Abstract
Modern land planning tools, such as cadastral and zoning maps, reduce the complexity of multidimensional social and spatial networks to abstract, homogeneous administrative and market assets. These tools predominantly serve bureaucratic and capitalist interests, often sidelining the needs of those who live, maintain, and benefit from these systems. This reductionist approach restricts citizens’ adaptive and innovative capacities to meet everyday human needs through negotiated spaces and practices, especially in the face of uncertainty and external disruptions. This research first contrasts the networked qualities of various archetypal urban fabrics in the United States, focusing on those that precede or challenge modern, Cartesian, and Euclidean land-use practices. Using a range of graph-theoretical methods, including Bill Hillier’s space syntax analysis and social network analysis, alongside qualitative social theory, particularly Manfred Max-Neef’s culturally grounded theory of fundamental human needs, the research proposes a framework for evaluating and comparing urban neighborhoods and localized contexts. Second, this study explores alternative mapping methods adapted from network science and ecological models. These approaches, supported by open-source spatial data and software, shift the focus from rigid categorical delineations to nested relational structures. The proposed methods aim to reveal vital, often hidden, aspects of local provisioning systems, offering a bottom-up approach to understanding and intervening in urban environments. The research aims to provide local stakeholders with approachable yet sophisticated conceptual and technical frameworks for practical, place-based, consensus-driven, bottom-up approaches to building localized and shared urban resilience and regenerative capacity.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2025 Special Focus— Sharing Practices and Sustainable Urban Fabrics
KEYWORDS
Urban Resilience, Regenerative Design, Space Syntax, Social Network Analysis
