The session will highlight how social and environmental conditions—known as Drivers of Health (DoH)—account for over 60% of health outcomes. CHM Hub®, a patented platform launched in 2014, uses geospatial analytics to identify population health needs and address local barriers. It integrates over 25 data sources, including access and health utilization patterns, to generate insights on resource deserts and DoH. Key features include Location Intelligence, Resource Optimization, and Scenario Planning, supporting use cases such as Case Management and Community Strategy. A special focus on maternal health will demonstrate how integrated data can uncover barriers in underserved communities

In today’s data-rich landscape, the challenge for school systems is not access, but structure and usability. The Los Angeles County Office of Education (LACOE), serving hundreds of schools and over a million students, faced the critical task of making vast amounts of data—federal, local, instructional, survey-based, and more —actionable at the school and district level. This session will explore how LACOE developed a geospatial framework to integrate and filter disparate data sources, enabling each district and school to uncover localized insights that drive equity and student success. Participants will gain practical takeaways for building scalable, structured, and relevant data systems that empower education leaders to move from information overload to informed decision-making.

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From “What happened?” to "What caused it happen?" to "What's going to happen" to  “What should we do about it?”, analytics often moves along a typical arc. But in homebuilding, where the "where" matters as much as the "what", adding geospatial intelligence transforms that analytical arc into a multidimensional roadmap. This session explores how spatial analytics amplifies each stage of the analytics journey, helping homebuilders move beyond static reports to more intuitive, visual, and actionable insights. The result? More strategic land decisions, sharper consumer and product focus, and fewer data-driven dead ends.

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Data costs more to onboard than it does to license. This tax keeps teams from adding new perspectives, embracing geospatial data, leads to worse products, and persists stubborn organizational silos.
In this talk, we'll introduce Overture's Global Entity Reference System (GERS) – a framework for structuring and matching map data across systems. GERS makes it easier and cheaper to share data, with more teams. We'll cover what GERS is, why it can succeed, how to connect with GERS, and run through several case studies covering how organizations connect to and leverage GERS.

Data costs more to onboard than it does to license. This tax keeps teams from adding new perspectives, embracing geospatial data, leads to worse products, and persists stubborn organizational silos.
In this talk, we'll introduce Overture's Global Entity Reference System (GERS) – a framework for structuring and matching map data across systems. GERS makes it easier and cheaper to share data, with more teams. We'll cover what GERS is, why it can succeed, how to connect with GERS, and run through several case studies covering how organizations connect to and leverage GERS.

The session explores how weather data, Earth Observation imagery, and AI support event detection, alert generation, and hazard visualization for insurers. Leveraging these technologies, the presentation shows how location intelligence platforms enhance rapid response and long-term climate risk management.

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Geodemographic classification systems have traditionally relied on experts to choose and standardise variables before applying clustering techniques to identify similar types of residential areas. Although effective, these methods can be limited by subjective choices and assumptions that may oversimplify the complex ways in which people and places are related. This research sets out a new deep learning approach that uses autoencoders to automate the selection of features and reveal hidden, non-linear patterns within large and detailed urban datasets.

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TELUS, one of Canada’s top telecom carriers, tackled major GIS challenges such as growing raster volumes, data duplication, challenging governance, and fragmented technologies, by building a Modern Cloud-Native Geospatial architecture. Using BigQuery as the central data warehouse, combined with Carto Platform and cloud-native pipeline leveraging Open Source GIS tools in GCP, see how TELUS was able to achieve data governance, performance, scalability, and ability to serve both data science network analytics and internal analytics dashboards, public facing coverage application and other applications.  Real-world uses include internal analytics dashboards, large-scale processing, public coverage maps, IoT qualification, and scalable preprocessing with Korem. Attendees will learn how these technologies addressed TELUS’s data, governance, and integration challenges through successful, real-world deployments.

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