  Urban land use inference is a critically important task that aids in city
planning and policy-making. Recently, the increased use of sensor and location
technologies has facilitated the collection of multi-modal mobility data,
offering valuable insights into daily activity patterns. Many studies have
adopted advanced data-driven techniques to explore the potential of these
multi-modal mobility data in land use inference. However, existing studies
often process samples independently, ignoring the spatial correlations among
neighbouring objects and heterogeneity among different services. Furthermore,
the inherently low interpretability of complex deep learning methods poses a
significant barrier in urban planning, where transparency and extrapolability
are crucial for making long-term policy decisions. To overcome these
challenges, we introduce an explainable framework for inferring land use that
synergises heterogeneous graph neural networks (HGNs) with Explainable AI
techniques, enhancing both accuracy and explainability. The empirical
experiments demonstrate that the proposed HGNs significantly outperform
baseline graph neural networks for all six land-use indicators, especially in
terms of 'office' and 'sustenance'. As explanations, we consider feature
attribution and counterfactual explanations. The analysis of feature
attribution explanations shows that the symmetrical nature of the `residence'
and 'work' categories predicted by the framework aligns well with the
commuter's 'work' and 'recreation' activities in London. The analysis of the
counterfactual explanations reveals that variations in node features and types
are primarily responsible for the differences observed between the predicted
land use distribution and the ideal mixed state. These analyses demonstrate
that the proposed HGNs can suitably support urban stakeholders in their urban
planning and policy-making.
