"},{"ID":198752,"user_firstname":"Alex","user_lastname":"Henesy","nickname":"ahenesy","user_nicename":"ahenesy","display_name":"Alex Henesy","user_email":"ahenesy@esri.com","user_url":"https:\/\/doc.arcgis.com\/en\/esri-demographics\/data\/updated-demographics.htm","user_registered":"2021-03-08 19:41:17","user_description":"Alex is a demographic and economic analyst on Esri's Data Development team. This team's economists, statisticians, demographers, geographers, and analysts produce independent small-area demographic and socioeconomic estimates and forecasts for the United States. The team develops exclusive demographic models and methodologies to create market-proven datasets, many of which are now industry benchmarks such as Tapestry Segmentation, Consumer Spending, Market Potential, and annual Updated Demographics.","user_avatar":"
"},{"ID":4161,"user_firstname":"Jim","user_lastname":"Herries","nickname":"Jim Herries","user_nicename":"jimhe","display_name":"Jim Herries","user_email":"jherries@esri.com","user_url":"","user_registered":"2018-03-02 00:15:47","user_description":"Jim Herries is a geographer with Esri in Redlands, California. He serves as Senior Principal GIS Engineer, GIS Engineering Lead, Cartography on the team responsible for ArcGIS Living Atlas of the World.\r\n\r\nJim works with teams on thematic mapping and other types of maps that bring data to life, reflecting a drive to help GIS users find insights as they go along. He constantly looks for ways to create clear, focused map information products that incorporate meaningful spatial analysis and evocative visualizations. \r\n\r\nWhen he started in GIS at Ohio State, he walked over to the campus library to transcribe census data by hand to paper so that he could hand-enter it into spreadsheets for upload into Arc\/INFO for mapping and analysis. Today, he appreciates how web GIS brings everyone access to good data in useful layers and maps as a starting point for great work.","user_avatar":"
"}],"short_description":"See Esri\u2019s preliminary demographic projections for the 2030 apportionment by state for the US House of Representatives. ","flexible_content":[{"acf_fc_layout":"content","content":"Every decade, the U.S. Census provides updated population counts that directly impact the allocation of political representation. Following the 2030 Census, the fixed total of 435 seats in the U.S. House of Representatives will be redistributed among the 50 states through a process known as\u00a0apportionment. This calculation is based on state-level population totals and has further downstream effects on both\u00a0Electoral College representation\u00a0and the\u00a0allocation of certain categories of federal funding.<\/p>\n
Shifts in population\u2014driven by migration, economic opportunities, and demographic changes\u2014are expected to produce notable adjustments in seat distribution. Esri\u2019s preliminary demographic projections indicate that the 2030 apportionment cycle could result in some of the most substantial representation changes in recent decades. This blog offers an initial outlook based on Esri\u2019s projected 2030 seat allocations and some of the interesting stories behind the numbers.<\/p>\n
Apportionment is the process of allocating U.S. House seats to states based on population counts from the decennial census. The U.S. uses the Method of Equal Proportions, which ensures that each seat represents roughly\u00a0the same number of people nationwide.\u00a0Every state is guaranteed at least one seat.<\/p>\n
This process has shifted political power by region as the nation\u2019s population has grown. Historically, the West and South regions have gained congressional seats while the Midwest and Northeast have lost congressional seats.<\/p>\n
Over the last 20 years, Esri has leveraged its Esri Updated Demographics population estimates to forecast congressional seat allocations in anticipation of the decennial U.S. Census. Watch as each of the 435 seats are assigned state-by-state using Esri’s 2030 population projections:<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_1j02vie4","time":false,"start":0,"stop":""},{"acf_fc_layout":"content","content":" Esri has a proven track record of utilizing its Esri Updated Demographics<\/a> population estimates to forecast congressional seat allocations prior to the release of the decennial U.S. Census. These forecasts have been applied to both the 2010 and 2020 apportionment cycles.<\/p>\n There are several key considerations that provide important context when analyzing Esri\u2019s 2020 projections:<\/p>\n In\u00a0June of 2025, Esri released the latest vintage of\u00a0Updated Demographics<\/a>, which includes\u00a0current year (2025) population estimates\u00a0and five year (2030) population projections*. Instead of waiting until the official 2030 estimates are published in five years, we conducted an early analysis of potential\u00a0congressional apportionment outcomes\u00a0using Esri\u2019s current five-year projections.<\/p>\n While other organizations have also produced\u00a02030 apportionment forecasts, many rely on\u00a0straight-line interpolation\u2014extending observed trends from 2020 to a mid-decade point directly through to 2030. This method assumes constant growth rates, which can be misleading because\u00a0population dynamics often shift within a decade\u00a0due to migration patterns, economic changes, and other demographic factors.<\/p>\n Esri\u2019s\u00a02030 projections\u00a0avoid this limitation by employing a\u00a0multi-method modeling framework\u00a0rather than simple linear extrapolation. The approach integrates:<\/p>\n <\/p>\n By combining results from these complementary methods, Esri produces\u00a0more robust and\u00a0adaptive projections\u00a0<\/a>that better reflect the complex, evolving nature of population change over the decade.