"},{"ID":9602,"user_firstname":"Adam","user_lastname":"Martin","nickname":"Adam Martin","user_nicename":"adam_martin","display_name":"Adam Martin","user_email":"Adam_Martin@esri.com","user_url":"https:\/\/www.esri.com\/en-us\/arcgis\/products\/arcgis-knowledge\/overview","user_registered":"2019-06-03 19:20:53","user_description":"Adam Martin is a Senior Product Manager focused on ArcGIS Knowledge and system interoperability. With more than fifteen years of entrepreneurial, government and private sector experience, he applies business and technology strategies as levers to improve government digital services and public decision making. Based in Nashville, TN, he focuses on how ArcGIS connects \u201call the data\u201d into knowledge graphs to find hidden patterns and relationships, provide quick context and accelerate decision making.","user_avatar":"
"}],"show_article_image":false,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/knowledge_card.png","wide_image":false,"short_description":"Explore\u00a0indirect relationships and understand the cascading impacts of decisions and disruptions with ArcGIS Knowledge Graphs.","flexible_content":[{"acf_fc_layout":"content","content":"At the 2026 Esri User Conference, one theme drew consistent interest across the plenary, technical sessions, and spotlight presentations: the power of connecting spatial and non-spatial data. By fusing data together into knowledge graphs, users can better explore indirect relationships, accelerate network analysis, understand the cascading impacts of decisions and disruptions, and generate smarter recommendations. ArcGIS Knowledge graphs allow teams to unlock insights that go beyond what a traditional map can show and analyze faster than traditional GIS workflows alone enable.\u00a0<\/span>\u00a0<\/span><\/p>\n From tracking the effects of a hurricane on critical infrastructure to\u00a0simulating species extinctions\u00a0and prioritizing bridge repairs, attendees saw firsthand how graph analytics can reveal connections that traditional maps and tables simply\u00a0can’t\u00a0capture. If you missed\u00a0it, or\u00a0want a curated look at the most compelling demos,\u00a0here’s\u00a0a rundown of the use cases that stood out.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":" When extreme weather events strike, the immediate question is rarely just \u201cwhat broke?\u201d\u00a0It\u2019s\u00a0\u201cwhat else will break because of it, and who is affected?\u201d This use case explores how dependency mapping\u00a0among critical lifelines\u00a0allows teams to trace the downstream effects of weather events on interconnected infrastructure, from power grids to\u00a0hospitals,\u00a0schools,\u00a0and community services.\u00a0By connecting\u00a0these community assets from disparate\u00a0data sources, using\u00a0dependencies\u00a0derived from logical and spatial relationships, teams can\u00a0better\u00a0simulate and\u00a0predict who will be impacted and how, surface critical risks before they escalate, and understand the full severity of a situation in time to act.<\/span>\u00a0<\/span><\/p>\n Applicable for:<\/span><\/b>\u00a0Resilience planners, emergency managers, risk managers, network architects, operations managers, GIS analysts<\/span>\u00a0<\/span><\/p>\n Industries:<\/span><\/b>\u00a0Electric and gas utilities, water and wastewater, telecommunications, transportation, healthcare, state and local government, emergency management<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_y85qn1w4\/238781673 ","time":false,"start":0},{"acf_fc_layout":"content","content":" Presenter:\u00a0<\/span><\/b> Monica Youngman, Chief Scientist, NOAA National Weather Service<\/span><\/p>\n Where it appeared at UC:<\/span><\/b>\u00a0Plenary and Special Interest Group session<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":" <\/p>\n"},{"acf_fc_layout":"content","content":" Not all infrastructure is equally\u00a0critical, and\u00a0knowing which assets to\u00a0prioritize for\u00a0maintenance and repair can save lives and reduce economic disruption. This demo introduces\u00a0how and\u00a0graph analytic\u00a0approach\u00a0can\u00a0expedite\u00a0analysis\u00a0to\u00a0identify\u00a0which structures are most essential. The approach\u00a0goes beyond simple condition ratings to factor in how connected an asset truly is within a regional transportation network and how vital it is to daily commutes,\u00a0enabling teams to effectively plan bridge repairs by prioritizing the most critical assets first.