We analyzed Philly street scenes and identified signs of gentrification using machine learning trained on longtime residents’ observations
Publish Date: 2026-03-30 08:38:00
Source Domain: theconversation.com
- New high-rise, modern apartment buildings that starkly contrast with traditional row homes are key signs of gentrification in Philadelphia, often described by longtime residents as “out of place” and lacking architectural style.
- Researchers at Drexel and Temple universities developed a method to map gentrification using residents’ descriptions and analysis of Google Street View images and machine learning techniques.
- Focus groups revealed that longtime residents could pinpoint visual cues associated with gentrification, such as changes in building design, materials, colors, and landscaping, corroborated by historical Street View data.
- They used a deep mapping AI model to determine differences in building scenery from pre- and post-gentrification time periods, achieving about 84% accuracy in identifying gentrified areas.
- Researchers emphasize the importance of transparency in AI models that predict gentrification, utilizing techniques like explainable AI (XAI) to help understand the model’s decision-making process and ensure its insights are relevant and accurate.
