基于图神经网络的城市事件预测：整合政府评级与众包报告
Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports
AI总结：
针对政府检查评级稀疏、众包报告有偏的问题，提出多视图多输出GNN模型整合两类数据预测城市事件真实状态，公开纽约市数据集并验证模型优越性，量化报告偏差。
摘要：
图神经网络（GNNs）广泛应用于城市时空预测，例如基础设施问题预测。在此场景中，政府官员希望了解坑洼或啮齿动物问题等事件发生在哪些社区。每个社区事件的真实状态（如街道状况）通过政府检查评级观测。然而，这些评级仅针对稀疏的社区和事件类型集合进行。我们还通过众包报告观测事件状态，其观测密度更高，但可能因异质报告行为存在偏差。首先，针对此类场景，我们提出一种多视图、多输出的基于GNN的模型，该模型同时使用无偏评级数据和有偏报告数据来预测事件的真实潜在状态。其次，我们开展纽约市城市事件的案例研究，收集、标准化并公开了一个涵盖3年、139种事件类型的数据集，包含9,615,863条众包报告和1,041,415条政府检查评级。最后，我们在真实和半合成数据上验证：与仅使用报告数据或仅使用评级数据的模型相比，我们的模型能更好地预测潜在状态，尤其是在评级数据稀疏且报告能预测评级的情况下。我们还量化了众包报告中的人口统计偏差，例如高收入社区的问题报告率更高。我们的分析展示了一种利用异质、稀疏和有偏数据进行潜在状态预测的广泛适用方法。
英文摘要：
Graph neural networks (GNNs) are widely used in urban spatiotemporal forecasting, such as predicting infrastructure problems. In this setting, government officials wish to know in which neighborhoods incidents like potholes or rodent issues occur. The true state of incidents (e.g., street conditions) for each neighborhood is observed via government inspection ratings. However, these ratings are only conducted for a sparse set of neighborhoods and incident types. We also observe the state of incidents via crowdsourced reports, which are more densely observed but may be biased due to heterogeneous reporting behavior. First, for such settings, we propose a multiview, multioutput GNN-based model that uses both unbiased rating data and biased reporting data to predict the true latent state of incidents. Second, we investigate a case study of New York City urban incidents and collect, standardize, and make publicly available a dataset of 9,615,863 crowdsourced reports and 1,041,415 government inspection ratings over 3 years and across 139 types of incidents. Finally, we show on both real and semi-synthetic data that our model can better predict the latent state compared to models that use only reporting data or models that use only rating data, especially when rating data is sparse and reports are predictive of ratings. We also quantify demographic biases in crowdsourced reporting, e.g., higher-income neighborhoods report problems at higher rates. Our analysis showcases a widely applicable approach for latent state prediction using heterogeneous, sparse, and biased data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
