the cell center method, the zone raster will end up analyzing a different set of cells from the value raster (see figure 1c) than you originally anticipated from figure 1a.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":263772,"id":263772,"title":"Figure 1: Internal conversion of feature zone without considering the value raster for cell alignment.","filename":"pic1.png","filesize":16841,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/getting-the-most-out-of-zonal-statistics\/pic1-2","alt":"Figure 1: Internal conversion of feature zone without considering the value raster for cell alignment.","author":"7091","description":"Figure 1: Internal conversion of feature zone without considering the value raster for cell alignment.","caption":"Figure 1: Internal conversion of feature zone without considering the value raster for cell alignment.","name":"pic1-2","status":"inherit","uploaded_to":249682,"date":"2018-07-02 06:39:30","modified":"2018-07-02 06:39:59","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":537,"height":297,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","thumbnail-width":213,"thumbnail-height":118,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","medium-width":464,"medium-height":257,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","medium_large-width":537,"medium_large-height":297,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","large-width":537,"large-height":297,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","1536x1536-width":537,"1536x1536-height":297,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","2048x2048-width":537,"2048x2048-height":297,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","card_image-width":537,"card_image-height":297,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic1.png","wide_image-width":537,"wide_image-height":297}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>To avoid this issue, there is a step you can take to ensure that the feature zone being converted to a raster is aligned with the value raster. Simply set the <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/environment-settings\/snap-raster.htm\">snap raster<\/a> to the value raster from the tool environment. The misalignment can also occur when the input zone is a raster, if the cell size and\/or cell alignment does not align with the value raster. You can set the snap raster environment to the value raster to get the expected output.<\/p>\n<p>Let&#8217;s look in the example below, in figure 2b, to understand how setting the snap raster to the value raster ensures the rasterization grid used for the internal conversion aligns with the value raster in figure 2b. As a result, the zone raster will analyze those cells from the value raster (see figure 2c) that you originally anticipated from figure 2a.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":263782,"id":263782,"title":"Figure 2: Internal conversion of feature zone considering the value raster for cell alignment.","filename":"pic2.png","filesize":16798,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/getting-the-most-out-of-zonal-statistics\/pic2-2","alt":"","author":"7091","description":"","caption":"Figure 2: Internal conversion of feature zone considering the value raster for cell alignment. ","name":"pic2-2","status":"inherit","uploaded_to":249682,"date":"2018-07-02 06:40:38","modified":"2018-07-02 06:40:53","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":542,"height":293,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","thumbnail-width":213,"thumbnail-height":115,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","medium-width":464,"medium-height":251,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","medium_large-width":542,"medium_large-height":293,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","large-width":542,"large-height":293,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","1536x1536-width":542,"1536x1536-height":293,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","2048x2048-width":542,"2048x2048-height":293,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","card_image-width":542,"card_image-height":293,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic2.png","wide_image-width":542,"wide_image-height":293}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><strong><em>The good news is that ArcGIS Pro 2.2 and ArcMap 10.6.1 use the value raster as the snap raster by default for internal conversion of the feature zones. <\/em><\/strong><em>If you are using a version prior to ArcGIS Pro 2.2 or ArcMap 10.6.1, specify the value raster as the snap raster in the environment.<\/em><\/p>\n<p><strong>2. Missing zones in the output<\/strong><\/p>\n<p>The most frequent cause of missing zones in the output occurs when the cell center of the rasterization grid does not fall within the feature zone. This can occur for zones that are smaller than the area of a cell of the internal zone raster or even for larger zones.<\/p>\n<p>In the example below, let&#8217;s look at how rasterization occurs for zones of different size and location. Figure 3a has three zones, where, zone1 is larger than a cell, and zone2 and zone3 are smaller than a cell, and the cell center falls outside zone2 and within zone3. During the zone rasterization process in figure 3b, it so happens that no cell centers fall within zone1 and zone2, and only zone3 contains a cell center. Therefore, only zone3 will be rasterized and the other two zones will essentially disappear, as shown in figure 3c.