westminsterresearch.westminster.ac.uk 的前十大競爭對手

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This dissertation develops a unified, spatio-temporal approach to the design of equitable public infrastructure, spanning two domains that share common structure: digital epidemiology and electric-vehicle (EV) charging networks. The central thesis is that infrastructure decisions under uncertainty benefit from the same modelling stack: hierarchical spatial meshing (H3), graph-centric representations, forecasting under sparsity, and queueing-aware optimisation with explicit equity constraints. First, we assess Peru's national digital contact tracing (DCT) programme during COVID-19. Using privacy-screened mobility traces from 1.66 million users and over 80,000 confirmed infections, we reconstruct dynamic contact networks, quantify behavioural differences between infected and non-infected populations, and couple these with geospatial socio-economic indicators to reveal disparities in exposure risk—providing evidence for equity-sensitive public-health strategies. Second, we introduce Spatial Neighbourhood Fusion (SPN), a lightweight, model-agnostic enhancement for short-horizon human-mobility forecasting on H3-indexed grids. By fusing each cell's history with multi-ring neighbour signals, SPN mitigates extreme sparsity in hourly origin-destination counts and delivers up to 9.85% accuracy gains across multiple state-of-the-art forecasters, with the largest improvements in low-mobility regions. Third, we leverage Georgia State's large-scale spatio-temporal vehicle mobility OD matrix dataset to enable data-driven optimization for EV charging infrastructure deployment. We develop a queueing-theoretic siting and sizing framework that integrates H3 spatial grids, traffic patterns, socio-economic context, and M/M/c service models to balance accessibility and system efficiency. We then present DOVA-PATBM, an adaptive geo-computational pipeline that combines Voronoi-H3 meshing, GNN-based intersection centrality, POI-aware demand estimation, and power-grid constraints to produce

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