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Spatial clustering method for geographic data

Osaragi, Toshihiro; (2002) Spatial clustering method for geographic data. (CASA Working Papers 41). Centre for Advanced Spatial Analysis (UCL): London, UK. Green open access

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In the process of visualizing quantitative spatial data, it is necessary to classify attribute values into some class divisions. In a previous paper, the author proposed a classification method for minimizing the loss of information contained in original data. This method can be considered as a kind of smoothing method that neglects the characteristics of spatial distribution. In order to understand the spatial structure of data, it is also necessary to construct another smoothing method considering the characteristics of the distribution of the spatial data. In this paper, a spatial clustering method based on Akaike’s Information Criterion is proposed. Furthermore, numerical examples of its application are shown using actual spatial data for the Tokyo Metropolitan area.

Type: Working / discussion paper
Title: Spatial clustering method for geographic data
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.casa.ucl.ac.uk/working_papers/paper41.p...
Language: English
Keywords: spatial data, space cluster, Quadtree, AIC (Akaike’s Information Criterion), information loss, visualization, classification
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/253
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