Compară metode

	15-Minute City Analysis	Urban Network Analysis
Domeniu	Urban Studies	Urban Studies
Familie	Process / pipeline	Process / pipeline
Anul apariției	2021	2012
Autorul original	Carlos Moreno	Andres Sevtsuk & Michael Mekonnen
Tip	Descriptive proximity assessment of daily needs by active travel	Graph-based centrality analysis of spatial urban networks
Sursa seminală	Moreno, C., Allam, Z., Chabaud, D., Gall, C., & Pratlong, F. (2021). Introducing the "15-Minute City": Sustainability, resilience and place identity in future post-pandemic cities. Smart Cities, 4(1), 93–111.	Sevtsuk, A., & Mekonnen, M. (2012). Urban network analysis: A new toolbox for ArcGIS. Revue Internationale de Géomatique, 22(2), 287–305.
Denumiri alternative	Fifteen-Minute City Analysis, Chrono-Urbanism Analysis, Proximity Index Analysis, Quarter-Hour City Assessment	UNA Toolbox, Spatial Network Centrality, Building-Level Network Analysis, Street Network Centrality Analysis
Înrudite	4	4
Rezumat	15-minute city analysis assesses how many of life's daily needs — shops, schools, healthcare, work, recreation — residents can reach within a short walk or bike ride, typically fifteen minutes, from their homes. Articulated by Carlos Moreno in 2021 under the banner of chrono-urbanism, the concept reorients planning around proximity and time rather than mobility and distance. The analysis operationalizes it by computing walk or cycle isochrones around residential locations and scoring how completely the essential categories of urban functions fall within reach, producing proximity indices that can be mapped, compared across neighbourhoods, and weighted by population.	Urban network analysis treats a city as a spatial graph of streets and buildings and measures the centrality of each location — how reachable, how central, and how well-connected it is along the actual travel network. Formalized in the Urban Network Analysis toolbox by Andres Sevtsuk and Michael Mekonnen in 2012, it differs from generic network science by weighting graph nodes with real urban data such as building floor area or population and by computing centralities within bounded search radii. The result is a set of metrics — reach, gravity, betweenness, closeness, straightness — that quantify the structural role of every building or street segment in the urban fabric.