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| Transit-Oriented Development Analysis× | Urban Network Analysis× | |
|---|---|---|
| Field | Urban Studies | Urban Studies |
| Family | Process / pipeline | Process / pipeline |
| Year of origin≠ | 1999 | 2012 |
| Originator≠ | Luca Bertolini | Andres Sevtsuk & Michael Mekonnen |
| Type≠ | Diagnostic model of development around public-transport nodes | Graph-based centrality analysis of spatial urban networks |
| Seminal source≠ | Bertolini, L. (1999). Spatial development patterns and public transport: the application of an analytical model in the Netherlands. Planning Practice & Research, 14(2), 199–210. DOI ↗ | Sevtsuk, A., & Mekonnen, M. (2012). Urban network analysis: A new toolbox for ArcGIS. Revue Internationale de Géomatique, 22(2), 287–305. DOI ↗ |
| Aliases | TOD Analysis, Node-Place Model, Transit Node Assessment, Station Area Development Analysis | UNA Toolbox, Spatial Network Centrality, Building-Level Network Analysis, Street Network Centrality Analysis |
| Related | 4 | 4 |
| Summary≠ | Transit-oriented development (TOD) analysis evaluates how well the land around public-transport stations supports compact, mixed-use, walkable development that feeds and is fed by transit. Its analytical backbone is Luca Bertolini's 1999 node–place model, which scores every station area on two axes — its value as a transport node and its value as a place of activity — and diagnoses whether the two are in balance. Combined with the classic density, diversity, and design dimensions and with network measures of access to stations, the approach identifies which station areas are under-developed, over-stressed, or ripe for intensification. | 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. |
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