<ul><li>Graph neural networks (GNNs) are utilized for urban spatiotemporal forecasting to predict infrastructure problems like potholes or rodent issues.</li><li>A multiview, multioutput GNN-based model is proposed to integrate government inspection ratings and crowdsourced reports for predicting the true latent state of incidents in neighborhoods.</li><li>A dataset of 9,615,863 crowdsourced reports and 1,041,415 government inspection ratings over 3 years in New York City across 139 types of incidents is collected, standardized, and made publicly available.</li><li>The model shows improved prediction of latent states by combining rating data with reporting data, especially in scenarios with sparse rating data and predictive reports, while highlighting demographic biases in crowdsourced reporting.</li></ul>

Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports

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