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Self-Organizing Maps and the US Urban Spatial Structure

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Listed:
  • Daniel Arribas-Bel
  • Charles R Schmidt

Abstract

In this paper we consider urban spatial structure in US cities using a multidimensional approach. We select six key variables (commuting costs, density, employment dispersion and concentration, land-use mix, polycentricity, and size) from the urban literature and define measures to quantify them. We then apply these measures to 359 metropolitan areas from the 2000 US Census. The adopted methodological strategy combines two novel techniques for the social sciences to explore the existence of relevant patterns in such multidimensional datasets. Geodesic self-organizing maps (SOM) are used to visualize the whole set of information in a meaningful way, while the recently developed clustering algorithm of the max- p is applied to draw boundaries within the SOM and analyze which cities fall into each of them.

Suggested Citation

  • Daniel Arribas-Bel & Charles R Schmidt, 2013. "Self-Organizing Maps and the US Urban Spatial Structure," Environment and Planning B, , vol. 40(2), pages 362-371, April.
    • Handle: RePEc:sae:envirb:v:40:y:2013:i:2:p:362-371
    DOI: 10.1068/b37014
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    References listed on IDEAS

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    1. Manfred M. Fischer & Arthur Getis (ed.), 2010. "Handbook of Applied Spatial Analysis," Springer Books, Springer, number 978-3-642-03647-7, January.
    2. Levinson, David & El-Geneidy, Ahmed, 2009. "The minimum circuity frontier and the journey to work," Regional Science and Urban Economics, Elsevier, vol. 39(6), pages 732-738, November.
    3. John I. Carruthers & Selma Lewis & Gerrit‐Jan Knaap & Robert N. Renner, 2010. "Coming undone: A spatial hazard analysis of urban form in American metropolitan areas," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 65-88, March.
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