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":263812,"id":263812,"title":"Figure 3: Internal conversion of feature zone leading to missing zones.","filename":"pic3.png","filesize":15083,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/getting-the-most-out-of-zonal-statistics\/pic3-2","alt":"Figure 3: Internal conversion of feature zone leading to missing zones.","author":"7091","description":"Figure 3: Internal conversion of feature zone leading to missing zones.","caption":"Figure 3: Internal conversion of feature zone leading to missing zones.","name":"pic3-2","status":"inherit","uploaded_to":249682,"date":"2018-07-02 06:42:59","modified":"2018-07-02 06:43:15","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":568,"height":255,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","thumbnail-width":213,"thumbnail-height":96,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","medium-width":464,"medium-height":208,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","medium_large-width":568,"medium_large-height":255,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","large-width":568,"large-height":255,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","1536x1536-width":568,"1536x1536-height":255,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","2048x2048-width":568,"2048x2048-height":255,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","card_image-width":568,"card_image-height":255,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic3.png","wide_image-width":568,"wide_image-height":255}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>To avoid this, ensure that each of your zones contains one or more cell centers. You can create more cell centers by specifying a smaller <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/environment-settings\/cell-size.htm\">cell size<\/a> in the environment. The default analysis cell size comes from the value raster. Therefore, specifying a cell size that is smaller than that of the value raster will enable more zones to be captured.<\/p>\n<p>For figure 4, let&#8217;s repeat the same example from figure 3 to see how changing cell size better captures all the zones. Similar to figure 3a, figure 4a also has three zones, where, zone1 is larger than a cell, and zone2 and zone3 are smaller than a cell but are spatially located differently. Figure 4b shows a finer rasterization grid based on a cell size that is four times smaller than the default cell size. During the zone rasterization process, multiple cell centers now fall within each of the zones. As a result, all the zones get rasterized as shown in figure 4c.<\/p>\n<p>Keep in mind that specifying a smaller cell size, will generate a larger output raster.\u00a0 In this example, the output raster will be sixteen times larger than the raster with default cell size. Even more important, the higher resolution output may give the wrong perception of a higher quality result than what it actually is, since the additional detail does not actually exist in the input value raster.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":263822,"id":263822,"title":"Figure 4: Internal conversion of feature zone with a smaller cell size capturing all zones.","filename":"pic4.png","filesize":19992,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/getting-the-most-out-of-zonal-statistics\/pic4-2","alt":"","author":"7091","description":"","caption":"Figure 4: Internal conversion of feature zone with a smaller cell size capturing all zones.","name":"pic4-2","status":"inherit","uploaded_to":249682,"date":"2018-07-02 06:43:50","modified":"2018-07-02 06:44:09","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":572,"height":262,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","thumbnail-width":213,"thumbnail-height":98,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","medium-width":464,"medium-height":213,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","medium_large-width":572,"medium_large-height":262,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","large-width":572,"large-height":262,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","1536x1536-width":572,"1536x1536-height":262,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","2048x2048-width":572,"2048x2048-height":262,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","card_image-width":572,"card_image-height":262,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/06\/pic4.png","wide_image-width":572,"wide_image-height":262}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><em>Note: If you are using a version prior to ArcGIS Pro 2.2 or ArcMap 10.6.1, specify the value raster as the snap raster in the environment.<\/em><\/p>\n<p><strong>3. Other sources of unexpected result<\/strong><\/p>\n<p>You may also get unexpected results during the <a href=\"http:\/\/prodev.arcgis.com\/en\/pro-app\/tool-reference\/conversion\/converting-features-to-raster-data.htm\">rasterization<\/a> of feature zones if you have:<\/p>\n<ul>\n<li>coincident points or multiple points on one cell<\/li>\n<li>coincident polylines or multiple polylines passing through one cell<\/li>\n<li>coincident polygons or partially overlapping polygons<\/li>\n<\/ul>\n<p>Be sure to read the <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/zonal-statistics.htm\">Zonal Statistics<\/a> and <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/how-zonal-statistics-works.htm\">How Zonal Statistics works<\/a> help pages to learn more about how this tool works.<\/p>\n<p><strong>Summary <\/strong><\/p>\n<p>With the information presented here, you should be able to better understand how this tool operates, and how to guarantee better and more understandable results.