\u00a0<\/span>\u00a0<\/span><\/p>\n Applicable for:<\/span><\/b>\u00a0Asset managers, transportation planners, maintenance planners, risk managers, operations analysts, GIS analysts<\/span>\u00a0<\/span><\/p>\n Industries:<\/span><\/b>\u00a0Departments of transportation, public works, rail and transit, AEC and transportation engineering, infrastructure\u00a0owners\u00a0and operators<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_zp0xl9f9\/238781673 ","time":false,"start":0},{"acf_fc_layout":"content","content":" Presenter:<\/span><\/b> Alberto Nieto, <\/span>Principal Product Engineer, Esri<\/p>\n Where it appeared\u00a0at UC:<\/span><\/b>\u00a0Plenary<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":" <\/p>\n"},{"acf_fc_layout":"content","content":" Anyone who has managed outreach to parcel owners knows how frustrating it can be to\u00a0identify\u00a0the right contact. Beyond individual homeowners, parcels are often held by corporate entities that may own hundreds of properties. Ownership chains are\u00a0complex,\u00a0records are\u00a0fragmented, and\u00a0reaching out to the wrong contact leads to costly delays.\u00a0This use case demonstrates how a knowledge graph can connect disparate ownership records to surface the true owner, streamlining communication to\u00a0inform\u00a0the right people proactively and efficiently.<\/span>\u00a0<\/span><\/b>\u00a0<\/span><\/p>\n The\u00a0Plenary\u00a0demonstration highlighted how a local\u00a0file knowledge graph\u00a0can be\u00a0in ArcGIS Pro\u00a0to\u00a0help reveal indirect\u00a0parent\u00a0corporate ownership relationships\u00a0to\u00a0develop more informed, streamlined engagement strategies.<\/span>\u00a0<\/span><\/p>\n The Data Analytics Spotlight demonstration unpacks this use case to\u00a0explain\u00a0<\/span>\u00a0<\/span><\/p>\n Applicable for:<\/span><\/b>\u00a0Land records managers, assessors, real estate analysts, community engagement teams, planners, investigators, GIS analysts<\/span>\u00a0<\/span><\/p>\n Industries:<\/span><\/b>\u00a0Local government, land administration, real estate, AEC, utilities, insurance, economic development, environmental and conservation organizations<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_47nh66r9\/238781673 ","time":false,"start":0},{"acf_fc_layout":"content","content":" Presenters:<\/span><\/b>\u00a0<\/span>Peter\u00a0Erlenbach, Sr. Solution Engineer, Esri<\/span>\u00a0<\/span><\/p>\n Where it appeared:<\/span><\/b>\u00a0Plenary\u00a0<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_ehy2g8my\/238781673","time":false,"start":0},{"acf_fc_layout":"content","content":" Presenters:<\/span><\/b>\u00a0<\/span>Adam Martin, Sr. Product Manager, Esri<\/span>\u00a0<\/span><\/p>\n Where it appeared:<\/span><\/b>\u00a0<\/span>Analytics and Data Science and ArcGIS<\/a>\u00a0Spotlight Session<\/p>\n"},{"acf_fc_layout":"content","content":" <\/p>\n"},{"acf_fc_layout":"content","content":" Conservation teams need more than observation data to protect at-risk species. They need to understand how those species depend on one another. This demo shows how ArcGIS Knowledge\u00a0provides the foundation to\u00a0connect\u00a0disparate\u00a0biodiversity datasets\u00a0— from\u00a0community driven species observations to\u00a0cataloged species\u00a0interactions\u00a0gathered\u00a0from scientific literature.\u00a0All this eventually turning\u00a0into an ecological\u00a0networkpossible\u00a0of\u00a0simulating\u00a0the loss of a keystone species,\u00a0tracing\u00a0cascading effects across the food web, and\u00a0pinpointing\u00a0exactly where on the landscape intervention is needed most. Drawing on hundreds of thousands of real-world observations and thousands of documented biotic interactions, the knowledge graph captures relationships that no map or spreadsheet could reveal on its own,\u00a0enabling\u00a0understanding\u00a0of\u00a0how biodiversity functions as a system and where it is most vulnerable.