{"id":2881002,"date":"2025-07-07T12:39:14","date_gmt":"2025-07-07T19:39:14","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&#038;p=2881002"},"modified":"2025-07-09T06:41:32","modified_gmt":"2025-07-09T13:41:32","slug":"analyze-variable-correlation-in-arcgis-business-analyst-web-app","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app","title":{"rendered":"Analyze variable correlation to find suitable areas for new affordable housing using ArcGIS Business Analyst Web App"},"author":321952,"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":[],"industry":[],"product":[36711],"class_list":["post-2881002","blog","type-blog","status-publish","format-standard","hentry","category-analytics","product-bus-analyst"],"acf":{"authors":[{"ID":321952,"user_firstname":"Sarah","user_lastname":"David","nickname":"Sarah David","user_nicename":"sdavid","display_name":"S David","user_email":"sdavid@esri.com","user_url":"","user_registered":"2022-11-08 21:39:20","user_description":"S David writes about ArcGIS Business Analyst.","user_avatar":"<img alt='' src='https:\/\/secure.gravatar.com\/avatar\/309366fd0364e37bfa30f7fe0a0bc5f3f3b2a3c42c1c7f873853e4962506a9e0?s=96&#038;d=blank&#038;r=g' srcset='https:\/\/secure.gravatar.com\/avatar\/309366fd0364e37bfa30f7fe0a0bc5f3f3b2a3c42c1c7f873853e4962506a9e0?s=192&#038;d=blank&#038;r=g 2x' class='avatar avatar-96 photo' height='96' width='96' loading='lazy' decoding='async'\/>"}],"short_description":"In the June 2025 release, Business Analyst Web App has a new capability in the suitability analysis workflow: a variable correlation matrix.","flexible_content":[{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW150967396 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW150967396 BCX0\">As part of the June 2025 release, ArcGIS Business Analyst Web App has a<\/span><span class=\"NormalTextRun SCXW150967396 BCX0\"> new<\/span> <span class=\"NormalTextRun SCXW150967396 BCX0\">capability in the <\/span><\/span><a class=\"Hyperlink SCXW150967396 BCX0\" href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/suitability-analysis.htm\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW150967396 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW150967396 BCX0\" data-ccp-charstyle=\"Hyperlink\">suitability analysis<\/span><\/span><\/a><span class=\"TextRun SCXW150967396 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW150967396 BCX0\"> workflow: <\/span><span class=\"NormalTextRun SCXW150967396 BCX0\">a <\/span><span class=\"NormalTextRun SCXW150967396 BCX0\">variable correlation<\/span><span class=\"NormalTextRun SCXW150967396 BCX0\"> matrix<\/span><span class=\"NormalTextRun SCXW150967396 BCX0\">.<\/span><\/span><span class=\"EOP SCXW150967396 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The suitability analysis workflow uses variables as criteria to score sites. To validate the variable selection in the analysis, use the correlation matrix. The correlation matrix provides a visualization of how variables correlate to one another and the final score. <\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><span class=\"TextRun SCXW231876194 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun CommentStart SCXW231876194 BCX0\">Using the correlation matrix, we can <\/span><span class=\"NormalTextRun SCXW231876194 BCX0\">identify<\/span><span class=\"NormalTextRun SCXW231876194 BCX0\"> variables that measure similar concepts, <\/span><span class=\"NormalTextRun SCXW231876194 BCX0\">indicated<\/span><span class=\"NormalTextRun SCXW231876194 BCX0\"> by a high correlation coefficient. Including such variables may unintentionally give certain factors more weight in the analysis. To improve the reliability of the suitability index, you can adjust the variable selection or reassign weights accordingly.<\/span><\/span><span class=\"EOP SCXW231876194 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/span><\/p>\n<p><span data-contrast=\"auto\">To access the correlation matrix, perform a suitability analysis and click the new <\/span><b><span data-contrast=\"auto\">Correlation matrix <\/span><\/b><span data-contrast=\"auto\">tab on the <\/span><b><span data-contrast=\"auto\">Results <\/span><\/b><span data-contrast=\"auto\">pane<\/span><span data-contrast=\"auto\">.\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2881952,"id":2881952,"title":"2025-07-07_16-19-29","filename":"2025-07-07_16-19-29.gif","filesize":212514,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-07-07_16-19-29","alt":"Use the correlation matrix.","author":"321952","description":"","caption":"","name":"2025-07-07_16-19-29","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 20:19:36","modified":"2025-07-07 20:19:51","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1280,"height":720,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","medium_large-width":768,"medium_large-height":432,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","large-width":1280,"large-height":720,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","1536x1536-width":1280,"1536x1536-height":720,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","2048x2048-width":1280,"2048x2048-height":720,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29-826x465.gif","card_image-width":826,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-19-29.gif","wide_image-width":1280,"wide_image-height":720}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">In this blog article, we\u2019ll\u00a0use\u00a0suitability analysis to rank areas in Tulsa, Oklahoma, that are in need of affordable housing. A lack of affordable housing creates financial insecurity for residents and limits a community\u2019s ability to thrive. <\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">To ensure that the analysis does not have skewed results, we will use the new correlation matrix capability to assess potential multicollinearity by identifying pairs of highly correlated variables. <span class=\"NormalTextRun SCXW101818594 BCX0\">Multicollinearity occurs when variables are highly correlated and may be redundant, meaning they capture similar information, which can affect the reliability of the results<\/span><span class=\"NormalTextRun SCXW101818594 BCX0\">.