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Community detection in spatial networks: Inferring land use from a planar graph of land cover objects

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journal contribution
posted on 06.02.2013, 14:05 by Alexis J. Comber, Chris F. Brunsdon, C.J.Q. Farmer
This paper applies three algorithms for detecting communities within networks. It applies them to a network of land cover objects, identified in an OBIA, in order to identify areas of homogenous land use. Previous research on land cover to land use transformations has identified the need for rules and knowledge to merge land cover objects. This research shows that Walktrap, Spinglass and Fastgreedy algorithms are able to identify land use communities but with different spatial properties. Community detection algorithms, arising from graph theory and networks science, offer methods for merging sub-objects based on the properties of the network. The use of an explicitly geographical network also identifies some limitations to network partitioning methods such as Spinglass that introduce a degree of randomness in their search for community structure. The results show such algorithms may not be suitable for analysing geographic networks whose structure reflects topological relationships between objects. The discussion identifies a number of areas for further work, including the evaluation of different null statistical models for determining the modularity of geographic networks. The findings of this research also have implications for the many activities that are considering social networks, which increasingly have a geographical component.

History

Citation

International Journal of Applied Earth Observation and Geoinformation, 2012, 18 (1), pp. 274-282

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Geography/GIS

Version

AM (Accepted Manuscript)

Published in

International Journal of Applied Earth Observation and Geoinformation

Publisher

Elsevier

issn

1569-8432

Copyright date

2012

Available date

06/02/2013

Publisher version

http://www.sciencedirect.com/science/article/pii/S0303243412000220

Language

en