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Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases
Author(s) -
Adnan Muhammad,
Longley Paul A,
Singleton Alex D,
Brunsdon Chris
Publication year - 2010
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/j.1467-9671.2010.01197.x
Subject(s) - cluster analysis , computer science , data mining , range (aeronautics) , cluster (spacecraft) , data science , process (computing) , database , geography , machine learning , engineering , programming language , aerospace engineering , operating system
Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.