\u00a0<\/span>\u00a0<\/span><\/p>\n This\u00a0example also\u00a0demonstrates\u00a0how\u00a0a\u00a0custom\u00a0web\u00a0application\u00a0can be\u00a0tailored to the specific visualization needs of your community, all\u00a0powered by an ArcGIS Knowledge Graph service\u00a0that is\u00a0designed and\u00a0explore\u00a0using ArcGIS desktop\u00a0applications.<\/span>\u00a0<\/span><\/p>\n Applicable for:<\/span><\/b>\u00a0Ecologists, conservation planners, biodiversity researchers, natural resource managers, environmental analysts, GIS analysts<\/span>\u00a0<\/span><\/p>\n Industries:<\/span><\/b>\u00a0Conservation, environmental management, forestry, agriculture, natural resources, parks and wildlife, research and academia, national and local government<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_eoougnj5\/238781673","time":false,"start":0},{"acf_fc_layout":"content","content":" Presenter:<\/span><\/b>\u00a0Hannah Nisonson, Solution Engineer, Esri<\/span>\u00a0<\/span><\/p>\n Where it appeared:<\/span><\/b>\u00a0<\/span>Conservation for Good\u00a0<\/span>Demo\u00a0Theater Presentation<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":" <\/p>\n"},{"acf_fc_layout":"content","content":" Rail safety depends on knowing exactly where every train is, what track conditions are, and how the broader network is configured at any given moment. Positive Train Control (PTC) is a federally mandated safety system\u00a0created in response to\u00a0devastating incidents,\u00a0including derailments, head-on collisions, and crew incursions,\u00a0that\u00a0caused\u00a0injuries, fatalities, and billions of dollars in damage.\u00a0<\/span>\u00a0<\/span><\/p>\n While PTC\u00a0excels at\u00a0automatically applying brakes in real time, the data it generates holds another layer of value that has gone\u00a0largely untapped. This demo\u00a0showed\u00a0how combining spatial and graph analysis in ArcGIS Knowledge can unlock patterns within PTC output logs that no map layer could surface alone,\u00a0identifying\u00a0high-risk track segments, engineers with above-average incident rates, and locomotives whose maintenance history correlates with operational anomalies. The result is a shift from reactive incident response to proactive risk management, finding the causes of problems and modeling solutions before they reach the field.\u00a0<\/span>\u00a0<\/span><\/p>\n Applicable for:<\/span><\/b>\u00a0Rail operations managers, safety managers, network engineers, maintenance planners, risk analysts, data scientists, GIS analysts<\/span>\u00a0<\/span><\/p>\n Industries:<\/span><\/b>\u00a0Freight rail, passenger rail, transit agencies, transportation regulators, AEC and rail engineering, infrastructure operators<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2980752,"id":2980752,"title":"image (41)","filename":"image-41.png","filesize":803572,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-knowledge\/analytics\/real-world-stories-that-show-whats-possible-with-knowledge-graphs\/image-41-3","alt":"Knowledge graph connecting rail networks for safer operations","author":"352692","description":"","caption":"Connecting rail operations with knowledge graphs to improve safety.","name":"image-41-3","status":"inherit","uploaded_to":2980738,"date":"2026-08-21 20:58:31","modified":"2026-08-21 22:26:49","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":1291,"height":1003,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","medium-width":336,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","medium_large-width":768,"medium_large-height":597,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","large-width":1291,"large-height":1003,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","1536x1536-width":1291,"1536x1536-height":1003,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","2048x2048-width":1291,"2048x2048-height":1003,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41-599x465.png","card_image-width":599,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/image-41.png","wide_image-width":1291,"wide_image-height":1003}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Presenter:<\/span><\/b>\u00a0Jim