Abstract: 

In today's cloud-native era, effectively harnessing the potential of spatial data science at scale remains a significant challenge. While leading data warehouses provide some level of spatial data support, they often lack the advanced analytical capabilities necessary for various geospatial use cases. This talk aims to address this gap by providing an overview of best practices for analyzing and modeling spatial data, with a specific focus on scalable and low-code solutions within the CARTO cloud-native environment. We will present real world examples from the telco industry, from big data processing and visualization to network planning. By leveraging simple SQL queries, attendees will discover how easy it is to combine spatial data at scale, analyze and model spatial patterns and processes, and to explore interactively and with the help of generative AI large data volumes both from vector and raster sources.

Bio: 

Giulia Carella is a Data Scientist at CARTO. She holds a PhD in Applied Statistics and has experience in the development and application of statistics and machine learning methods for spatio-temporal data, with applications ranging from climate science to spatial demography.