{"id":2942719,"date":"2025-11-10T09:10:23","date_gmt":"2025-11-10T17:10:23","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2942719"},"modified":"2025-11-10T09:28:36","modified_gmt":"2025-11-10T17:28:36","slug":"use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app","title":{"rendered":"Map low food access in the Atlanta metro area using an AI assistant in 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":[37141],"tags":[],"industry":[],"product":[36711],"class_list":["post-2942719","blog","type-blog","status-publish","format-standard","hentry","category-decision-support","product-bus-analyst"],"acf":{"authors":[{"ID":349662,"user_firstname":"Moe","user_lastname":"Abdullah","nickname":"Moe Abdullah","user_nicename":"mabdullahesri-com_esriinc","display_name":"Moe Abdullah","user_email":"mabdullah@esri.com","user_url":"","user_registered":"2023-12-01 15:21:23","user_description":"Moe is a Solution Engineer for the State and Local Government team at Esri, where he helps communities harness the power of geospatial intelligence to make smarter, more sustainable decisions. He holds a dual Bachelor of Science in Geospatial Sciences and Geography and a Master of Science in Geospatial Sciences from Missouri State University. Passionate about creating a lasting positive impact, Moe is driven by the intersection of technology and community.\r\n\r\nOutside of work, Moe is a dedicated Chelsea FC fan, an outdoor enthusiast who loves to hike and explore, and a traveler who connects with people and cultures through food and shared stories.","user_avatar":""},{"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":""}],"short_description":"Use Business Analyst assistant (preview) to analyze low food access and identify target locations for emergency food pantries.","flexible_content":[{"acf_fc_layout":"content","content":"

Food deserts (now known by the USDA Economic Research Service\u00a0as\u00a0<\/span>low access areas<\/span><\/a>) are areas where there is limited access to grocery stores and affordable food.\u00a0You can use GIS to support demographic analysis of potential low access areas, such as in the Atlanta metro area.\u00a0This information can be used to inform target locations for emergency food\u00a0pantries.<\/span>\u00a0<\/span><\/p>\n

For example, use the AI assistant in ArcGIS Business Analyst Web App to map grocery stores with POI search and create a bivariate color-coded map of population density and poverty. With this information, you can identify an impoverished area with no local access to grocery stores and create a site for further analysis, like running an infographic to learn more about that community’s demographic composition. <\/span>\u00a0<\/span><\/p>\n

Check out the video below for a demonstration<\/span>\u00a0or follow along with the written description<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_uhrh82x8","time":false,"start":0,"stop":""},{"acf_fc_layout":"content","content":"

For this example,\u00a0<\/span>you\u2019ll<\/span>\u00a0use the AI assistant in Business Analyst to generate suggestions\u00a0<\/span>for your mapping and analysis<\/span>.<\/span>\u00a0<\/span>Access to the assistant<\/span><\/a>\u00a0is administered at the ArcGIS Online organization level.\u00a0<\/span>\u00a0<\/span><\/p>\n

To get started, c<\/span>lick\u00a0<\/span>Business Analyst\u00a0assistant (preview)\u00a0<\/span><\/b>on the app header.\u00a0<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2947321,"id":2947321,"title":"2025-11-06_12-39-17","filename":"2025-11-06_12-39-17.png","filesize":17774,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app\/2025-11-06_12-39-17","alt":"Business Analyst assistant (preview) on the app header","author":"321952","description":"","caption":"","name":"2025-11-06_12-39-17","status":"inherit","uploaded_to":2942719,"date":"2025-11-06 17:40:58","modified":"2025-11-06 17:41:07","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":456,"height":124,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17-213x124.png","thumbnail-width":213,"thumbnail-height":124,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","medium-width":456,"medium-height":124,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","medium_large-width":456,"medium_large-height":124,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","large-width":456,"large-height":124,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","1536x1536-width":456,"1536x1536-height":124,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","2048x2048-width":456,"2048x2048-height":124,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","card_image-width":456,"card_image-height":124,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-39-17.png","wide_image-width":456,"wide_image-height":124}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Map grocery stores<\/span>\u00a0<\/span><\/h3>\n

Let\u2019s\u00a0perform a\u00a0<\/span>points of interest (POI)\u00a0search<\/span><\/a>\u00a0to evaluate where grocery stores\u00a0are\u00a0located\u00a0in\u00a0the Atlanta metro area.\u00a0Map grocery stores with the\u00a0prompt:\u00a0<\/span>Create a map of grocery stores\u00a0in Atlanta\u00a0metro<\/span>.\u00a0<\/span>\u00a0<\/span><\/p>\n

