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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Batty, M. (1974). Spatial entropy. Geographical Analysis, 6(1), 1–31.
Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd ed.). Hoboken, NJ: Wiley.
Hagen-Zanker, A., & Jin, Y. (2012). A new method of adaptive zoning for spatial interaction models. Geographical Analysis, 44(4), 281–301.
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.
Jaynes, E. T. (1982). On the rationale of maximum-entropy methods. Proceedings of the IEEE, 70(9), 939–952.
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.
Openshaw, S., & Rao, L. (1995). Algorithms for reengineering 1991 census geography. Environment and Planning A, 27, 425–446.
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.
Polani, D. (2002). Measures for the organization of self-organizing maps. In Self-organizing neural networks (pp. 13–44). Heidelberg: Physica-Verlag.
Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.
Vaz, E. (2018). Regional intelligence: A new kind of GIScience. Habitat International, 72, 1–108. https://doi.org/10.1016/j.habitatint.2017.11.015
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Vaz, E., Bandur, D. (2018). Merging Entropy in Self-Organisation: A Geographical Approach. In: Pinto, H., Noronha, T., Vaz, E. (eds) Resilience and Regional Dynamics. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-95135-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-95135-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95134-8
Online ISBN: 978-3-319-95135-5
eBook Packages: Economics and FinanceEconomics and Finance (R0)
Keywords
- Mutual Information Network
- Geographic Information System Development
- Census Division Level
- Spatial Entropy
- Neuron Grid
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
