{"id":2944298,"date":"2025-11-14T08:33:32","date_gmt":"2025-11-14T16:33:32","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2944298"},"modified":"2025-11-14T08:34:50","modified_gmt":"2025-11-14T16:34:50","slug":"small-changes-big-impact-generate-spatial-weights-matrix","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/announcements\/small-changes-big-impact-generate-spatial-weights-matrix","title":{"rendered":"Small Changes, Big Impact: Generate Spatial Weights Matrix"},"author":354322,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[37101],"tags":[24311,25581],"industry":[],"product":[36561],"class_list":["post-2944298","blog","type-blog","status-publish","format-standard","hentry","category-announcements","tag-analysis","tag-spatial-statistics","product-arcgis-pro"],"acf":{"authors":[{"ID":354322,"user_firstname":"Josiah","user_lastname":"Parry","nickname":"Josiah Parry","user_nicename":"jparry","display_name":"Josiah Parry","user_email":"jparry@esri.com","user_url":"https:\/\/josiahparry.com","user_registered":"2024-03-06 19:22:54","user_description":"Josiah Parry is a Senior Product Engineer in Spatial Analytics and Data Science and helps lead the R-ArcGIS Bridge project.","user_avatar":""}],"short_description":"Spatial Statistics tools just got a whole lot faster and more customizable.","flexible_content":[{"acf_fc_layout":"content","content":"

Underpinning many of the tools in the Spatial Statistics<\/strong> toolbox<\/a> are Spatial Weights Matrices<\/strong>\u2014we call them SWM<\/strong>s for short. They\u2019re used everywhere from Hotspot Analysis<\/a>, Cluster and Outlier Analysis<\/a>, Build Balanced Zones<\/a>, Spatial Autoregression<\/a> and much more.<\/p>\n

A SWM is how we quantify Tobler\u2019s First Law of Geography<\/strong><\/a> which says:<\/p>\n

\u201ceverything is related to everything else, but near things are more related to distance things.\u201d<\/em><\/p><\/blockquote>\n

A SWM determines which features are considered neighbors and just how much \u201cinfluence\u201d they exert on each other.<\/p>\n

ArcGIS Pro 3.6 brings an entirely revamped Generate Spatial Weights Matrix<\/strong> <\/a>tool. While you may not have ever used the tool directly, some of the changes in this release will be felt everywhere in the Spatial Statistics toolbox.<\/p>\n

Enhancements at a glance:<\/strong><\/h3>\n
    \n
  • Contiguity algorithm is upwards of 200x faster<\/strong> than ArcGIS Pro 3.5 and prior<\/li>\n
  • Customizable spatial weights matrices<\/li>\n
  • We\u2019ve introduced a Higher Order Contiguity<\/strong> neighborhood type.<\/li>\n
  • Weight neighbors based on Length of Shared Border<\/strong><\/li>\n
  • Model distance decay with kernel-based weights<\/li>\n<\/ul>\n

    Contiguity just got a lot faster<\/strong><\/h2>\n

    We rebuilt the contiguity algorithm from the ground up. Working with the core geoprocessing team, they\u2019ve release a new tool Pairwise Polygon Neighbors<\/strong> which now powers our contiguity algorithm.<\/p>\n

    The performance gains are substantial. Running contiguity on detailed US counties used to take about 4 minutes. It now finishes in roughly 10 seconds<\/strong>! 240 times faster for that dataset. Your mileage will vary depending on your data, but the improvement should be noticeable whenever you use contiguity in Hot Spot Analysis, Cluster and Outlier Analysis, or any other Spatial Statistics tool. The speed increase will be most noticeable for complex polygons with many vertices.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2945744,"id":2945744,"title":"gswm-us-counties","filename":"gswm-us-counties.png","filesize":1080271,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/announcements\/small-changes-big-impact-generate-spatial-weights-matrix\/gswm-us-counties","alt":"A screenshot of a map of the contiguous detailed US Counties. On the right hand side is the geoprocessing messages of the Generate Spatial Weights Matrix tool with an elapsed time of 9.74 seconds.","author":"354322","description":"","caption":"Contiguity for detailed US counties completed in 9.74 seconds.","name":"gswm-us-counties","status":"inherit","uploaded_to":2944298,"date":"2025-10-27 23:33:41","modified":"2025-10-28 00:00:43","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":1919,"height":1138,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties.png","medium-width":440,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties.png","medium_large-width":768,"medium_large-height":455,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties.png","large-width":1821,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties-1536x911.png","1536x1536-width":1536,"1536x1536-height":911,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties.png","2048x2048-width":1919,"2048x2048-height":1138,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties-784x465.png","card_image-width":784,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-us-counties-1821x1080.png","wide_image-width":1821,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

    Customizable Spatial Weights Matrices<\/strong><\/h2>\n

    A spatial weights matrix is made by making two decisions which can be paraphrased with the following questions:<\/p>\n

     <\/p>\n

      \n
    1. How do we find our neighbors in physical space?<\/li>\n
    2. How much do they influence each other?<\/li>\n<\/ol>\n

       <\/p>\n

      In ArcGIS Pro 3.4 we shipped the Neighborhood Explorer<\/strong> experience, which gives users the ability to create new SWMs by specifying the neighborhood type and weighting scheme separately. We’ve embraced the customizability that this provides in this release of Generate Spatial Weights Matrix.<\/p>\n