Turn\u00a0point\u00a0clustering off\u00a0so that\u00a0<\/span>each point\u00a0represents\u00a0an individual grocery store. You can see\u00a0that the center of the Atlanta metro area has the densest grocery\u00a0store\u00a0distribution; the number of stores declines as you move away from the city.\u00a0This is likely because\u00a0the\u00a0metro outskirts have less population density\u00a0compared to the dense urban center.\u00a0<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2947323,"id":2947323,"title":"2025-11-06_12-41-48","filename":"2025-11-06_12-41-48.png","filesize":120269,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app\/2025-11-06_12-41-48","alt":"Grocery stores mapped as POI icons in the Atlanta metro area","author":"321952","description":"","caption":"","name":"2025-11-06_12-41-48","status":"inherit","uploaded_to":2942719,"date":"2025-11-06 17:43:00","modified":"2025-11-06 17:43:04","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":503,"height":486,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","medium-width":270,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","medium_large-width":503,"medium_large-height":486,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","large-width":503,"large-height":486,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","1536x1536-width":503,"1536x1536-height":486,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","2048x2048-width":503,"2048x2048-height":486,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48-481x465.png","card_image-width":481,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-41-48.png","wide_image-width":503,"wide_image-height":486}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Create a bivariate color-coded map<\/span>\u00a0<\/span><\/h3>\n

Let\u2019s\u00a0study these outskirts to analyze population density, poverty, and grocery store availability. You can\u00a0evaluate\u00a0if there\u00a0are any target areas to strategically\u00a0identify\u00a0new grocery store locations to promote\u00a0equitable\u00a0food access\u00a0using a\u00a0<\/span>color-coded map<\/span><\/a>.\u00a0Create a bivariate color-coded map with the prompt:\u00a0<\/span>Create a map of the poverty index\u00a0and population density<\/span>.\u00a0<\/span>\u00a0<\/span><\/p>\n

By default, the map uses <\/span>block groups<\/span><\/a>\u00a0as the standard geography.\u00a0In general, a block group is composed of 600 to 3,000 residents\u00a0and is not uniform in size. Some block groups may appear larger than others because the population is not as dense.\u00a0<\/span>T<\/span>o better visualize patterns in the data, use hexagon mapping<\/span>\u00a0with\u00a0<\/span>resolution<\/span>\u00a06 hexagons<\/span>.<\/span>\u00a0<\/span><\/span>Hexagons<\/span><\/span><\/a> are six-sided polygons that are used in GIS to apply a uniform grid on a map. <\/span><\/span>The hexagon size is<\/span>\u00a0referred to as the resolution<\/span>, with 1 being the smallest and 15 being the biggest. Resolution 6 hexagons <\/span>cover a smaller average area per hexagon,<\/span>\u00a0so the analysis is hyper-localized.<\/span><\/span>\u00a0<\/span><\/p>\n

You can use the<\/span>\u00a0map legend<\/span>\u00a0to understand the meaning of\u00a0<\/span>the hexagon colors.\u00a0<\/span>To simplify the colors, use a\u00a0<\/span><\/span>3×3<\/span><\/span>\u00a0<\/span><\/span>grid. (Your color scheme may differ than what is pictured below and in the video.)\u00a0<\/span><\/span>In this, brown\u00a0<\/span>represents<\/span>\u00a0<\/span>high poverty<\/span>\u00a0and high population density,\u00a0<\/span>whereas<\/span>\u00a0pale yellow\u00a0<\/span>represents<\/span>\u00a0low poverty and low population density.<\/span><\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2947326,"id":2947326,"title":"2025-11-06_12-47-20","filename":"2025-11-06_12-47-20.png","filesize":538042,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app\/2025-11-06_12-47-20","alt":"Map of grocery store POI icons and color-coded hexagons representing poverty index and population density with a legend","author":"321952","description":"","caption":"","name":"2025-11-06_12-47-20","status":"inherit","uploaded_to":2942719,"date":"2025-11-06 17:48:42","modified":"2025-11-06 17:48:47","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":1348,"height":748,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","medium-width":464,"medium-height":257,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","medium_large-width":768,"medium_large-height":426,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","large-width":1348,"large-height":748,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","1536x1536-width":1348,"1536x1536-height":748,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","2048x2048-width":1348,"2048x2048-height":748,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20-826x458.png","card_image-width":826,"card_image-height":458,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/2025-11-06_12-47-20.png","wide_image-width":1348,"wide_image-height":748}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Visually, you can see areas that have fewer grocery store icons and hexagon color-coding that\u00a0<\/span>indicates<\/span>\u00a0<\/span>high poverty<\/span>\u00a0index and medium to high population density. For example, zoom\u00a0<\/span>in to<\/span>\u00a0Canton. This hexagon\u00a0<\/span>does not include a grocery store POI\u00a0<\/span>dot<\/span>, nor\u00a0<\/span>do<\/span>\u00a0<\/span>its<\/span>\u00a0neighboring hexagons. It is also navy blue\u00a0<\/span>in\u00a0<\/span>color,\u00a0<\/span>indicating<\/span>\u00a0<\/span>high poverty<\/span> and medium population density. You <\/span>can further analyze this region to understand if it qualifies as a low access area and should be prioritized as an area in need of new grocery stores.<\/span><\/span>\u00a0<\/span><\/p>\n

