Chapter 6 Entropy as a measure of agglomeration
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This chapter presents an idea of using Shannon’s entropy to measure the spatial agglomeration of points over tessellated space. This concept was presented previously forty years ago by Lews (1979), but since then it has been poorly analysed. The chapter develops this methodology and presents the statistical and economic aspects of detecting the agglomeration with entropy. Applying entropy metrics to point data requires the transformation of data to obtain the shares assigned to each point, which total 100%. As proposed by Lews (1979), one can use Voronoi tessellation, which divides the region’s area into non-overlapping tiles where point data are centroids. Shares of tiles’ areas in the total area give the weights required for entropy. Voronoi tessellation tiles approximate the point pattern effectively in the continuous space and can be used to examine the existence of agglomeration with entropy measure. Examples for presented point pattern for urban areas and regions prove it is an efficient method in spatial analysis.

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