{"id":504992,"date":"2019-05-06T09:42:09","date_gmt":"2019-05-06T16:42:09","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=504992"},"modified":"2019-05-06T10:19:50","modified_gmt":"2019-05-06T17:19:50","slug":"introducing-the-new-resolution-preserving-cell-size-projection-method","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/introducing-the-new-resolution-preserving-cell-size-projection-method","title":{"rendered":"Introducing the new resolution preserving cell size projection method"},"author":7091,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[386202,386222,386212,29991,386192],"industry":[],"product":[36561,37031,36991],"class_list":["post-504992","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-cell-size","tag-cell-size-projection-method","tag-proejction","tag-raster-analysis","tag-resolution-preserving-cell-size","product-arcgis-pro","product-spatial-analyst","product-arcgis-desktop"],"acf":{"short_description":"ArcGIS Pro 2.3 and ArcMap 10.7 Spatial Analyst tools now support a new environment, the Cell Size Projection Method, to control the calculation o","flexible_content":[{"acf_fc_layout":"content","content":"<p>ArcGIS Pro 2.3 and ArcMap 10.7 Spatial Analyst tools now support a new environment, the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/environment-settings\/cell-size-projection-method.htm\">Cell Size Projection Method<\/a>, to control the calculation of the output raster cell size when datasets are projected during tool execution. To learn more about projections, see <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/help\/mapping\/properties\/coordinate-systems-and-projections.htm\">Coordinate systems, projections, and transformations.<\/a><\/p>\n<p>In previous software versions, the cell size was converted using a linear unit conversion when projecting from a Projected Coordinate System (PCS) to another PCS or an angular unit conversion when going from Geographic Coordinate System (GCS) to another GCS. So, projecting a 30 meter raster dataset from UTM zone 10N to WGS84 Web Mercator would continue to use 30 meter as the output cell size. When projecting from GCS to PCS or vice versa, previous versions took an average cell size based on the ratios of diagonal lengths in source and destination projections. Therefore, this method, currently exposed as the \u2018convert units\u2019 method simply copied the cell size from input to output, changing units if necessary.<\/p>\n<p>This approach has some limitations:<\/p>\n<ul>\n<li>It does not account for distortion when projecting from one PCS to another PCS (1 projected meter in, say, a UTM zone at a given location may not cover the same ground distance as 1 projected meter at the same location in an Albers projection).<\/li>\n<li>When computing the ratios of diagonal lengths, the behavior of the projection at only the four corner points was used. This may have introduced excessive distortion, depending on the projection and the extent.<\/li>\n<\/ul>\n<p>The two new methods: \u2018preserve resolution\u2019 and \u2018center of extent\u2019 both are intended to calculate a projected cell size that doesn\u2019t change the ground distance of the cell, thus avoiding unnecessary resampling during an implicit projection operation happening as a Spatial Analyst tool or a feature to raster conversion tool executes. You should use the \u2018center of extent\u2019 method when you know the geographic location where your cell size is most accurate. If you use this method, you can use a raster with a small extent in that same area as the cell size source or create a temp raster \u2018around\u2019 that location and use that as the cell size source. If you are unsure, use the \u2018preserve resolution\u2019 method. To learn more about the three cell size projection methods see <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/environment-settings\/how-the-cell-size-projection-method-environment-setting-works.htm\">How the Cell Size Projection Method environment setting works<\/a>.<\/p>\n<p>If you do not know at which specific geographic location your cell size is best but want to improve on the \u2018convert units\u2019 method when a projection is involved while working with Spatial Analyst tools, you can use the \u2018preserve resolution\u2019 method.<\/p>\n<h2>Why use the resolution preserving cell size projection method?<\/h2>\n<p>In the \u2018preserve resolution\u2019 method the same number of square cells as are in the original extent are preserved in the projected extent. The output cell size is calculated based on the ratios of the areas of the projected extent to the original extent. This approach is based on how Esri software currently chooses a cell size when moving from a camera image coordinate system to a geodetic (GCS or PCS) system. This method calculates the average size of a square cell more accurately for all combinations of GCS and PCS than the default \u2018convert units\u2019 method.