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Merging Entropy in Self-Organisation: A Geographical Approach

  • Eric VazEmail author
  • Dragos Bandur
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Spatially-referenced data has achieved a unique place in regional science over the last decades. Much of the evolution Geographic Information Systems and Science have witnessed is due to the advances in the field of geocomputation and categorization of social and economic phenomena over geographical space. One of the traditional ways of analyzing socioeconomic data is by using rigid administrative boundaries, where internal structure, as well as the distribution of phenomena, lead to the disruption of their internal structure. This chapter assesses a more natural approach for data aggregation by using self-organizing maps. It aims to extend the debate on mutual information as well as spatial data, showing how data aggregation directly affects entropy values within the correlation of regions. This supports the identification of a new method that registers stronger correlated areas through a combination of entropy and self-organization, which offers new insights into topological innovation of spatially-explicit data and its integration in the field of regional science.

References

  1. Batty, M. (1974). Spatial entropy. Geographical Analysis, 6(1), 1–31.CrossRefGoogle Scholar
  2. Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
  3. Hagen-Zanker, A., & Jin, Y. (2012). A new method of adaptive zoning for spatial interaction models. Geographical Analysis, 44(4), 281–301.CrossRefGoogle Scholar
  4. Hausser, J., & Strimmer, K. (2009). Entropy inference and the James-stein estimator, with application to nonlinear gene association networks. The Journal of Machine Learning Research, 10, 1469–1484.Google Scholar
  5. Jaynes, E. T. (1982). On the rationale of maximum-entropy methods. Proceedings of the IEEE, 70(9), 939–952.CrossRefGoogle Scholar
  6. Openshaw, S. (1977). A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modeling. Transactions of the Institute of British Geographers, 2, 459–472.CrossRefGoogle Scholar
  7. Openshaw, S., & Rao, L. (1995). Algorithms for reengineering 1991 census geography. Environment and Planning A, 27, 425–446.CrossRefGoogle Scholar
  8. Parenteau, M. P., & Sawada, M. C. (2011). The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health. International Journal of Health Geographics, 10(1), 1–15.CrossRefGoogle Scholar
  9. Polani, D. (2002). Measures for the organization of self-organizing maps. In Self-organizing neural networks (pp. 13–44). Heidelberg: Physica-Verlag.CrossRefGoogle Scholar
  10. Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.CrossRefGoogle Scholar
  11. Vaz, E. (2018). Regional intelligence: A new kind of GIScience. Habitat International, 72, 1–108.  https://doi.org/10.1016/j.habitatint.2017.11.015CrossRefGoogle Scholar
  12. Vaz, E., Cusimano, M., & Hernandez, T. (2015). Land use perception of self-reported health: Exploratory analysis of anthropogenic land use phenotypes. Land Use Policy, 46, 232–240.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Geography and Environmental StudiesRyerson UniversityTorontoCanada

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