\u00a0 Keep in mind that there is a similar behavior in play for other Zonal tools, such as <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/tabulate-area.htm\">Tabulate Area<\/a>, <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/zonal-histogram.htm\">Zonal Histogram<\/a> and <a href=\"http:\/\/prodev.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/zonal-statistics-as-table.htm\">Zonal Statistics as Table<\/a> so you can employ the lessons learned here to achieve greater success with those tools, too. Keep in mind, that ArcGIS Pro 2.2 and ArcMap 10.6.1 now use the value raster as the snap raster by default for internal conversion of the feature zones. If you are using a version prior to ArcGIS Pro 2.2 or ArcMap 10.6.1, specify the value raster as the snap raster in the environment.<\/p>\n<p>Let us know if you encounter other scenarios where you get an unexpected result while doing your analysis, or if you have any questions or comments. You can reach me at <a href=\"mailto:schatterjee@esri.com\">schatterjee@esri.com<\/a>.<\/p>\n"}],"authors":[{"ID":7091,"user_firstname":"Sarmistha","user_lastname":"Chatterjee","nickname":"Sarmistha Chatterjee","user_nicename":"schatterjee17","display_name":"Sarmistha Chatterjee","user_email":"SChatterjee@esri.com","user_url":"","user_registered":"2018-03-02 00:19:17","user_description":"Sarmistha is a Product Engineer in Esri's raster analysis group, where she focuses on raster and scientific multidimensional data analysis. She is especially interested in combining GIS technology, spatial analysis, and software engineering to study changes for conservation and better decision-making. Before joining Esri in 2017, Sarmistha earned her PhD in Geography from the University of Delaware, where she researched fluvial geomorphology, water resources, and spatial modeling.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2024\/08\/Headshot_SarmisthaChatterjee-213x200.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}],"related_articles":[{"ID":76751,"post_author":"7091","post_date":"2017-05-01 08:00:38","post_date_gmt":"2017-05-01 08:00:38","post_content":"","post_title":"Why your output raster is larger than your environment extent?","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"why-your-output-raster-is-larger-than-your-environment-extent","to_ping":"","pinged":"","post_modified":"2018-03-26 21:12:54","post_modified_gmt":"2018-03-26 21:12:54","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/why-your-output-raster-is-larger-than-your-environment-extent\/","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":259412,"post_author":"5751","post_date":"2018-06-29 16:18:02","post_date_gmt":"2018-06-29 16:18:02","post_content":"","post_title":"Executing Spatial Analyst Tools Using ArcGIS Pro SDK","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"executing-spatial-analyst-tools-using-arcgis-pro-sdk-2","to_ping":"","pinged":"","post_modified":"2018-06-29 17:07:55","post_modified_gmt":"2018-06-30 00:07:55","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=259412","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":123261,"post_author":"6871","post_date":"2018-03-06 15:15:26","post_date_gmt":"2018-03-06 15:15:26","post_content":"With growing raster dataset sizes, the processing times for analysis is becoming longer and longer. However, by employing concepts from parallel computing, we can improve performance and scalability of workflows with Spatial Analyst tools. In this blog, we will identify scenarios where the <a href=\"https:\/\/docs.python.org\/3.6\/library\/multiprocessing.html\">multiprocessing Python module<\/a> (Python.org) can be used to optimize performance of workflows by dividing up the work between multiple processes on a given machine, taking advantage of multiple CPU cores available on modern computer hardware. We will identify candidate raster operations that benefit most from parallel computation and learn how to develop efficient parallel systems for raster geoprocessing within the robust Python environment.<!--more-->\n\nCandidate raster operations for parallelization can be broadly classified into three geoprocessing scenarios. Parallelism is invoked using a different mechanism for each scenario.\n\u2022 <a href=\"#processing-large-raster\">Processing a large raster dataset<\/a>\u00a0with\u00a0a single analysis tool\n\u2022 <a href=\"#batch-processing-collection\">Batch processing a collection of raster datasets<\/a>\u00a0by\u00a0running a single analysis tool multiple times\n\u2022\u00a0<a href=\"#processing-a-workflow\">Raster geoprocessing workflow<\/a>\u00a0by\u00a0running suitable analysis tools chained together\n\nFrom our in-house testing, we evaluated the performance of a local operation executed parallelly on a large sample dataset. The graph below summarizes the performance improvements that were observed.