A spatially aware method for mapping movement-based and place-based regions from spatial flow networks
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Community detection (CD) is a frequent method for analysing flow networks in geography. It allows us to partition the network into a set of densely interconnected regions, called communities. We introduce a new technique for including geographical weighting into existing methods for detecting spatially coherent communities. We take a link-based CD algorithm and adjust it to incorporate geographical weighting. We call this approach geographically weighted community detection (GWCD). Our method is demonstrated on two case studies of commonly encountered flow networks: commuter flows and taxi pick-up/drop-off flows. Further, we test different measures of distance for geographic weighting and compare our results with the unmodified CD algorithm. Our results show that GWCD can capture the geographical nature of flow regions, generating spatially smaller and more compact areas than if geography is omitted and that it can be used to distinguish between different types of movement-type communities.
Sekulic , S , Long , J & Demšar , U 2021 , ' A spatially aware method for mapping movement-based and place-based regions from spatial flow networks ' , Transactions in GIS , vol. Early View . https://doi.org/10.1111/tgis.12772
Transactions in GIS
Copyright © 2021 The Authors. Transactions in GIS published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
DescriptionThis work was supported by the Economic and Social Research Council and The Scottish Graduate School of Social Science.
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