Linganisha mbinu

	Walkability Index	Street Network Analysis
Nyanja	Urban Studies	Urban Studies
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2010	2017
Mwanzilishi	Lawrence Frank and colleagues	Geoff Boeing (OSMnx); graph-theoretic street analysis tradition
Aina	Composite neighbourhood index of how supportive the built environment is of walking	Graph-theoretic measurement of street-network structure and connectivity
Chanzo asilia	Frank, L. D., Sallis, J. F., Saelens, B. E., Leary, L., Cain, K., Conway, T. L., & Hess, P. M. (2010). The development of a walkability index: Application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, 44(13), 924–933.	Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.
Majina mbadala	Frank Walkability Index, Walk Score, Neighborhood Walkability Index, Pedestrian Environment Index	Street Pattern Analysis, Road Network Metrics, Urban Street Connectivity Analysis, Configurational Street Analysis
Zinazohusiana	4	4
Muhtasari	A walkability index measures how well a neighbourhood's built environment supports walking, by combining a small set of land-use and street-design variables into a single score. The influential index developed by Lawrence Frank and colleagues sums standardized measures of residential density, land-use mix, street connectivity, and retail floor-area ratio, giving extra weight to intersection density because connected street grids most strongly enable walking. Consumer tools such as Walk Score popularized the same idea by scoring an address on the proximity and variety of nearby destinations, making walkability a routine input to planning, public health, and real-estate analysis.	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.