Geospatial Evaluation of Remote Working Hubs: A Graph Convolutional Network Model

Remote Working Hubs (RWHs) are emerging as an alternative to conventional office spaces, offering shared environments that foster collaboration and reduce the isolation, often associated with working from home. To better understand the role of RWHs in urban planning and their impact on surrounding amenities, this study introduces a novel scoring model that leverages Graph Convolutional Networks (GCNs) for geospatial analysis. The new model evaluates the connectivity and influence of various urban amenities on RWHs, providing a comprehensive assessment framework. A case study in Dublin demonstrates the model's capability to support sustainable urban planning by identifying optimal locations for RWHs based on proximity to essential amenities. The results show that the new model outperforms traditional distance-based methods, offering a more nuanced understanding of the interactions between RWHs and the urban environment. This research provides valuable insights for urban planners and policymakers aiming to enhance the integration of RWHs into smart city infrastructures.