Urban street flooding presents significant challenges for metropolitan areas like New York City, particularly in the face of intense rain events. In this workshop, we will explore the causes and variability of streetflooding using NYC Open Data and machine learning techniques. Building on prior research, we aim to reproduce findings on flood risk factors while incorporating updated data from NYC 311 service requests data and other sources such as the U.S. Census. This approach will enhance our understanding of how socio-economic and infrastructural factors contribute to flooding, offering new insights into the spatial dynamics of flood risk.
The workshop will focus on three key topics: data cleaning to process NYC 311 flood reports and supplementary datasets, exploratory data analysis to identify patterns in flood risk factors, and predictive modeling using Random Forest regression. By analyzing how factors like land features, topography, and population dynamics influence flood risk, participants will gain hands-on experience with urban flood modeling techniques.
Four students from the Spring 2025 Introduction to Data Science course at UConn will present their projects in sequential order, each focusing on one aspect of the core topics. The presentations will be followed by a Q&A session, providing participants with an opportunity to engage with the presenters and explore the findings in greater depth.
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