<\/span> After using the correlation matrix, we can make any necessary changes to the variable selection to create a stronger analysis. This supports more informed decision-making about where to allocate resources for housing affordability.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\n<p><span class=\"TextRun SCXW178978495 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW178978495 BCX0\">To start the workflow, click <\/span><\/span><strong><span class=\"TextRun SCXW178978495 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW178978495 BCX0\">Run analysis <\/span><\/span><\/strong><span class=\"TextRun SCXW178978495 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW178978495 BCX0\">and choose<\/span><\/span><strong><span class=\"TextRun SCXW178978495 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW178978495 BCX0\"> Suitability analysis<\/span><\/span><\/strong><span class=\"TextRun SCXW178978495 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW178978495 BCX0\">.\u00a0<\/span><\/span><span class=\"EOP SCXW178978495 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2881102,"id":2881102,"title":"2025-06-09_16-21-06","filename":"2025-06-09_16-21-06-1.png","filesize":41283,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-06-09_16-21-06-2","alt":"Run the suitability analysis workflow.","author":"321952","description":"","caption":"","name":"2025-06-09_16-21-06-2","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 18:07:38","modified":"2025-07-07 18:07:45","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":532,"height":316,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","medium-width":439,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","medium_large-width":532,"medium_large-height":316,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","large-width":532,"large-height":316,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","1536x1536-width":532,"1536x1536-height":316,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","2048x2048-width":532,"2048x2048-height":316,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","card_image-width":532,"card_image-height":316,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-21-06-1.png","wide_image-width":532,"wide_image-height":316}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h3><span data-contrast=\"none\">Set the area of interest<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"none\">For this analysis, let\u2019s set the area of interest to Tulsa, Oklahoma. <\/span><span data-contrast=\"auto\">Tulsa is currently experiencing a housing affordability crisis, with the city of Tulsa creating an <\/span><a href=\"https:\/\/www.cityoftulsa.org\/mayor\/housing\/\"><span data-contrast=\"none\">Executive Order<\/span><\/a><span data-contrast=\"auto\"> to\u00a0address\u00a0the lack of 6,000 affordable housing units for residents.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"sidebar","content":"<p><span class=\"TextRun SCXW107329917 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW107329917 BCX0\">If <\/span><span class=\"NormalTextRun SCXW107329917 BCX0\">you\u2019re<\/span><span class=\"NormalTextRun SCXW107329917 BCX0\"> familiar with mapping and analysis workflows in Business Analyst Web App, you may be used to starting workflows by setting the analysis extent. In the June 2025 release, the analysis extent <\/span><span class=\"NormalTextRun SCXW107329917 BCX0\">field <\/span><span class=\"NormalTextRun SCXW107329917 BCX0\">has been renamed the area of interest.<\/span><\/span><span class=\"EOP SCXW107329917 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\">For the location type, select the <\/span><\/span><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><strong><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW74936774 BCX0\">Geographies<\/span><\/strong><span class=\"NormalTextRun SCXW74936774 BCX0\"><strong> and hexagons<\/strong> <\/span><\/span><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\">option<\/span><span class=\"NormalTextRun SCXW74936774 BCX0\">. <\/span><span class=\"NormalTextRun SCXW74936774 BCX0\">For the <\/span><\/span><strong><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\">Area of interest<\/span><\/span><\/strong><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\">, enter <\/span><\/span><span style=\"text-decoration: underline\"><span class=\"TextRun Underlined SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\">Tulsa<\/span><\/span><\/span><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\"> and select <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW74936774 BCX0\">the <\/span><\/span><strong><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW74936774 BCX0\">Tulsa<\/span><span class=\"NormalTextRun SCXW74936774 BCX0\"> City, OK <\/span><\/span><\/strong><span class=\"TextRun SCXW74936774 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW74936774 BCX0\">result.<\/span><span class=\"NormalTextRun SCXW74936774 BCX0\"> Block groups are the default level of detail.