       <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2945743,"id":2945743,"title":"Neighborhood Type","filename":"gswm-nb.png","filesize":12847,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/announcements\/small-changes-big-impact-generate-spatial-weights-matrix\/gswm-nb","alt":"A screenshot of the Neighborhood Type parameter of the Generate Spatial Weights matrix tool.","author":"354322","description":"The neighborhood type options in the Generate Spatial Weights Matrix.","caption":"Neighborhoods can be defined many different ways.","name":"gswm-nb","status":"inherit","uploaded_to":2944298,"date":"2025-10-27 23:33:36","modified":"2025-10-27 23:57:58","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":511,"height":299,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","medium-width":446,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","medium_large-width":511,"medium_large-height":299,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","large-width":511,"large-height":299,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","1536x1536-width":511,"1536x1536-height":299,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","2048x2048-width":511,"2048x2048-height":299,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","card_image-width":511,"card_image-height":299,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-nb.png","wide_image-width":511,"wide_image-height":299}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

      Higher Order Contiguity<\/strong><\/h3>\n

      When using polygon data, the most intuitive way to identify neighbors is by identifying adjacent polygons. Polygons that touch each other are considered to be contiguous.<\/strong><\/p>\n

       <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2945742,"id":2945742,"title":"gswm-guerry-1png","filename":"gswm-guerry-1png.png","filesize":884703,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/announcements\/small-changes-big-impact-generate-spatial-weights-matrix\/gswm-guerry-1png","alt":"France's districts from the 1830's Guerry data set. A district in the center is selected and the neighboring features are outlined in black with lines connecting to them.","author":"354322","description":"First order contiguity","caption":"First order contiguity.","name":"gswm-guerry-1png","status":"inherit","uploaded_to":2944298,"date":"2025-10-27 23:33:22","modified":"2025-10-28 00:01:15","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":1918,"height":1093,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png.png","medium-width":458,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png.png","medium_large-width":768,"medium_large-height":438,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png.png","large-width":1895,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png-1536x875.png","1536x1536-width":1536,"1536x1536-height":875,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png.png","2048x2048-width":1918,"2048x2048-height":1093,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png-816x465.png","card_image-width":816,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-1png-1895x1080.png","wide_image-width":1895,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

      Often, though, the immediate neighbors don\u2019t always capture the full extent of spatial relationships. In some use cases, such as epidemiology, neighborhoods should include the neighbors of a feature\u2019s immediate neighbors. This is what higher order contiguity is used for.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2945753,"id":2945753,"title":"Second order contiguity","filename":"gswm-guerry-2.png","filesize":915232,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/announcements\/small-changes-big-impact-generate-spatial-weights-matrix\/gswm-guerry-2","alt":"France's districts from the 1830's Guerry data set. A district in the center is selected and the neighboring features are outlined in black with lines connecting to them.","author":"354322","description":"Second order contiguity as displayed in neighborhood explorer.","caption":"Second order contiguity.","name":"gswm-guerry-2","status":"inherit","uploaded_to":2944298,"date":"2025-10-27 23:36:31","modified":"2025-10-28 00:01:23","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":1918,"height":1103,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2.png","medium-width":454,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2.png","medium_large-width":768,"medium_large-height":442,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2.png","large-width":1878,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2-1536x883.png","1536x1536-width":1536,"1536x1536-height":883,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2.png","2048x2048-width":1918,"2048x2048-height":1103,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2-809x465.png","card_image-width":809,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-guerry-2-1878x1080.png","wide_image-width":1878,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

       <\/p>\n

      The order indicates the degree of neighbors. First order is the immediate neighbors. Then second order would include the neighbors of those neighbors and so on.<\/p>\n

       <\/p>\n

      Kernel Weights<\/strong><\/h3>\n

      We’ve added kernel functions for calculating weights: Gaussian, Epanechnikov, Quartic, and Triangular. Each kernel creates a different distance-decay relationship. Gaussian weights decay smoothly along a bell curve. Triangular weights have constant, steep decay. Epanechnikov starts slow and drops off sharply. The kernel you choose affects how much influence distant neighbors have on your focal feature, which changes your results.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2945745,"id":2945745,"title":"gswm-weighting","filename":"gswm-weighting.png","filesize":12529,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/announcements\/small-changes-big-impact-generate-spatial-weights-matrix\/gswm-weighting","alt":"Weighting Method parameter dropdown from the Generate Spatial Weights matrix tool.","author":"354322","description":"","caption":"","name":"gswm-weighting","status":"inherit","uploaded_to":2944298,"date":"2025-10-27 23:33:52","modified":"2025-10-27 23:57:10","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":512,"height":271,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","medium-width":464,"medium-height":246,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","medium_large-width":512,"medium_large-height":271,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","large-width":512,"large-height":271,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","1536x1536-width":512,"1536x1536-height":271,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","2048x2048-width":512,"2048x2048-height":271,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","card_image-width":512,"card_image-height":271,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/gswm-weighting.png","wide_image-width":512,"wide_image-height":271}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

      Our newer tools such as Bivariate Spatial Association enable you to choose a kernel weight. However, with this new enhancement you can create a SWM and provide it to any tool that may not have kernel weighting options.<\/p>\n

      Get Started<\/strong><\/h2>\n

      The enhancements to Generate Spatial Weights Matrix give you full control over how spatial relationships are defined in your analysis. Build custom SWMs with neighborhood types and weighting schemes that aren’t available in other tools, then reuse that same SWM across the entire Spatial Statistics toolbox. Whether you’re running Hot Spot Analysis, Cluster and Outlier Analysis, or any other spatial statistics tool, you can now ensure they’re all using the exact spatial relationship structure your analysis requires.<\/p>\n"}],"related_articles":"","show_article_image":true,"card_image":false,"wide_image":false},"yoast_head":"\nSmall Changes, Big Impact: Generate Spatial Weights Matrix<\/title>\n<meta name=\"description\" content=\"Spatial statistics just got a whole lot faster. 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