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Complexity and Spatial Networks pp 257–271Cite as

Spatial and Commuting Networks

A Unifying Perspective

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Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

A wide literature is devoted to the study of the relevance of space, encompassing several fields and disciplines, such as geography, economics, epidemiology, environmental and regional sciences. For example, space-time modelling has been a relevant focus of research in spatial economics starting from Hägerstrand (1967) and Wilson (1967, 1970). While the former paid attention to the modelling of spatial diffusion phenomena, the latter unified movements of spatial flows under the umbrella of statistical and information theory, by means of spatial interaction models. In these models, the relevance of spatial structure emerged in the associated cost/impedance functions. In parallel, starting from Zipf (1932) and Simon (1955), the importance of spatial structures (homogeneous or heterogeneous) has been discussed extensively in the literature, by focusing on the relationships between urban growth, agglomeration economies, and commuting costs (see, among others, Krugman 1991; Rossi-Hansberg and Wright 2006). A point of concern is that, in these spatial (growth and interaction) models, the effects of spatial topology and connectivity are only implicitly included, but never explicitly considered and discussed.

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Notes

  1. 1.

    See also Chap 19 by Reggiani, in this volume.

  2. 2.

    Since the data are directly gathered at the single firm level, it is reasonable to expect low and non-systematic measurement errors.

  3. 3.

    The districts are classified as follows: (1) central cities in regions with urban agglomerations; (2) highly urbanised districts in regions with urban agglomerations; (3) urbanised districts in regions with urban agglomerations; (4) rural districts in regions with urban agglomerations; (5) central cities in regions with tendencies towards agglomeration; (6) highly urbanised districts in regions with tendencies towards agglomeration; (7) rural districts in regions with tendencies towards agglomeration; (8) urbanized districts in regions with rural features; and (9) rural districts in regions with rural features.

  4. 4.

    Our result would vary if we imposed a minimum threshold on the flows associated with each network link. A threshold set at three would support a finding of scale-free characteristics of the commuting network.

  5. 5.

    We employ the regime multicriteria method Hinloopen and Nijkamp (1990). In detail, three scenarios have been considered: (a) equal weights to all criteria; (b) ascending weights; and (c) descending weights. A final MCA of the rankings obtained provides the final results. We assume the hypothesis of no correlation between the criteria employed in the MCA.

  6. 6.

    The two macro-criteria employed here clearly identify two different types of phenomena: Spearman's correlation between the rankings resulting from the spatial and connectivity MCAs is equal to –0.369 for 1995 and to –0.311 for 2005. This is confirmed by the cross-correlations between the spatial and the connectivity criteria, which range – in absolute values – from 0.066 to 0.501.

  7. 7.

    In this context, had inflows and outflows been employed as criteria within the spatial mobility macro-criterion, a ranking similar to the one obtained for the connectivity macro-criterion would have emerged.

  8. 8.

    If high-degree nodes were found to be also connected to each other, then highly interconnected clusters could emerge, possibly leading, according to Holme (2005), to a core-periphery network structure (Chung and Lu 2002). Most importantly, Holme shows that transportation networks (more generically, geographically-embedded networks) tend to share this characteristic.

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Acknowledgments

The present chapter is adapted from a previous paper by the same authors titled: “The Evolution of the Commuting Network in Germany: Spatial and Connectivity Patterns”, published in Journal of Transport and Land Use (2009) (available at http://www.jtlu.org). The authors wish to thank Pietro Bucci for his help in the empirical application regarding the connectivity analysis and multicriteria evaluation.

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Authors and Affiliations

  1. Institute for Economic Research (IRE), University of Lugano, Lugano, Switzerland

    Roberto Patuelli

  2. The Rimini Centre for Economic Analysis, Rimini, Italy

    Roberto Patuelli

Authors
  1. Roberto Patuelli
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  2. Aura Reggiani
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  3. Peter Nijkamp
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  4. Franz-Josef Bade
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Editors and Affiliations

  1. Dipto. Scienze Economiche, Università di Bologna, Piazza Scaravilli 2, Bologna, 40126, Italy

    Aura Reggiani

  2. Dept. Spatial Economics, Free University, De Boelelaan 1105, Amsterdam, 1081 HV, Netherlands

    Peter Nijkamp

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Patuelli, R., Reggiani, A., Nijkamp, P., Bade, FJ. (2009). Spatial and Commuting Networks. In: Reggiani, A., Nijkamp, P. (eds) Complexity and Spatial Networks. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01554-0_18

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  • DOI: https://doi.org/10.1007/978-3-642-01554-0_18

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