Merging Entropy in Self-Organisation: A Geographical Approach

Eric Vaz () and Dragos Bandur
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Eric Vaz: Ryerson University
Dragos Bandur: Ryerson University

Chapter Chapter 9 in Resilience and Regional Dynamics, 2018, pp 171-186 from Springer

Abstract: 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.

Keywords: Mutual Information Network; Geographic Information System Development; Census Division Level; Spatial Entropy; Neuron Grid (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-319-95135-5_9

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DOI: 10.1007/978-3-319-95135-5_9

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