<\/span><\/span><span class=\"EOP SCXW74936774 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2881112,"id":2881112,"title":"2025-06-09_16-22-29","filename":"2025-06-09_16-22-29.png","filesize":31839,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-06-09_16-22-29","alt":"Set the area of interest.","author":"321952","description":"","caption":"","name":"2025-06-09_16-22-29","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 18:07:52","modified":"2025-07-07 18:07:58","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":383,"height":393,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","medium-width":254,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","medium_large-width":383,"medium_large-height":393,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","large-width":383,"large-height":393,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","1536x1536-width":383,"1536x1536-height":393,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","2048x2048-width":383,"2048x2048-height":393,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","card_image-width":383,"card_image-height":393,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-09_16-22-29.png","wide_image-width":383,"wide_image-height":393}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<h3><span data-contrast=\"none\">Select criteria<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\">The next step in the workflow is to select criteria. There are featured lists that display a set of curated variable lists, including variable lists by industry, or we can pick variables from the <\/span><a href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/data-browser.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">data browser<\/span><\/a><span data-contrast=\"auto\">.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Click <\/span><b><span data-contrast=\"auto\">Select criteria <\/span><\/b><span data-contrast=\"auto\">to browse the data browser for variables related to housing affordability. Add the following variables to the analysis:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<table data-tablestyle=\"MsoTableGrid\" data-tablelook=\"1696\">\n<tbody>\n<tr>\n<td data-celllook=\"65536\"><b><span data-contrast=\"auto\">Variable<\/span><\/b><span data-ccp-props=\"{&quot;335551550&quot;:2,&quot;335551620&quot;:2}\">\u00a0<\/span><\/td>\n<td data-celllook=\"65536\"><b><span data-contrast=\"auto\">Dataset<\/span><\/b><span data-ccp-props=\"{&quot;335551550&quot;:2,&quot;335551620&quot;:2}\">\u00a0<\/span><\/td>\n<td data-celllook=\"65536\"><a href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/data-browser.htm#ESRI_SECTION1_804AD34E420848AF9502A67F75D6D952\" target=\"_blank\" rel=\"noopener\"><b><span data-contrast=\"none\">Calculation type<\/span><\/b><\/a><span data-ccp-props=\"{&quot;335551550&quot;:2,&quot;335551620&quot;:2}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">2025 Housing Affordability Index<\/span><span data-ccp-props=\"{&quot;335559685&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/updated-demographics.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Esri Updated Demographics<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">Index<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">2023 Households w\/ Mortgage: Monthly Owner Cost 50+% of Income<\/span><span data-ccp-props=\"{&quot;335559685&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/acs.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">ACS 5-Year<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">Percentage<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">2023 Households with Gross Rent 50+% of Income<\/span><span data-ccp-props=\"{&quot;335559685&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/acs.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">ACS 5-Year<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">Percentage<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">2023 Households Below the Poverty Level<\/span><span data-ccp-props=\"{&quot;335559685&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/acs.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">ACS 5-Year<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">Percentage<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">2025 Average Home Value<\/span><span data-ccp-props=\"{&quot;335559685&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/updated-demographics.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Esri Updated Demographics<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">Average<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">2030 Average Home Value<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><a href=\"https:\/\/doc.arcgis.com\/en\/esri-demographics\/latest\/esri-demographics\/updated-demographics.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Esri Updated Demographics<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<td data-celllook=\"0\"><span data-contrast=\"auto\">Average<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{}\">\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n"},{"acf_fc_layout":"sidebar","content":"<p><span class=\"TextRun SCXW248489912 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW248489912 BCX0\">This variable list is inspired by the <\/span><\/span><a class=\"Hyperlink SCXW248489912 BCX0\" href=\"https:\/\/learn.arcgis.com\/en\/projects\/find-suitable-areas-for-new-affordable-housing\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW248489912 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW248489912 BCX0\" data-ccp-charstyle=\"Hyperlink\">Find suitable areas for new affordable housing<\/span><\/span><\/a><span class=\"TextRun SCXW248489912 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"> <span class=\"NormalTextRun SCXW248489912 BCX0\">tutorial.