\n\n<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_performance_graph.png\"><img class=\"size-large wp-image-101830\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_performance_graph-1024x507.png\" alt=\"Multiprocessing performance graph\" width=\"640\" height=\"316\" \/><\/a>\n\nThe advantages of optimizing raster analysis tasks with Python multiprocessing are evident from this graph. We can see significant improvements while adding more processes to help with the analysis, especially going from one to four processes. Beyond that, gains were negligible due to the overhead of spawning multiple processes. <a name=\"processing-large-raster\"><\/a>\n<h2>Processing a large raster dataset<\/h2>\nThe increase in resolution of raster datasets has led to larger and larger data sizes. Currently, datasets are on the order of gigabytes and increasing, with billions of raster cells. While computing power of the processors and size of the memory in computers have increased appreciably, legacy equipment and algorithms suited to manipulating small rasters with coarser resolution make processing these improved data sources costly. [1]\n\nData decomposition, also known as divide and conquer, is a popular strategy used in parallel computing that we will take advantage of to parallelly process a large raster dataset. The algorithms used in raster analysis tools can be broadly classified into four categories \u2013 local, focal, zonal and global operations. For a deeper dive into the types of cell-based raster operations, <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/help\/analysis\/spatial-analyst\/performing-analysis\/the-types-of-operations-in-spatial-analyst.htm\">read this article<\/a>. Local, focal and zonal raster operations are simple to program with when it comes to data decomposition. Once the data is decomposed appropriately, each data \u2018chunk\u2019 can be operated on independently on by a process without the need to communicate with other processes. However, global raster operations are tougher to integrate with data decomposition and require communication between processes. Let us look at an example of processing a large raster dataset using a local math raster operation, Square Root. The Local, or per-cell operations, are the simplest to parallelize using a \u2018divide and conquer\u2019 strategy, since the resulting value at each cell only depends on the input value at that cell location. For each cell, the <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/spatial-analyst\/square-root.htm\">Square Root<\/a> tool calculates the square root of the value from that cell. Using the tool serially would mean simply running the Square Root tool on the entire large dataset, but, this process can be time consuming.\n\n[caption id=\"attachment_101843\" align=\"alignnone\" width=\"640\"]<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_large_default3.png\"><img class=\"size-large wp-image-101843\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_large_default3-1024x318.png\" alt=\"Default, non-optimized sequential approach to processing a large raster\" width=\"640\" height=\"198\" \/><\/a> Default, non-optimized serial approach to processing a large raster[\/caption]\n\nInstead, through data decomposition, we can redesign the analysis task to utilize multiple worker processes simultaneously, thus improving the performance of the overall analysis. The graphic below depicts splitting the domain of a large raster into several smaller chunks, and using multiple worker processes to simultaneously perform analysis on each of the sub-sections. The results are then stitched back together for the final output.\n\n[caption id=\"attachment_101845\" align=\"alignnone\" width=\"640\"]<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_large_parallel.png\"><img class=\"size-large wp-image-101845\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_large_parallel-1024x414.png\" alt=\"Multiprocessing large rasters parallelly\" width=\"640\" height=\"258\" \/><\/a> Multiprocessing large rasters parallelly[\/caption]\n\nA sample script is <a href=\"https:\/\/github.com\/nRajasekar92\/DevSummit-2017\/blob\/master\/SampleScripts\/parallel_local.py\">shared on GitHub here<\/a> that goes in depth on how this problem can be solved programmatically using ArcGIS Desktop and the Python <em>multiprocessing<\/em> module. <a name=\"batch-processing-collection\"><\/a>\n<h2>Batch processing a collection of raster datasets<\/h2>\nWhen it comes to batch processing many datasets through a raster analysis tool, the common approach would be to use an iterator in ModelBuilder or write a Python script with a loop to iterate over each dataset in the batch and process them serially. This method can be time consuming when working with a large collection of rasters, particularly when running analysis tasks that have significant compute times.\n\n[caption id=\"attachment_101847\" align=\"alignnone\" width=\"640\"]<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_collection_default.png\"><img class=\"size-large wp-image-101847\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_collection_default-1024x495.png\" alt=\"Default sequential approach to processing a multiple rasters\" width=\"640\" height=\"309\" \/><\/a> Default sequential approach to processing a multiple rasters[\/caption]\n\nParallelism is a straightforward mechanism for batch processing when the analysis of each dataset in the batch can be performed independently from the rest. From the graphic below we can see how multiple processes working simultaneously enable faster processing of the batch queue.