Salīdzināt metodes

	Street Network Analysis	Urban Sprawl Measurement
Nozare	Urban Studies	Urban Studies
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2017	2014
Autors	Geoff Boeing (OSMnx); graph-theoretic street analysis tradition	Reid Ewing & Shima Hamidi (building on Galster et al.)
Tips	Graph-theoretic measurement of street-network structure and connectivity	Composite index combining multiple dimensions of urban form into a sprawl/compactness score
Pirmavots	Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.	Ewing, R., & Hamidi, S. (2015). Compactness versus sprawl: A review of recent evidence from the United States. Journal of Planning Literature, 30(4), 413–432.
Citi nosaukumi	Street Pattern Analysis, Road Network Metrics, Urban Street Connectivity Analysis, Configurational Street Analysis	Sprawl Index, Compactness Index of Sprawl, Ewing Sprawl Index, Composite Sprawl Measure
Saistītās	4	4
Kopsavilkums	Street network analysis treats a city's streets as a mathematical graph — intersections as nodes, street segments as edges — and measures its structure with graph-theoretic indicators of connectivity, density, centrality, and efficiency. From this representation come the metrics that distinguish a permeable grid from a tree-like cul-de-sac suburb: intersection density, average node degree, the share of dead-ends, betweenness centrality, and circuity (how much longer network routes are than straight lines). Tools such as Geoff Boeing's OSMnx made it routine to download, model, and analyse the street network of any place on Earth from OpenStreetMap, turning street-pattern analysis into a reproducible, comparative science of urban form.	Urban sprawl measurement quantifies how compact or sprawling a metropolitan region is by combining several distinct dimensions of urban form into a single composite index. The dominant approach, developed by Reid Ewing, Shima Hamidi and colleagues, captures four factors — development density, land-use mix, activity centering, and street-network connectivity — and folds standardized indicators of each into one score, calibrated so the average region equals 100 and higher values mean greater compactness. Because sprawl is multidimensional, no single variable such as density adequately describes it, which is why the composite-index strategy has become the standard for comparing regions and linking form to outcomes.