<\/span><\/span><span class=\"EOP SCXW248489912 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW99858538 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW99858538 BCX0\"><span class=\"TextRun SCXW250479417 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW250479417 BCX0\">The two variables <\/span><\/span><strong><span class=\"TextRun SCXW250479417 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW250479417 BCX0\">2025 Average Home Value <\/span><\/span><\/strong><span class=\"TextRun SCXW250479417 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW250479417 BCX0\">and <\/span><\/span><strong><span class=\"TextRun SCXW250479417 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW250479417 BCX0\">2030 Average Home Value <\/span><\/span><\/strong><span class=\"TextRun SCXW250479417 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW250479417 BCX0\">may be redundant. <\/span><span class=\"NormalTextRun SCXW250479417 BCX0\">Typically,<\/span><span class=\"NormalTextRun SCXW250479417 BCX0\"> homes increase in value by 5-10% per year<\/span><span class=\"NormalTextRun SCXW250479417 BCX0\">; therefore, the<\/span><span class=\"NormalTextRun SCXW250479417 BCX0\"> 2025 home value compared to the projected 2030 home value will <\/span><span class=\"NormalTextRun SCXW250479417 BCX0\">likely be<\/span> <span class=\"NormalTextRun SCXW250479417 BCX0\">similar<\/span><span class=\"NormalTextRun SCXW250479417 BCX0\">. It will be important to check the correlation matrix to see if these variables are highly correlated so that we can remove one and a<\/span><span class=\"NormalTextRun SCXW250479417 BCX0\">void using skewed results.<\/span><\/span><span class=\"EOP SCXW250479417 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/span><\/span><\/p>\n<h3><span data-contrast=\"none\">Explore the results<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\"><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">The map and the<\/span><\/span><strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\"> Results<\/span><\/span><\/strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\"> pane automatically update.<\/span> <span class=\"NormalTextRun SCXW20186743 BCX0\">Before we explore the results, we need to adjust one of the variables. <\/span><span class=\"NormalTextRun SCXW20186743 BCX0\">In the <\/span><\/span><strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">Suitability analysis <\/span><\/span><\/strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">pane, in the <\/span><\/span><strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">Variable <\/span><\/span><\/strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">section, change the <\/span><\/span><strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">2025 Housing Affordability Index<\/span><\/span><\/strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\"><strong>\u00a0<\/strong>variable to use <\/span><\/span><strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">Inverse <\/span><\/span><\/strong><span class=\"TextRun SCXW20186743 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW20186743 BCX0\">influence. Inverse influence means that the lower the value of the variable, the greater its effect on the score. <\/span><\/span><span class=\"EOP SCXW20186743 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2881972,"id":2881972,"title":"2025-07-07_16-14-35","filename":"2025-07-07_16-14-35.png","filesize":16167,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-07-07_16-14-35","alt":"Use inverse influence for the housing affordability index variable.","author":"321952","description":"","caption":"","name":"2025-07-07_16-14-35","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 20:20:57","modified":"2025-07-07 20:21:03","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":385,"height":273,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","medium-width":368,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","medium_large-width":385,"medium_large-height":273,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","large-width":385,"large-height":273,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","1536x1536-width":385,"1536x1536-height":273,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","2048x2048-width":385,"2048x2048-height":273,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","card_image-width":385,"card_image-height":273,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-14-35.png","wide_image-width":385,"wide_image-height":273}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW213755463 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW213755463 BCX0\">The color ramp on the map uses red to signify a high score and pale yellow to <\/span><span class=\"NormalTextRun SCXW213755463 BCX0\">represent<\/span><span class=\"NormalTextRun SCXW213755463 BCX0\"> a low score<\/span><span class=\"NormalTextRun SCXW213755463 BCX0\">. In this case, a high score means that the block group has highly unaffordable housing. <\/span><\/span><span class=\"EOP CommentStart SCXW213755463 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2882002,"id":2882002,"title":"2025-07-07_16-21-52","filename":"2025-07-07_16-21-52.png","filesize":261447,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-07-07_16-21-52","alt":"The results are displayed.","author":"321952","description":"","caption":"","name":"2025-07-07_16-21-52","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 