<\/p>\n<p>If the areas of the original rectangular extent and (shape preserving) projected extent are A<sub>0<\/sub> and A<sub>1<\/sub>, then the areas of the square cells, respectively, are, ca<sub>0<\/sub> = A<sub>0<\/sub>\/n and ca<sub>1 <\/sub>= A<sub>1<\/sub>\/n<\/p>\n<p>Since the number of cells remains constant for both cases, the ratio of the area of extent to the area of the square cell are equal, A<sub>0<\/sub>\/ca<sub>0<\/sub> = A<sub>1<\/sub>\/ca<sub>1<\/sub><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":505032,"id":505032,"title":"CSPM_Fig1","filename":"CSPM_Fig1.png","filesize":4650,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/introducing-the-new-resolution-preserving-cell-size-projection-method\/cspm_fig1","alt":"Figure 1: Cell size projection using the new \u2018preserve resolution\u2019 method","author":"7091","description":"Figure 1: Cell size projection using the new \u2018preserve resolution\u2019 method","caption":"Figure 1: Cell size projection using the new \u2018preserve resolution\u2019 method","name":"cspm_fig1","status":"inherit","uploaded_to":504992,"date":"2019-05-03 20:44:35","modified":"2019-05-03 20:50:26","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":560,"height":210,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","medium-width":464,"medium-height":174,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","medium_large-width":560,"medium_large-height":210,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","large-width":560,"large-height":210,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","1536x1536-width":560,"1536x1536-height":210,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","2048x2048-width":560,"2048x2048-height":210,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","card_image-width":560,"card_image-height":210,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig1.png","wide_image-width":560,"wide_image-height":210}},"image_position":"left-center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>So, the output cell area is ca<sub>1<\/sub> = (A<sub>1<\/sub>\/A<sub>0<\/sub>) * ca<sub>0<\/sub><\/p>\n<p>and the output cell size is CellSize_projected = \u221a((A<sub>1<\/sub>\/A<sub>0<\/sub>) * ca<sub>0<\/sub>)<\/p>\n<p>In this method, the cell size conversion factor is, \u221a(A<sub>1<\/sub>\/A<sub>0<\/sub>)<\/p>\n<p>Let\u2019s look at two examples to see how the \u2018preserve resolution\u2019 method picks a better output cell size than \u2018convert units\u2019.<\/p>\n<h3>Example 1:<\/h3>\n<p>Let\u2019s take an elevation raster (R_input), located in Vermont (Figure 2), in a PCS (NAD_1983_StatePlane_Vermont_FIPS_4400) and project it to another PCS (WGS 1984 World Mercator) using the default cell size projection methods, \u2018convert units\u2019 (R_out_CU) and \u2018preserve resolution\u2019 (R_out_PR). Here both the spatial references are shape preserving, where the input spatial reference is suitable at a state level and the output spatial reference is suitable for the entire world. We will then compare the geodesic distance between the cell centers to determine which method preserves the geodesic distance more accurately.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":505052,"id":505052,"title":"CSPM_Fig2","filename":"CSPM_Fig2.png","filesize":15064,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/introducing-the-new-resolution-preserving-cell-size-projection-method\/cspm_fig2","alt":"Figure 2: Location of the input raster (R_Input), its cell boundaries and cell centers.","author":"7091","description":"Figure 2: Location of the input raster (R_Input), its cell boundaries and cell centers.","caption":"Figure 2: Location of the input raster (R_Input), its cell boundaries and cell centers.","name":"cspm_fig2","status":"inherit","uploaded_to":504992,"date":"2019-05-03 20:46:44","modified":"2019-05-03 20:50:13","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":510,"height":353,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","medium-width":377,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","medium_large-width":510,"medium_large-height":353,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","large-width":510,"large-height":353,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","1536x1536-width":510,"1536x1536-height":353,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","2048x2048-width":510,"2048x2048-height":353,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","card_image-width":510,"card_image-height":353,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig2.png","wide_image-width":510,"wide_image-height":353}},"image_position":"left-center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>Before we compare the geodesic distances between the two methods, lets understand what are the different ways a raster is projected in ArcGIS Pro. When you add a raster to the map in ArcGIS Pro, the spatial reference of the map becomes the same as the spatial reference of the raster. For example, if the first layer added has a NAD_1983_StatePlane_Vermont_FIPS_4400 PCS, the map will have the same spatial reference, and all other layers will project on the fly to match this spatial reference. This on-the-fly raster projection is meant for a richer display experience but does not preserve the raster structure (different cells can have on-the-fly-projected different sizes, rotations, and distortions). However, when you project a raster using the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/data-management\/project-raster.htm\">Project Raster<\/a> tool or specifying the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/tool-reference\/environment-settings\/output-coordinate-system.htm\">Output Coordinate System<\/a> environment of a geoprocessing tool, the raster is actually \u00a0projected into a new raster structure (every cell is an identical rectangle in the output spatial reference, with sides parallel to the coordinate system axes). When measuring geodesic distance for the comparison of the cell size projection methods, it is recommended to use the actual projected raster instead of the raster that is projected on the fly.<\/p>\n<p>In figure 3, the cell size of the input raster, R_input is 30 meters. This is approximately the same as its geodesic ground distance, which you can find out using the <a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/help\/mapping\/navigation\/measure.htm\">Measure tool<\/a> in ArcGIS Pro.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":505082,"id":505082,"title":"CSPM_Fig3","filename":"CSPM_Fig3.png","filesize":11570,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/introducing-the-new-resolution-preserving-cell-size-projection-method\/cspm_fig3","alt":"Figure 3: Cell boundaries of R_input, cell centers of R_input, R_out_CU, R_out_PR and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input for comparison.","author":"7091","description":"Figure 3: Cell boundaries of R_input, cell centers of R_input, R_out_CU, R_out_PR and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input for comparison.","caption":"Figure 3: Cell boundaries of R_input, cell centers of R_input, R_out_CU, R_out_PR and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input for comparison.","name":"cspm_fig3","status":"inherit","uploaded_to":504992,"date":"2019-05-03 21:07:59","modified":"2019-05-03 21:08:09","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":636,"height":440,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","medium-width":377,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","medium_large-width":636,"medium_large-height":440,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","large-width":636,"large-height":440,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","1536x1536-width":636,"1536x1536-height":440,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","2048x2048-width":636,"2048x2048-height":440,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","card_image-width":636,"card_image-height":440,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig3.png","wide_image-width":636,"wide_image-height":440}},"image_position":"left-center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>When the raster is projected using the \u2018convert units\u2019 methods, the projected cell size of the output raster, R_out_CU remains 30 meters, but 30m in WGS 1984 World Mercator is a much smaller distance on the ground, 21 meters. So, raster projection using the \u2018convert units\u2019 method has unnecessarily increased the resolution of the output raster. If we were projecting in the opposite direction (starting from WGS 1984 World Mercator), then we would have lost a significant amount of raster data. When the same raster is projected using the \u2018preserve resolution\u2019 method, its cell size becomes 42 meters and the geodesic ground distance remains 30 meters, which is the same as the geodesic distance of the input raster.<\/p>\n<h3>Example 2:<\/h3>\n<p>In this example, we will project from a UTM zone to its adjacent zone, which can happen when mosaicking together many different DEMs for a larger (state sized) area. In figure 4, let\u2019s take a raster (R_input2) in WGS 1984 UTM Zone 11N and project it to its adjacent zone WGS 1984 UTM Zone 12N, creating output (R_out_CU2) and (R_out_PR2) for \u2018convert units\u2019 and \u2018preserve resolution\u2019 respectively. From here we will again compare the geodesic distance between the cell centers to determine which method preserve the geodesic distance more accurately.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":505092,"id":505092,"title":"CSPM_Fig4","filename":"CSPM_Fig4.png","filesize":11802,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/spatial-analyst\/analytics\/introducing-the-new-resolution-preserving-cell-size-projection-method\/cspm_fig4","alt":"Figure 4: Cell boundaries of R_input2, cell centers of R_input2, R_out_CU2, R_out_PR2 and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input2 for comparison.","author":"7091","description":"Figure 4: Cell boundaries of R_input2, cell centers of R_input2, R_out_CU2, R_out_PR2 and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input2 for comparison.","caption":"Figure 4: Cell boundaries of R_input2, cell centers of R_input2, R_out_CU2, R_out_PR2 and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input2 for comparison.","name":"cspm_fig4","status":"inherit","uploaded_to":504992,"date":"2019-05-03 