The study explores the use of Graph Neural Networks (GNN) to predict local cultural characteristics by combining various sources of information about neighborhoods. Results show that incorporating group profiles and area socio-economic data improves predictions significantly. The research highlights the importance of leveraging online review data like Yelp for studying neighborhood dynamics.
Early urban research struggled with understanding the complexity of neighborhoods, but advancements in technology now allow for a more comprehensive analysis. The study emphasizes the significance of considering relational dynamics between neighborhoods and their cultural dimensions. By utilizing GNNs, researchers can gain insights into predicting future neighborhood states based on past conditions.
The paper discusses the challenges faced by traditional quantitative research methods in incorporating full neighborhood contextuality. It also delves into the potential of machine learning models like GNNs to predict outcomes such as gentrification and crime spread based on mobility patterns and group profiles.
Overall, the study showcases how advanced technologies like GNNs can revolutionize urban research by providing a deeper understanding of neighborhood dynamics and cultural evolution.
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by Thiago H Sil... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.17905.pdfDeeper Inquiries