| id |
caadria2020_384 |
| authors |
Patt, Trevor Ryan |
| year |
2020 |
| title |
Spectral Clustering for Urban Networks |
| source |
D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 91-100 |
| doi |
https://doi.org/10.52842/conf.caadria.2020.2.091
|
| summary |
As planetary urbanization accelerates, the significance of developing better methods for analyzing and making sense of complex urban networks also increases. The complexity and heterogeneity of contemporary urban space poses a challenge to conventional descriptive tools. In recent years, the emergence of urban network analysis and the widespread availability of GIS data has brought network analysis methods into the discussion of urban form. This paper describes a method for computationally identifying clusters within urban and other spatial networks using spectral analysis techniques. While spectral clustering has been employed in some limited urban studies, on large spatialized datasets (particularly in identifying land use from orthoimages), it has not yet been thoroughly studied in relation to the space of the urban network itself. We present the construction of a weighted graph Laplacian matrix representation of the network and the processing of the network by eigen decomposition and subsequent clustering of eigenvalues in 4d-space.In this implementation, the algorithm computes a cross-comparison for different numbers of clusters and recommends the best option based on either the 'elbow method,' or by "eigen gap" criteria. The results of the clustering operation are immediately visualized on the original map and can also be validated numerically according to a selection of cluster metrics. Cohesion and separation values are calculated simultaneously for all nodes. After presenting these, the paper also expands on the 'silhouette' value, which is a composite measure that seems especially suited to urban network clustering.This research is undertaken with the aim of informing the design process and so the visualization of results within the active 3d model is essential. Within the paper, we illustrate the process as applied to formal grids and also historic, vernacular urban fabric; first on small, extract urban fragments and then over an entire city networks to indicate the scalability. |
| keywords |
Urban morphology; network analysis; spectral clustering; computation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (4,152,122 bytes) |
| references |
Content-type: text/plain
|
Agryzkov, T, Tortosa, L, Vincent, JF and Wilson, R (2019)
 A Centrality Measure for Urban Networks Based on the Eigenvector Centrality Concept
, Environment and Planning B, 46(4), pp. 668-680
|
|
|
|
|
Arbelaitz, O, Gurratxaga, I, Muguerza, J, Pérez, JM and Perona, I (2013)
An Extensive Comparative Study of Cluster Validity Indices
, Pattern Recognition, 46(1), pp. 243-256
|
|
|
|
|
Arthur, D and Vassilvitskii, S (2007)
K-Means++: The Advantages of Careful Seeding
, Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, pp. 1027-1035
|
|
|
|
|
Batty, M (2004)
A New Theory of Space Syntax
, CASA Working Papers Series(75)
|
|
|
|
|
Boulmakoul, B, Besri, Z, Karim, L, Boulmakoul, A and Lbath, A (2017)
Combinatorial connectivity and spectral graph analytics for urban public transportation system
, Transportation Research Procedia, 27, pp. 1154-1162
|
|
|
|
|
Chung, FRK (1996)
Spetral Graph Theory
, American Mathematical Society, Providence
|
|
|
|
|
Hillier, B and Hanson, J (1997)
The Reasoning Art: Or, The Need for an Analytical Theory of Architecture
, Proceedings of the 1st International Space Syntax Symposium, London, pp. 1:1-5
|
|
|
|
|
Marcus, L, Westin, S and Liebst, LS (2013)
Network Buzz: Conception and Geometry of Networks in Geography, Architecture, and Sociology
, Proceedings of the 9th International Space Syntax Symposium, Seoul, pp. 68:1-13
|
|
|
|
|
Ng, AY, Jordan, MI and Weiss, Y (2001)
On Spectral Clustering: Analysis and an Algorithm
, Advances in Neural Information Processing Systems, 14, pp. 849-856
|
|
|
|
|
Nourian, P, Rezvani, S, Sariyildiz, S and van der Hoeven, F (2016)
Spectral Modelling for Spatial Network Analysis
, SimAUD, London
|
|
|
|
|
Patt, TR (2018)
Multiagent approach to temporal and punctual urban redevelopment in dynamic, informal contexts
, IJAC, 16(3), pp. 199-211
|
|
|
|
|
Rousseeuw, PJ (1987)
Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis
, Journal of Computational and Applied Mathematics, 20(November), pp. 53-65
|
|
|
|
|
Sevtsuk, A and Mekonnen, M (2012)
Urban Network Analysis: A New Toolbox for ArcGIS
, Revue Internationale de Géomatique, 22(2), pp. 287-305
|
|
|
|
|
Shi, J and Malik, J (2000)
Normalized Cuts and Image Segmentation
, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), pp. 888-905
|
|
|
|
|
von Luxburg, U (2007)
A Tutorial on Spectral Clustering
, Statistics and Computing, 17(4), pp. 395-416
|
|
|
|
| last changed |
2022/06/07 07:59 |
|
