CRII: III: Interpretable Models for Spatio-Temporal Event Forecasting using Social Sensors

在特定时间和特定地点发生的重大事件，如疾病爆发和犯罪事件，对我们的社会产生巨大影响。这强烈促使人们需要提前预测事件的发生，以减少潜在的社会动荡和造成的损害。例如，对于基于社交网络报告和交通传感器的交通拥堵预测，这些方法将告知当局未来将发生拥堵的地方，以及为什么某些正在发生的交通事件和拥堵热点将使特定道路上的问题恶化。近年来，随着机器学习开始应用于越来越多的实际应用，这种模型可解释性引起了越来越多的关注。作为一个对社会产生重大影响的领域，时空事件预测模型的可解释性对于赢得从业者的信任并在日常工作中被广泛采用尤为重要。然而，与传统的机器学习模型一样，社会事件预测模型仍然主要关注预测的准确性，并且正在迅速变得过于复杂和模糊，以至于人类操作员难以理解。因此，迫切需要填补数据科学家和从业者之间日益扩大的差距。为了解决这一问题，本项目致力于开发一种新的时空社会事件预测框架，该框架可以共同优化模型的准确性和可解释性，并自动说明预测生成的解释过程。为了应对空间依赖和高维大数据等挑战，该项目旨在探索条件独立性和空间拓扑，以提高空间依赖模式的稀疏性。然后，该项目将继续开发原始数据特征的分层连接格，以加强数据的说明性高级表示的简洁性和稀疏性。为解决拟定的优化问题，实现准确性和可解释性的共同最大化，本项目还涉及对相应的优化方法进行研究，并对效率和最优性进行严格的理论分析。最后，系统地探讨了社会事件预测中模型可解释性的评价策略。该项目的成功将为可解释数据挖掘和机器学习的通用研究提供启示。在这个项目中开发的方法和工具将有助于填补数据科学家和特定领域预测专家之间的空白。最后，该项目将提供宝贵的资源，以支持具有新主题、数据集、技术和软件的课程，并为代表性不足的学生提供更多的研究机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Significant events that occur at certain times and in specific locations, such as disease out-breaks and crime incidents, have tremendous impacts on our society. This strongly motivates the need to anticipate event occurrences in advance in order to reduce the potential social upheaval and damage caused. For example, for traffic congestion predictions based on social network reporting and traffic sensors, these methods would inform the authorities where the future congestion will occur and why certain ongoing traffic incident and congestion hot spots will worsen the problem on specific roads. In recent years, such model interpretability has attracted increasing attention as machine learning is beginning to be applied to ever more practical applications. As a domain with significant impact on society, the interpretability of spatio-temporal event forecasting models is particularly important in order to earn the trust of practitioners and become widely adopted in their everyday workflow. However, like conventional machine learning models, models for social event forecasting still primarily focus on prediction accuracy and are rapidly becoming too sophisticated and obscure to be easily understood by human operators. There is thus an urgent need to fill the increasing gap between data scientists and practitioners. To address it, this project focuses on developing a novel spatio-temporal social event forecasting framework that can jointly optimize the model accuracy and interpretability, and automatically illustrate the explanatory process of prediction generation. To address challenges like spatial dependency and high-dimensional large data, the project aims at exploring the conditional independence and spatial topology to boost the sparsity of spatial dependence patterns. The project will then move on to exploit the hierarchical conjunction lattice of primitive data features to enforce the conciseness and sparsity of expository high-level representations of the data. To solve the formulated optimization problem for jointly maximizing accuracy and interpretability, this project also involves research on the corresponding optimization methods with rigorous theoretical analysis on efficiency and optimality. Finally, strategies for evaluating model interpretability in social event forecasting are systematically investigated. The success of this project will shed a light on the generic research in interpretable data mining and machine learning. The methods and tools developed in this project will help fill the gaps between data scientists and domain-specific forecasting experts. Finally, this project will provide valuable resources to support courses with new topics, datasets, techniques, and software, and gives more research opportunities for underrepresented students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.