20:22:17","modified":"2025-07-07 20:22:23","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":1871,"height":775,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52.png","medium-width":464,"medium-height":192,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52.png","medium_large-width":768,"medium_large-height":318,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52.png","large-width":1871,"large-height":775,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52-1536x636.png","1536x1536-width":1536,"1536x1536-height":636,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52.png","2048x2048-width":1871,"2048x2048-height":775,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52-826x342.png","card_image-width":826,"card_image-height":342,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-21-52.png","wide_image-width":1871,"wide_image-height":775}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">Let\u2019s explore the housing cost burden. Generally, it is recommended that the cost of housing (rent or mortgage payments) should not exceed 30% of a household\u2019s income. The suitability analysis includes two variables representing households paying more than 50% of their income on mortgage payments or rent; this shows a high housing cost burden.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Click the <\/span><b><span data-contrast=\"auto\">Histogram <\/span><\/b><span data-contrast=\"auto\">tab on the <\/span><b><span data-contrast=\"auto\">Results <\/span><\/b><span data-contrast=\"auto\">pane. Click <\/span><b><span data-contrast=\"auto\">Settings<\/span><\/b><span data-contrast=\"auto\"> and use the <\/span><b><span data-contrast=\"auto\">Criterion<\/span><\/b><span data-contrast=\"auto\"> drop-down menu to change the variable.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"10\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Using <\/span><b><span data-contrast=\"auto\">2023 Households w\/ Mortgage: Monthly Owner Cost 50+% of Income <\/span><\/b><span data-contrast=\"auto\">in the histogram, we can see that the mean is 5.42. This means that 5.42% of the population in Tulsa is paying over 50% of their income to pay for their mortgage.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"10\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Using <\/span><b><span data-contrast=\"auto\">2023 Households with Gross Rent 50+% of Income<\/span><\/b><span data-contrast=\"auto\"> in the histogram, we can see that the mean is 19.94. That means that 19.94% of the population in Tulsa is paying over 50% of their income to pay their rent. <\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">These results show that there is a housing affordability issue in Tulsa and identifies areas that are the most burdened. However, it is necessary to validate the variable selection using the correlation matrix to determine if the results are skewed in any way.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;201341983&quot;:0,&quot;335551550&quot;:1,&quot;335551620&quot;:1,&quot;335559685&quot;:0,&quot;335559737&quot;:0,&quot;335559738&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:279}\">\u00a0<\/span><\/p>\n<h3><span data-contrast=\"none\">Analyze variable correlation<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"auto\">Now, let\u2019s check if the variable selection has any overlap or redundancy using the new correlation matrix. On the <\/span><b><span data-contrast=\"auto\">Results <\/span><\/b><span data-contrast=\"auto\">pane, click the <\/span><b><span data-contrast=\"auto\">Correlation matrix <\/span><\/b><span data-contrast=\"auto\">tab. Click <\/span><b><span data-contrast=\"auto\">Expand <\/span><\/b><span data-contrast=\"auto\">to see a larger view. By default, the correlation matrix displays each item in the chart with color-coding to visualize the strength of the variable correlation.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2881992,"id":2881992,"title":"2025-07-07_16-15-07","filename":"2025-07-07_16-15-07.png","filesize":62290,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-07-07_16-15-07","alt":"Use the correlation matrix.","author":"321952","description":"","caption":"","name":"2025-07-07_16-15-07","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 20:21:21","modified":"2025-07-07 20:21:30","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":1869,"height":524,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07.png","medium-width":464,"medium-height":130,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07.png","medium_large-width":768,"medium_large-height":215,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07.png","large-width":1869,"large-height":524,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07-1536x431.png","1536x1536-width":1536,"1536x1536-height":431,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07.png","2048x2048-width":1869,"2048x2048-height":524,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07-826x232.png","card_image-width":826,"card_image-height":232,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-07-07_16-15-07.png","wide_image-width":1869,"wide_image-height":524}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span data-contrast=\"auto\">In this example, the dark green cell shows a strong positive correlation. Correlation is measured using Pearson&#8217;s r, which is a coefficient ranging between -1 and 1 that measures both the strength and direction of a linear relationship.