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A graph neural network framework for spatial geodemographic classification.pdf (1.82 MB)

A graph neural network framework for spatial geodemographic classification

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posted on 2023-10-12, 08:41 authored by Stefano De Sabbata, Pengyuan Liu

Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.

History

Author affiliation

School of Geography, Geology and the Environment, University of Leicester

Version

  • VoR (Version of Record)

Published in

International Journal of Geographical Information Science

Publisher

Taylor & Francis

issn

1365-8824

Copyright date

2023

Available date

2023-10-12

Language

en

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