Javier Alfaro

Gentrification is an urban phenomenon marked by socioeconomic shifts that can displace long-term residents and increase inequality. Accurate measurement is essential for effective urban planning and equitable development. Traditional reliance on census data is costly, slow, and lacks the spatial and temporal resolution needed to detect neighborhood-level changes in real time. This study addresses these challenges by combining open satellite imagery with machine learning techniques to quantify gentrification more effectively. By analyzing high-resolution imagery, we detect physical changes—such as shifts in building density, rooftop materials, and green spaces—that are linked to gentrification but often overlooked by census- based approaches. Using the Greater London Area as a case study, our method improves measurement accuracy by up to 8%, achieving a balanced accuracy of 77% across 4,085 neighborhoods. Even a small improvement in accuracy can enable better identification of at-risk neighborhoods, helping policymakers intervene before displacement pressures become irreversible.

This data study group challenge focused on extracting information from legacy offshore seismic data. The British Geological Survey has an archive of thousands of images of scanned paper records that contain information about the marine subsurface, but these images are time-consuming to investigate manually. This challenge aimed to investigate methods to automatically extract information from these images, particularly identifying the presence of shallow gas below the seabed using patterns in the images. Shallow gas is a hazard that can threaten undersea cables, pipelines, and create problems for siting wind turbines. Knowing the location of hazardous gas will help to de-risk the installation of wind farms, a crucial infrastructure to achieve NetZero.