Spatial Stack with Matt Forrest

Large Language Models can write poetry and debug code, but they still don't understand the fundamental physics of the real world. Ask an AI to find the "nearest restaurant" to a specific coordinate, and it struggles because it lacks Spatial Intelligence.
In this episode, we sit down with Jia Yu, the co-creator of Apache Sedona and co-founder of Wherobots, to discuss why geospatial data breaks standard big data engines and how he built the solution that now powers over 2 million downloads a month.
We trace the 10-year journey from a PhD research paper to a top-level Apache project, diving into the deep technical challenges of distributed computing. Jia explains why spatial data requires a completely different architecture than standard text or numbers and how the industry is finally moving toward a "Spatial Lakehouse" to break down data silos.
In this episode, we explore:
- The "Multimodality" Trap: Why mixing vector, raster, and LiDAR data crashes traditional systems.
- How SedonaDB is bringing massive scale to single-node machines (so you don't always need a cluster).
- The hardest problem in distributed computing - How to split a map across 1,000 servers without breaking the data.
- The multi-year fight to get native geometry support into Apache Iceberg.
- Why the next generation of models must evolve from text-based to spatially intelligent.
✅ Sign Up for Wherobots: https://wherobots.com/
✅ Learn more about Apache Sedona: https://wherobots.com/apache-sedona/
✅ What is Apache Sedona: https://wherobots.com/blog/what-is-apache-sedona/
✅ Test out SedonaDB: https://sedona.apache.org/sedonadb/latest/
✅ Connect with Jia on LinkedIn: https://www.linkedin.com/in/dr-jia-yu/ 
00:00:00 - Intro & Welcome 
00:00:51 - The Origin Story: From GeoSpark to Apache Sedona 
00:06:03 - Why Geospatial Data is "Special" (The Multimodality Problem) 
00:09:47 - When to Move to Distributed Computing? 
00:13:21 - The Secret to Maintaining a Vibrant Open Source Community
00:18:11 - The Features That Drove Adoption: Spatial SQL & Python 
00:22:35 - Deep Dive: How Spatial Partitioning Works 
00:28:57 - Why Build a Cloud-Native Platform? 
00:33:05 - The Rise of the Spatial Lakehouse & Apache Iceberg 
00:40:17 - Introducing SedonaDB: A Single-Node Engine 
00:45:10 - The Future: Why AI Needs Spatial Intelligence 
00:48:44 - Advice for Getting Started with Spatial Engineering
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

In 1963, the US Postal Service introduced "Mr. Zip" to make mail delivery faster. They never intended for those five digits to determine your insurance premiums, your home value, or your health outcomes.
In this short deep-dive, we explore how an arbitrary logistical tool became a shorthand for community and why that’s dangerous. From the misleading boundaries of Dallas, Texas, to the tragic data failures during the Flint water crisis, we uncover the real story behind the map.

Listen in to learn why it's time to move beyond the zip code and start looking at the details that actually matter.

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Whenever you’re ready, here are 3 ways I can help you:
🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/
🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

Maps have been around for thousands of years, but what they represent and how they work is changing faster than ever.
In this episode, I’m joined by Cliff Allison, who has spent more than 30 years building enterprise-scale mapping systems for governments and global organizations. Today, he leads government global sales at TomTom, helping bring modern, AI-powered mapping infrastructure to some of the most demanding use cases in the world.
We talk about how maps have evolved from static snapshots into living systems that update continuously, how open standards and collaboration made global mapping possible at scale, and why machines are now increasingly interacting with maps and with each other.
We also explore what this shift means for defense, intelligence, humanitarian response, and decision-making, and why mapping is no longer just a visualization layer, but a foundational system for understanding and predicting the world.
If you work in geospatial, data, AI, or infrastructure, this conversation will change how you think about maps.
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Whenever you’re ready, here are 3 ways I can help you:
🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/
🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