\n\n[caption id=\"attachment_101848\" align=\"alignnone\" width=\"640\"]<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_collection_parallel.png\"><img class=\"size-large wp-image-101848\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_collection_parallel-1024x484.png\" alt=\"Using multiprocessing to efficiently process multiple rasters in a parallel fashion\" width=\"640\" height=\"302\" \/><\/a> Using multiprocessing to efficiently process multiple rasters in a parallel fashion[\/caption]\n\nA sample script is <a href=\"https:\/\/github.com\/nRajasekar92\/DevSummit-2017\/blob\/master\/SampleScripts\/parallel_batch.py\">shared on GitHub here<\/a> that describes in detail how batch processing can be solved programmatically using ArcGIS Desktop and the Python multiprocessing module. <a name=\"processing-a-workflow\"><\/a>\n<h2>Raster geoprocessing workflows<\/h2>\nA geoprocessing workflow is a multiple-step procedure that combines geoprocessing tools and geographic data to produce a meaningful result. Within ArcGIS, raster geoprocessing workflows can be written as models using <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/help\/analysis\/geoprocessing\/modelbuilder\/what-is-modelbuilder-.htm\">ModelBuilder<\/a> or as <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/help\/analysis\/geoprocessing\/basics\/python-and-geoprocessing.htm\">Python scripts<\/a>. The standard approach in either cases is to chain together the tools needed to perform your analysis and execute them serially. Let us look at an example workflow that assesses a suitability raster by running the Slope, Aspect, Reclassify and Weighted Sum tools. These tools are available as part of the <a href=\"http:\/\/pro.arcgis.com\/en\/pro-app\/help\/analysis\/spatial-analyst\/basics\/what-is-the-spatial-analyst-extension.htm\">Spatial Analyst Extension in ArcGIS Desktop<\/a>.\n\n[caption id=\"attachment_101852\" align=\"alignnone\" width=\"640\"]<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_workflow_default.png\"><img class=\"size-large wp-image-101852\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_workflow_default-1024x441.png\" alt=\"Default approach to performing a workflow\" width=\"640\" height=\"275\" \/><\/a> Default approach to performing a workflow[\/caption]\n\nIn the above example, as tools are executed in a serial fashion, only one worker process can be utilized at a time to complete the workflow. To parallelize any geoprocessing workflow, the first task is to decompose the problem and identify parts of it that may be handled independently. This workflow is a good candidate for parallel processing as the execution of Slope, Aspect and Reclassify tools are independent tasks. These tasks have no dependencies on the execution of other tools in the workflow. However, the Weighted Sum tool is a dependent task, having dependencies on the outputs from the Slope, Aspect and Reclassify tools. Until the Slope, Aspect and Reclassify tools have finished executing, the Weighted Sum tool cannot begin its analysis. Having identified the independent and dependent tasks within this workflow, we can redesign this problem to parallelize it.\n\n[caption id=\"attachment_101857\" align=\"alignnone\" width=\"640\"]<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_workflow_parallel1.png\"><img class=\"size-large wp-image-101857\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/04\/multiproc_workflow_parallel1-1024x473.png\" alt=\"Incorporating parallel processing into model workflows\" width=\"640\" height=\"295\" \/><\/a> Incorporating parallel processing into model workflows[\/caption]\n\nIn this redesigned workflow, we are making use of multiple worker processes to execute independent tasks simultaneously, rather than depend on a single worker to process each task serially.\n<h2>Best practices and considerations<\/h2>\nThere are many factors to consider while combining the capabilities of the arcpy and multiprocessing modules to improve performance of raster geoprocessing tasks.\n\n\u2022 There are always overheads associated with spawning multiple processes that must be accounted for when considering a parallelized approach. In situations where the processing task is complex, and the processing data is large or the batch size is huge, parallelism can provide performance benefits that outweigh the overhead. In other situations, such as when the processing task is very simple, or if the processing datasets were not particularly large, it may be that utilizing multiprocessing does not provide any significant performance benefit and, in some cases, slows down overall processing speed. As parallelizing your code can be tedious, you should consider the end goal and opportunity cost of investing time in parallelization. For a task that is run repetitively, say on a scheduled basis, and in big data processing, parallelization can be advantageous.\n\n\u2022 Avoid writing output rasters from multiple processes to a common File Geodatabase (FGDB) or to multiple Esri Grid rasters within a common folder workspace. These output formats often experience schema locks or synchronization issues when accessed by multiple simultaneous processes.\n\n\u2022 You are encouraged to use ArcGIS Pro, ArcGIS Server or ArcMap with ArcGIS for Desktop Background Geoprocessing (64-bit) for parallelized raster analysis. Using 64-bit processing to perform analysis on systems with large amounts of RAM may help with processing large data efficiently.\n<h2>References<\/h2>\n[1] Barnes, Lehman, Mulla. \u201cPriority-Flood: An Optimal Depression-Filling and Watershed-Labeling Algorithm for Digital Elevation Models\u201d. Computers &amp; Geosciences. Vol 62, Jan 2014, pp 117-127, doi: \u201d10.1016\/j.cageo.2013.04.024\u201d.","post_title":"Multiprocessing with ArcGIS - Raster Analysis","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"multiprocessing-with-arcgis-raster-analysis","to_ping":"","pinged":"","post_modified":"2018-03-28 21:49:45","post_modified_gmt":"2018-03-28 21:49:45","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/multiprocessing-with-arcgis-raster-analysis\/","menu_order":0,"post_type":"blog","post_mime_type":"","comment_count":"0","filter":"raw"}],"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/07\/zs_card.png","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2018\/07\/wide5.jpg"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ 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