ISI Report

Sociological phenomenology offers a lens through which to explore the lived experiences of individuals in a community, particularly in the context of neighborhood development and local culture. This paper examines how social interactions, collective memory, and spatial configurations shape the identity of a neighborhood. By integrating new technological approaches such as attention mechanisms in big data analysis and large language models, this research investigates how AI, particularly generative AI, can assist in the understanding of local culture and neighborhood development. Through a sociological phenomenological lens, the study explores how AI in business and prompt engineering might transform our insights into local culture and the social dynamics of urban and rural neighborhoods. This paper explores the intersection of sociological phenomenology and neighborhood development, focusing on how local culture and social dynamics shape the identity and transformation of communities. Through qualitative ethnographic methods and the integration of advanced data analytics techniques, such as attention mechanisms in big data analysis and large language models, this study examines how technological tools can enhance our understanding of neighborhood evolution and cultural narratives. By leveraging generative AI and prompt engineering, the research investigates how these technologies can simulate and predict the socio-cultural changes within neighborhoods, providing valuable insights for urban planning, community development, and policy-making. The findings underscore the potential for AI to inform a more culturally sensitive and inclusive approach to neighborhood growth, while preserving the lived experiences and identities of residents.