<\/p>\n *For this analysis, Esri Updated Demographics July 1st<\/sup>\u00a0estimates were date shifted to April 1st<\/sup>\u00a0estimates to align with the 2030 Census reference date.<\/em><\/p>\n"},{"acf_fc_layout":"content","content":" click on the map to explore<\/em><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2949936,"id":2949936,"title":"2030 Seats Gained or Lost","filename":"2030-Seats-Gained-or-Lost-scaled.png","filesize":438515,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-scaled.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/esri-demographics\/state-government\/esri-mid-decade-apportionment-projections-for-2030-how-the-next-census-could-reshape-the-congressional-landscape\/2030-seats-gained-or-lost","alt":"Projected 2030 house seats gained and lost by state","author":"6491","description":"","caption":"","name":"2030-seats-gained-or-lost","status":"inherit","uploaded_to":2948097,"date":"2025-11-25 00:35:33","modified":"2025-11-25 00:36:29","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":2560,"height":1524,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-scaled.png","medium-width":438,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-scaled.png","medium_large-width":768,"medium_large-height":457,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-scaled.png","large-width":1814,"large-height":1080,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-1536x914.png","1536x1536-width":1536,"1536x1536-height":914,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-2048x1219.png","2048x2048-width":2048,"2048x2048-height":1219,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-781x465.png","card_image-width":781,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030-Seats-Gained-or-Lost-1815x1080.png","wide_image-width":1815,"wide_image-height":1080}},"image_position":"center","orientation":"horizontal","hyperlink":"https:\/\/esri.maps.arcgis.com\/apps\/instant\/insets\/index.html?appid=4cb7133fb2e949e8a0efa71df36b74ab"},{"acf_fc_layout":"content","content":" The following scenarios are based on population counts at the state level, assuming all other states are held constant at Esri\u2019s 2030 projected values.\u00a0<\/em><\/p>\n Esri Demographics contains over 15,000 demographic and socioeconomic estimates created by Esri\u2019s Data Development team. Topics include population, housing, consumer spending, market potential, ArcGIS Tapestry, and much more. It is available throughout ArcGIS in the following products:<\/p>\n2030 Projected Winners and Losers<\/strong><\/h2>\n"},{"acf_fc_layout":"image","image":{"ID":2949612,"id":2949612,"title":"2030WinnersLosers","filename":"2030WinnersLosers.png","filesize":18670,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/esri-demographics\/state-government\/esri-mid-decade-apportionment-projections-for-2030-how-the-next-census-could-reshape-the-congressional-landscape\/2030winnerslosers","alt":"projected 2030 state winners and losers in congressional apportionment","author":"6491","description":"","caption":"","name":"2030winnerslosers","status":"inherit","uploaded_to":2948097,"date":"2025-11-20 21:08:55","modified":"2025-11-20 21:09:39","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":613,"height":301,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","medium-width":464,"medium-height":228,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","medium_large-width":613,"medium_large-height":301,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","large-width":613,"large-height":301,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","1536x1536-width":613,"1536x1536-height":301,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","2048x2048-width":613,"2048x2048-height":301,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","card_image-width":613,"card_image-height":301,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2030WinnersLosers.png","wide_image-width":613,"wide_image-height":301}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"
Projection Accuracy<\/strong><\/h2>\n
\n
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2030 Projections \u2013 why now?<\/strong><\/h2>\n
\n
Explore the Esri 2030 Apportionment Map in
\nArcGIS Living Atlas of the World<\/strong><\/h2>\nBehind the Numbers – states on the cusp<\/strong><\/h2>\n
Hanging on to the last seat by the skin of their teeth:<\/strong><\/h4>\n
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Losing a seat – so close but no cigar:<\/strong><\/h4>\n
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Almost there – so close to getting one more:<\/strong><\/h4>\n
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Gaining a seat, but just barely: <\/strong><\/h4>\n
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Explore Esri Demographics<\/strong><\/h2>\n
\n
\n<\/em><\/strong>Ready to use layers and maps<\/a><\/li>\n
\n<\/em><\/strong>Identify sites and evaluate markets with\u00a0ArcGIS Business Analyst<\/a>, demographic mapping software.<\/li>\n
\n<\/em><\/strong>Enhance workflows and apps with the\u00a0ArcGIS GeoEnrichment Service<\/a>\u00a0\u2013 Enrich data with additional location-based information about people and places in a specific area.<\/li>\n
\n<\/em><\/strong>Enrich your data with demographic data in ArcGIS Online using Enrich Layer<\/a>\u00a0and in ArcGIS Pro using Enrich<\/a>.<\/li>\n
\n<\/em><\/strong>Access high-quality, location-based data from the Esri Store<\/a>.<\/li>\n<\/ul>\n