Integrating Machine Learning in Urban Pedagogy: Addressing Homelessness in Skid Row

Abstract

This paper investigates the application of machine learning in urban and architectural education, with a focus on addressing homelessness in Skid Row, Los Angeles. It presents a case study of an urban design studio utilizing data-driven methods to propose transitional housing solutions, emphasizing the importance of design in the context of social justice. The study explores the use of machine learning and digital cartography for a detailed analysis of Skid Row’s dense homeless population, offering students a thorough insight into urban challenges. The research also identifies the complexities involved in integrating these technologies into educational frameworks, including issues with data accuracy, technical hurdles, and ethical considerations. The paper concludes by advocating for an interdisciplinary, data-informed, and socially conscious approach in architectural and urban design education, highlighting its necessity in preparing students to effectively tackle contemporary urban problems.