At Iggy we provide easy access to hundreds of geospatial features to help companies make sense of ‘place’. We believe that incorporating ‘place’ into data science and machine learning pipelines can have a huge impact on predictive capabilities in a wide range of fields such as travel, real estate, healthcare, logistics and many more.

Traditionally this data was accessible in a tabular form, but recently we have been experimenting with converting our tabular data into a graph representation, and applying graph machine learning to build derived products. This allows us to leverage the power of graphs and to more effectively model the relationships between different entities in our data.

In this talk we will present: 1. What is geospatial data? 2. Why are we interested in graph representations of geospatial data? 3. What do our graph representations look like? 4. How are we applying graph machine learning? 5. What are some use cases and derived products that we are building using graph machine learning?

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