<p>This paper explores the application of fuzzy set precepts within the ambit of urban and regional studies, which we term fuzzy urban and regional sets (FURS). Drawing on census tract data from the Dallas–Fort Worth–Arlington metropolitan statistical area (DFW), we illustrate how urban phenomena can be represented as fuzzy sets characterized by degrees of membership rather than binary classifications. This approach shifts the analytical focus from discrete categorization to gradations in socioeconomic and spatial characteristics. Such a shift is important because conventional "crisp" sets often obscure meaningful variation within and across urban systems, and the fuzzy set framework enables a more nuanced interpretation of these patterns. We introduce the concept of fuzzy clubs and apply fuzzy set operators such as intersection, union, and complement to examine their relationships in the DFW region. We further develop measures of fuzzy subsethood and entropy, compare these to conventional entropy metrics, and extend the framework to spatial accessibility using distance-based membership functions. These examples demonstrate how FURS provide a more flexible and intuitive framework for representing and comparing complex urban and regional phenomena, with potential applications across a wide range of research contexts. This paper contributes a conceptual bridge between fuzzy set theory and urban and regional analysis, demonstrating how fuzzy set precepts can be operationalized to generate operational insights into complex urban phenomena.</p>