DescriptionGeodemographic classifications have been a fundamental tool in quantitative geography and geocomputation since the 1970s, with a wide range of commercial and academic applications, from policy-making and advertisement to socio-demographic studies. However, while a variety of both commercial and open classification have been developed, the core methodological approaches used in creating them are still rooted in classic machine learning methods, such as k-means. For instance, both the nation-wide 2011 Output Area Classification (2011 OAC) for the United Kingdom and the 2011 London Output Area Classification (2011 LOAC) have been developed using a k-means approach. Despite the success of using k-means in understanding socio-demographic features within GIScience, we argue that such machine learning approach often neglects the underlying geographic patterns in data representing area objects. In the past decades, the use of deep neural network has had a transformative impact in the field of machine learning and artificial intelligence.
Graph neural network is one type of deep learning approach, which can directly operate on the graph structure, and has the ability to aggregate values for nodes from their connected neighbours to learn a more spatial sensitive embeddings. It has the potential to be integrated in geodemographic classifications with an addition account of geographic patterns behind the data. In this research, we present the use of one type of graph neural network GraphSage to create geographically sensitive embeddings for the constructed spatial graph using 2011 London census data, and demonstrate the usefulness of our approach to create a new type of geodemographic classification.
|Period||2 Sep 2021|
|Event title||Royal Geographical Society with IBG Annual International Conference 2021|
|Location||London, United Kingdom|
|Degree of Recognition||International|