Create a site and run an infographic<\/span>\u00a0<\/span><\/h3>\n

To further analyze this region,\u00a0let\u2019s\u00a0<\/span>create a site<\/span><\/a>\u00a0<\/span>in\u00a0Canton, Georgia, to understand if this area qualifies\u00a0as a\u00a0low\u00a0access.\u00a0Create a site with the prompt:\u00a0<\/span>Create a site in Canton Georgia with 5 and 10 minute drive times<\/span>.<\/span>\u00a0<\/span><\/p>\n

The site is added to the map with 10- and 20-minute drive\u00a0times.\u00a0Drive\u00a0times\u00a0determine\u00a0how the area around a site is measured;\u00a010- and 20-minute drive\u00a0times\u00a0represent\u00a0the distance traveled to or from the site\u00a0in a given\u00a0time.\u00a0With a site,\u00a0you\u00a0can perform more localized analysis, like running an infographic.<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2942762,"id":2942762,"title":"assistant 7","filename":"assistant-7.png","filesize":77411,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app\/assistant-7","alt":"Use the assistant to create a site.","author":"321952","description":"","caption":"","name":"assistant-7","status":"inherit","uploaded_to":2942719,"date":"2025-10-13 18:08:31","modified":"2025-10-13 18:08:36","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":323,"height":327,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","medium-width":258,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","medium_large-width":323,"medium_large-height":327,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","large-width":323,"large-height":327,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","1536x1536-width":323,"1536x1536-height":327,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","2048x2048-width":323,"2048x2048-height":327,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","card_image-width":323,"card_image-height":327,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-7.png","wide_image-width":323,"wide_image-height":327}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Click on the site and use its <\/span>pop-up<\/span> menu to <\/span>run<\/span>\u00a0the\u00a0<\/span><\/span>State of the Community<\/strong>\u00a0<\/span><\/span>infographic. This infographic includes information about population growth, households compared to housing units, community participation, jobs and the economy, health, and at-risk population variables<\/span>.\u00a0\u00a0<\/span>\u00a0<\/span><\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2942764,"id":2942764,"title":"assistant 10","filename":"assistant-10.gif","filesize":63730,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/bus-analyst\/decision-support\/use-an-ai-assistant-to-map-low-food-access-in-the-atlanta-metro-area-using-arcgis-business-analyst-web-app\/assistant-10","alt":"Interact with infographic elements to learn more about median household income.","author":"321952","description":"","caption":"","name":"assistant-10","status":"inherit","uploaded_to":2942719,"date":"2025-10-13 18:09:22","modified":"2025-10-13 18:09:27","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":624,"height":351,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","medium_large-width":624,"medium_large-height":351,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","large-width":624,"large-height":351,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","1536x1536-width":624,"1536x1536-height":351,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","2048x2048-width":624,"2048x2048-height":351,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","card_image-width":624,"card_image-height":351,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/10\/assistant-10.gif","wide_image-width":624,"wide_image-height":351}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

In conclusion<\/span>\u00a0<\/span>\u00a0<\/span><\/h3>\n

With the information from this analysis, you can identify a target neighborhood to support food access, such as with a community donation center or food pantry. The decision is informed by the location of other grocery stores and the community’s composition, specifically serving low-income communities with insufficient grocery store access. <\/span>\u00a0<\/span><\/p>\n

In this blog article, you used Business Analyst assistant (preview) to perform a POI search, create a color-coded map with hexagons, and create a site. We welcome your feedback! Please use the feedback option at the bottom of this blog article or post on Esri Community. <\/span>\u00a0<\/span><\/p>\n

This article uses\u00a0<\/span>the\u00a0<\/span>Places data<\/span>set<\/span>\u00a0from Data Axle,<\/span>\u00a0<\/span>the\u00a0<\/span>Esri Updated Demographics dataset from Esri<\/span>, and basemaps provided by Esri.<\/span><\/span>\u00a0<\/span><\/em><\/p>\n

This article uses ArcGIS Business Analyst Web App. You can use the assistant, map points of interest (POI), create a color-coded map, create a site, and run infographics using\u00a0Standard and Advanced licenses.\u00a0Hexagon mapping is only available with an Advanced license.<\/span><\/i>\u00a0<\/span><\/p>\n"}],"related_articles":[{"ID":2344112,"post_author":"321952","post_date":"2024-06-28 08:30:51","post_date_gmt":"2024-06-28 15:30:51","post_content":"","post_title":"Introducing Business Analyst Assistant (Beta) in ArcGIS Business Analyst Web App | June 2024","post_excerpt":"","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"introducing-business-analyst-assistant-beta-june-2024","to_ping":"","pinged":"","post_modified":"2024-06-28 08:46:53","post_modified_gmt":"2024-06-28 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