We have an incredible amount of public geospatial data—high-resolution elevation, weather forecasts, floodplain maps, real-time sensors—yet most people still can’t easily answer a simple question:
“What’s my flood risk right here, right now?”
In this episode, I’m joined by Kevin Bullock, an aerospace engineer and remote sensing expert at Development Seed, to talk about how he turned years of geospatial expertise into Hydra Atlas, a mobile app designed to make flood risk understandable and accessible for everyday users.
We explore why so much critical data remains difficult to use, how Kevin pulled together datasets from FEMA, NOAA, and USGS, and why mobile—not web—was the right platform for this problem. Kevin also shares what it was like building a geospatial app with Swift, testing real-world use cases, and designing an interface that prioritizes clarity over complexity.
This conversation goes beyond flooding. It’s about modern GIS, product thinking, open data, and what happens when geospatial professionals stop building tools for other experts and start building tools for people.
If you’re interested in geospatial product development, public data, mobile mapping, or turning complex systems into usable software, this episode is for you.
Download HydraAtlas: https://apps.apple.com/us/app/hydraatlas/id6749492232
Follow Kevin on LinkedIn: https://www.linkedin.com/in/kevbullock/
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Whenever you’re ready, here are 3 ways I can help you:
🎓 Modern GIS Accelerator: The step-by-step roadmap to master Python, Spatial SQL & Cloud workflows. Stop just "making maps" and start building spatial solutions. 👉 https://forrest.nyc/accelerator/
🧪 The Spatial Lab: Join the top 5% of geospatial professionals in our private community. Get access to exclusive courses, mentorship, and the network you need to level up. 👉  https://forrest.nyc/spatial-lab/
🧭 Career Compass: Not sure where to start? Get the fast, practical steps to land the GIS role you actually want. 👉 https://forrest.nyc/career-compass/
📰 Daily modern GIS insights: https://forrest.nyc
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc

If there’s one word to describe the past year in geospatial, it’s change.
In this solo episode, I take you behind the scenes of what I’ve been seeing, hearing, and working on across geospatial, cloud, and AI over the past year, and how those shifts are shaping what actually matters heading into 2026 .
I talk about:
- Where AI is real vs overhyped in geospatial workflows
- Why cloud-native geospatial has quietly crossed into real production systems
- How formats like GeoParquet, Iceberg, and modern compute engines are changing where spatial data lives
- Why architecture and systems thinking are becoming the most valuable skills in the industry
- The rise of power skills (not “soft skills”) across roles like data engineering, product, architecture, and leadership
- What roles are emerging, and how they actually work together in modern spatial teams
This isn’t a predictions episode built on hype. It’s a grounded look at what changed, what didn’t, and what skills and mindsets will matter most as geospatial continues to integrate with the broader data and AI ecosystem.
If you’re a GIS professional, data engineer, architect, product manager, or leader trying to understand how spatial fits into modern systems, this episode will help you frame what’s next, and how to prepare for it.
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🚀 Ready to move beyond desktop GIS?
Step into the Spatial Lab: a global community for ambitious geospatial professionals who want to break out of outdated workflows and join the top 5% of the field.
👉 Join Spatial Lab: https://forrest.nyc/spatial-lab/
🎓 Want structured, career-changing learning?
🚀 Modern GIS Accelerator: https://forrest.nyc/accelerator/
— master Python, Spatial SQL & cloud workflows in 2 weeks
🧭 Career Compass: https://forrest.nyc/career-compass/
— fast, practical steps to land the GIS role you want
🪄 AI Copilot for GIS: https://forrest.nyc/ai-copilot-for-gis/
— learn to integrate AI into your geospatial workflows & boost your productivity
📰 Weekly modern GIS insights: https://forrest.nyc
⚡️ Spots for the next live cohort and mentorship cycle are closing soon,  join now to lock in your place and momentum.
CONNECT WITH ME
📸 Instagram:  https://www.instagram.com/matt_forrest/
💼 LinkedIn: https://www.linkedin.com/in/mbforr/
📧 Newsletter: https://forrest.nyc
🌐 Website: https://forrest.nyc