Esri User Conference 2026: Spatial Statistics & GeoAI Sessions
Spatial Statistics in ArcGIS Pro can supercharge any analysis by providing insight into the structure of the data, identifying meaningful patterns in space and time, modeling spatial relationships between variables, and making predictions. Attendees can learn about spatial analysis and data science, ask questions, and preview the latest tools and methods released in ArcGIS Pro 3.7.
The Analytics area showcase will be available from Tuesday, July 14 to Thursday, July 16, for spatial statistics and analysis questions.
Workshops & Demo Theatres include:
Spatial data science: This presentation shares examples of applying spatial data science processes to real-world problems, covering posing questions, data selection, method identification, result evaluation, and communication.
R-ArcGIS Bridge: Discover how to integrate spatial analysis and mapping capabilities of ArcGIS with open-source R libraries. Learn to incorporate R capabilities into workflows such as downloading ArcGIS Survey123 attachments, geocoding addresses, and deploying custom geoprocessing services to ArcGIS Enterprise.
Kriging: A foundational understanding of Kriging, the most widely used interpolation method in GIS, including best practices, assumptions, and result interpretation. See how Empirical Bayesian Kriging (EBK) allows accurate interpolation in 2D and 3D, using explanatory variables and exploring results as a continuous voxel layer.
Related Analytics & GeoAI Sessions:
Spotlight Session: See how ArcGIS, enhanced with AI, transforms GIS workflows. Learn how AI models extract and analyze data faster, how geospatial context strengthens AI decisions, and how AI assistants boost productivity within ArcGIS apps. Explore agentic AI for automation and integration across systems.
Geospatial Artificial Intelligence (GeoAI) in ArcGIS: This session highlights the application of ArcGIS GeoAI capabilities across imagery, 3D, unstructured text, and feature, tabular, and time-series data and workflows for clustering, pattern detection, prediction, forecasting, and information extraction.
Geospatial foundation models: Explore how GeoAI integrates foundation models into analytical workflows, including remote sensing foundation models, location embeddings, and geospatial vision-language models. Discuss implications for environmental, urban, and other application areas.
ArcGIS Spatial Analyst: Explore how to analyze terrains, select locations, discover spatial patterns, find routes, manage risk, and detect patterns using ArcGIS Spatial Analyst for spatial modeling and raster data analysis.
ArcGIS Business Analyst: Discover available data through the ArcGIS GeoEnrichment service and locally installed datasets, including Esri-published variables and ArcGIS Living Atlas of the World data for analyses, infographics, and workflows.
ArcGIS Knowledge: Learn the foundations of building and exploring knowledge graphs to uncover hidden relationships between people, places, events, and assets. This session covers key concepts, data integration patterns, and visualization tools for accelerating investigations by combining spatial and nonspatial data.
Deep learning in ArcGIS: A high-level overview of key concepts, common challenges, and best practices for applying deep learning across ArcGIS, with real-world examples and emerging approaches with oriented imagery.