Abstract
Urban mobility is a complex and ever-changing landscape. Understanding the flow of people and vehicles through city streets is crucial for city planners, transportation engineers, and researchers. With urban populations growing, the need to optimize transportation systems and enhance urban livability has never been more pressing.
We’ve been working on combining the strengths of MovingPandas—a powerful Python library for movement data analysis—and CARTO, a user-friendly platform for scalable spatial analysis and visualization. Together, within the Snowflake Lakehouse environment, we offer a groundbreaking framework for analyzing mobility patterns. This integration enables users to uncover hidden traffic hotspots, optimize transportation networks, and ultimately make smarter urban planning decisions.