\u00a0Darker greens indicate stronger positive correlation (close to +1), darker reds indicate stronger negative correlation (close to -1), and near-white indicates weak or no correlation (near 0).\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">As expected, the two home value variables show a very strong positive correlation, with a Pearson coefficient above 0.9.\u00a0Click on the corresponding item in the chart to view its metadata. The pop-up includes a scatterplot, showing how both variables are nearly identical.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2881702,"id":2881702,"title":"2025-06-16_13-33-20","filename":"2025-06-16_13-33-20.png","filesize":37554,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/analytics\/analyze-variable-correlation-in-arcgis-business-analyst-web-app\/2025-06-16_13-33-20","alt":"Click on the item in the chart representing home value correlation.","author":"321952","description":"","caption":"","name":"2025-06-16_13-33-20","status":"inherit","uploaded_to":2881002,"date":"2025-07-07 19:36:32","modified":"2025-07-07 19:36:41","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":714,"height":474,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","medium-width":393,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","medium_large-width":714,"medium_large-height":474,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","large-width":714,"large-height":474,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","1536x1536-width":714,"1536x1536-height":474,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","2048x2048-width":714,"2048x2048-height":474,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20-700x465.png","card_image-width":700,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2025-06-16_13-33-20.png","wide_image-width":714,"wide_image-height":474}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<p><span class=\"TextRun SCXW61533955 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW61533955 BCX0\">A high Pearson correlation coefficient (e.g., above 0.9) between the home value variables suggests potential multicollinearity, meaning the variables may be capturing the same concept. <\/span><span class=\"NormalTextRun SCXW61533955 BCX0\">To improve the strength of the suitability analysis design, we need to adjust the home value variables. We can do any of the following: <\/span><\/span><span class=\"EOP SCXW61533955 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li>Remove one of the home value variables from the analysis.<\/li>\n<li>Merge the correlated home value variables into a subindex. (See\u00a0<a class=\"xref xref\" href=\"https:\/\/links.esri.com\/ba-web\/composite-indices\" target=\"_blank\" rel=\"noopener\">Creating Composite Indices Using ArcGIS<\/a> for detailed guidance on building subindices.)<\/li>\n<li>Adjust weights to mitigate the effect of the highly correlated home value variables so that the same underlying concept isn&#8217;t counted twice. <span class=\"TextRun SCXW73237864 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW73237864 BCX0\">Weights represent the relative importance of each criterion as it contributes to the final score.<\/span> <\/span><\/li>\n<\/ul>\n<p>Always apply domain knowledge when interpreting the correlation matrix to inform variable selection.<\/p>\n<h3><span data-contrast=\"none\">Conclusion<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"none\">This blog article describes using the new variable correlation capability in the suitability analysis workflow. We welcome your feedback! Please use the feedback option at the bottom of this blog article or post on Esri Community.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"sidebar","content":"<h3><span data-contrast=\"none\">Resources<\/span><span data-ccp-props=\"{&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:160,&quot;335559739&quot;:80}\">\u00a0<\/span><\/h3>\n<p><span data-contrast=\"none\">Learn more about the suitability analysis workflow in the following help documentation:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">To read the help documentation about the workflow, see <\/span><a href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/suitability-analysis.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Perform a suitability analysis.<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">To learn about the underlying calculations used in the correlation matrix, see <\/span><a href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/understand-variable-correlation.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Variable correlation reference.<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"3\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">To learn about the calculations in the <strong>Results<\/strong> pane, see <\/span><a href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/understand-the-results-pane.htm\" target=\"_blank\" rel=\"noopener\"><span data-contrast=\"none\">Results pane reference<\/span><\/a><a href=\"https:\/\/doc.arcgis.com\/en\/business-analyst\/web\/points-of-interest-search.htm\"><span data-contrast=\"none\">.<\/span><\/a><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Center","spotlight_image":false}],"related_articles":[{"ID":2853062,"post_author":"341682","post_date":"2025-06-26 09:46:31","post_date_gmt":"2025-06-26 16:46:31","post_content":"","post_title":"What's new in ArcGIS Business Analyst Web App June 2025","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"whats-new-in-arcgis-business-analyst-web-app-june-2025","to_ping":"","pinged":"","post_modified":"2025-07-21 15:03:19","post_modified_gmt":"2025-07-21 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