Barry, Sr. Solution Engineer, Esri<\/span><\/p>\n Where it appeared:<\/span><\/b>\u00a0Rail\u00a0Special Interest Group Meeting<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"content","content":" <\/p>\n"},{"acf_fc_layout":"content","content":" In healthcare, critical relationships rarely surface in a spreadsheet. Patient journeys span networks of providers, facilities, referrals, and events.\u00a0These\u00a0connections are\u00a0nearly\u00a0invisible\u00a0in tabular data but become\u00a0immediately\u00a0apparent\u00a0when visualized and analyzed as a graph. This use case\u00a0demonstrates\u00a0how\u00a0a\u00a0sequence\u00a0of\u00a0spatial and relational\u00a0analytical\u00a0questions\u00a0can\u00a0uncover\u00a0important\u00a0patterns\u00a0across\u00a0the\u00a0healthcare\u00a0system\u00a0that drive actions.\u00a0<\/span>\u00a0<\/span><\/p>\n In this example,\u00a0the analyst\u00a0uses\u00a0spatial analytics\u00a0to\u00a0quickly\u00a0find\u00a0drive times\u00a0for each\u00a0patient\u00a0to health providers, then uses graph analytics to quickly calculate average drive times to specific providers that match their\u00a0specific\u00a0medical\u00a0conditions\u00a0and preferences.\u00a0Then, the analysis\u00a0uses\u00a0spatial\u00a0analytics\u00a0to find\u00a0outlier\u00a0groups\u00a0of\u00a0patients\u00a0with higher\u00a0average\u00a0drive times\u00a0and\u00a0graph analytics to\u00a0identify\u00a0their\u00a0most\u00a0commonly\u00a0shared\u00a0medical specialties.\u00a0The interplay of these analytic tools\u00a0can\u00a0more quickly\u00a0give\u00a0health\u00a0providers or insurers\u00a0common-sense\u00a0insights\u00a0needed to improve their coverage network. For example, they can decide whether to\u00a0add specific providers or provider types to their network to\u00a0improve\u00a0customer service and health outcomes.<\/span>\u00a0<\/span><\/p>\n Applicable for:<\/span><\/b>\u00a0Provider network managers, health data analysts, care coordinators, public health analysts, epidemiologists, fraud investigators, data scientists, GIS analysts<\/span>\u00a0<\/span><\/p>\n Industries:<\/span><\/b>\u00a0Health insurance, hospital and health systems, public health, government health agencies, social services, healthcare\u00a0regulation\u00a0and research<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"kaltura","video_id":"1_6jpagppd","time":false,"start":0},{"acf_fc_layout":"content","content":" Presenter:<\/span><\/b> Megan Banaski,\u00a0<\/span>Sr. Product Engineer, Esri<\/p>\n Where it appeared:<\/span><\/b>\u00a0<\/span>ArcGIS Knowledge: Connecting Data and Analyzing Relationships<\/a>\u00a0Technical session<\/p>\n"},{"acf_fc_layout":"content","content":"Quick links<\/h2>\n
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1. Understanding cascading infrastructure impacts during extreme weather events<\/span><\/b>\u00a0<\/span><\/h2>\n
\n\ud83e\udc45<\/span> Back to Quick Links<\/a><\/p>\n2. Prioritizing bridge\u00a0repairs\u00a0using\u00a0graph\u00a0analytics and\u00a0commuter Data<\/span><\/b>\u00a0<\/span><\/h2>\n
\n\ud83e\udc45<\/span> Back to Quick Links<\/a><\/p>\n3. Tracing true\u00a0parcel\u00a0ownership to\u00a0improve\u00a0communication\u00a0and\u00a0engagement\u00a0<\/span><\/b>\u00a0<\/span><\/h2>\n
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\n\ud83e\udc45<\/span> Back to Quick Links<\/a><\/p>\n4. Mapping extinction\u00a0cascades:\u00a0how the\u00a0loss of\u00a0one\u00a0species\u00a0affects an\u00a0entire\u00a0ecosystem<\/span><\/b>\u00a0<\/span><\/h2>\n
\n\ud83e\udc45<\/span> Back to Quick Links<\/a><\/p>\n5. Connecting rail\u00a0networks for\u00a0safer\u00a0operations: positive\u00a0train\u00a0control<\/span><\/b>\u00a0<\/span><\/h2>\n
\n\ud83e\udc45<\/span> Back to Quick Links<\/a><\/p>\n6. Improving healthcare access with spatial and graph analytics<\/b><\/h2>\n