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Spatial autocorrelation statistics have a long-standing history being used by geographers to determine whether identifiable spatial patterns exist in data. However, existing research has identified that solely relying on p-values can be problematic when working with large datasets. This paper introduces a generalised model that can capture geographical data’s spatial patterns using a graph convolutional network (GCN). The preliminary analysis demonstrates that GCN can capture the localities among areas in local-scale datasets by processing the data features and the spatial information separately into the graph network.
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\r\n\r\nGISRUK is the largest academic conference in Geographic Information Science in the UK. Since 1993, GISRUK has attracted international researchers and practitioners in GIS and cognate fields, including geography, computer science, data science, and urban planning to share the latest advances in spatial computing and analysis.
\r\n\r\nThe conference will cover all aspects of theoretical and applied geographical information science, but this year we will also have special sessions for papers which focus on aspects of the COVID19 pandemic, including ways of mapping the spread of the disease and spatially analysing the variation in its impact on such things as mobility, social inequalities and the outcomes of interventions such as stay at home orders.
\r\n\r\nConference proceedings of all accepted papers will be made available online
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