Accurately defining urbanity is essential for effective urban analysis and spatial planning, but conventional approaches often fail to capture the complexity of dynamic urban systems. Many existing studies assess urbanity alone through a single approach, like morphologically or socio-economically, and many countries continue to rely on rigid administrative classifications that inadequately reflect functional and socio-spatial realities. As a result, emerging and transitional urban areas are frequently misidentified, leading to planning inefficiencies and resource misallocation. In response, this study conceptualizes urbanity as a multidimensional continuum and develops an integrated analytical framework to better capture true urban characteristics. The framework combines two complementary indices, a Morphological Urbanity Index (MUI) derived from density, diversity, and accessibility, and a Lifestyle-Based Urbanity Index (LUI) constructed using census-based socio-economic indicators, service accessibility measures, and geospatial proxies such as night-time lights and point-of-interest density. All indicators were normalized and integrated within a GIS (Geographic Information System) environment using raster-based overlay techniques and fuzzy logic modeling. The framework was empirically tested in Kandy District, Sri Lanka, and validated through spatial comparison with existing administrative classifications. Results indicate that urbanity differs when assessed through form-based and lifestyle-based indices. Integrating these dimensions produces a more comprehensive understanding by capturing both spatial structure and lived experience. The resulting urbanity continuum reveals varying intensities of urban characteristics across space, challenging the conventional urban–rural dichotomy. Comparative analysis with administrative boundaries identifies significant spatial mismatches, exposing patterns of hidden urbanization in officially classified rural areas. The study demonstrates that measuring urbanity through an integrated multidimensional framework provides a more realistic representation of contemporary urban conditions. Although tested in Kandy District, the proposed approach is scalable and adaptable to diverse global contexts, supporting improved urban classification and evidence-based spatial planning.