21:08:35","modified":"2019-05-03 21:08:42","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":653,"height":440,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","medium-width":387,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","medium_large-width":653,"medium_large-height":440,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","large-width":653,"large-height":440,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","1536x1536-width":653,"1536x1536-height":440,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","2048x2048-width":653,"2048x2048-height":440,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","card_image-width":653,"card_image-height":440,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Fig4.png","wide_image-width":653,"wide_image-height":440}},"image_position":"left-center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p>The cell size for the input raster, R_input2 is 30 meters, which is approximately the same as the geodesic ground distance in this area. When the raster is projected using the \u2018convert units\u2019 method, the cell size of the output raster, R_out_CU2 becomes 30 meters and the geodesic ground distance becomes approximately 31 meters. When the same raster is projected using the \u2018preserve resolution\u2019 method, its cell size becomes 28.99 meters, and the geodesic ground distance is 30 meters, which is the same as the input raster.<\/p>\n<p>In both the examples, the \u2018preserve resolution\u2019 method seems to preserve the geodesic ground distance between cell centers better than the \u2018convert units\u2019 method. Other combinations of input and output spatial references may, of course, show difference between the methods, but overall it is safe to say that the \u2018preserve resolution\u2019 Method is a better approach for preserving geodesic distance while projecting datasets.<\/p>\n<p>An important property of the \u2018preserve resolution\u2019 method is that the output cell size depends on the location of the dataset. The same combination of input and output spatial reference will yield a different output cell size if the input raster dataset is at a different geographic location. If you need to use one cell size for different raster datasets (for example, processing adjacent DEM tiles), specify one raster dataset to use as the cell size source while performing raster analysis for the tiles.<\/p>\n<p>Next time you use a Spatial Analyst geoprocessing tool or python command to:<\/p>\n<ul>\n<li>create an output with a spatial reference different from that of the input dataset,<\/li>\n<li>use input datasets with different spatial references,<\/li>\n<li>specify an analysis cell size using a dataset with a different spatial reference,<\/li>\n<\/ul>\n<p>pay attention to the output cell size and consider using either the preserve resolution or center of extent methods. By default, the projection method will do the \u2018convert units\u2019, which existed in previous versions of ArcGIS, but as we have seen, this method may unnecessarily increase or decrease the resolution of your valuable raster data.<\/p>\n<h3>Additional resources<\/h3>\n<p><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/help\/analysis\/spatial-analyst\/performing-analysis\/handling-projections-during-analysis.htm\">Handling projections during analysis in Spatial Analyst<\/a><\/p>\n<p><a href=\"https:\/\/pro.arcgis.com\/en\/pro-app\/help\/analysis\/spatial-analyst\/performing-analysis\/how-the-analysis-window-is-determined.htm\">How the analysis window is determined in Spatial Analyst<\/a><\/p>\n"}],"authors":[{"ID":9492,"user_firstname":"James","user_lastname":"Tenbrink","nickname":"jt","user_nicename":"jtenbrink","display_name":"James TenBrink","user_email":"jtenbrink@esri.com","user_url":"","user_registered":"2019-04-25 18:00:10","user_description":"Jim has a Bachelor of Science degree in Computer Science from Hope College in Holland, Michigan, and a Master of Science degree in Computer Science from Rensselaer Polytechnic Institute in Troy, New York.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/04\/JimIcon-213x200.png' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"},{"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":79471,"post_author":"6771","post_date":"2017-09-18 13:42:02","post_date_gmt":"2017-09-18 13:42:02","post_content":"","post_title":"Learn spatial analysis techniques with scenario-based case studies","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"learn-spatial-analysis-techniques-with-scenario-based-case-studies","to_ping":"","pinged":"","post_modified":"2021-08-02 23:38:51","post_modified_gmt":"2021-08-03 06:38:51","post_content_filtered":"","post_parent":0,"guid":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/learn-spatial-analysis-techniques-with-scenario-based-case-studies\/","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\/2019\/05\/CSPM_thumbnail.jpg","wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/05\/CSPM_Banner001.jpg"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - 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