CAREER: Cross-Domain Urban Data Mining

根据美国2010年人口普查，大约80.7%的美国人口居住在城市地区。城市化使人们的生活现代化，但也产生了许多城市问题，如交通拥堵、空气污染、健康、教育和生活质量。与此同时，随着传感技术的快速发展和数字文档的广泛应用，越来越多的城市数据以数字形式积累起来，包括人的痕迹、交通、空气质量、当地事件、车辆碰撞、噪音报告等等。美国的许多城市（如纽约市、芝加哥和洛杉矶）都加入了开放数据倡议，并创建了网站向公众发布城市数据。这样的大数据意味着对城市的丰富了解，可以使我们能够应对许多关键的城市挑战。本项目开发新颖的数据挖掘技术，帮助人们揭示城市大数据中复杂的相关性。虽然每种类型的城市数据之前都在其各自的领域进行了分析，但我们缺乏一种有原则的方法来整合和分析从不同领域收集的数据，以便从多个方面更好地理解城市问题。该项目研究了系统的解决方案，以整合和建模城市数据，发现隐藏的模式，并以可解释的方式呈现和可视化结果。关键创新在于如何有效地利用异构的城市数据，并从这些数据中学习相互强化的知识。该项目探讨了社会科学、交通、生态和城市规划等其他研究领域的现实问题，并承诺在这些领域产生跨学科的影响。最终，该项目致力于推进城市计算技术，这是一个新兴的跨学科研究领域，旨在解决快速发展的城市环境中的挑战和机遇。

According to U.S. 2010 Census, about 80.7% of the U.S. population live in urban area. Urbanization has modernized people's lives but also generated many urban issues such as traffic congestion, air pollution, health, education, and life quality. In the meantime, with the rapid progress in sensing technologies and widely-used digital documentation, increasing amount of urban data are being accumulated in the digital form, including human traces, traffic, air quality, local events, vehicle collisions, noise reports, and many more. Many cities in the U.S. (e.g., New York City, Chicago, and Los Angeles) have joined the open data initiative and created websites to release the city data to the public. Such big data implies rich knowledge about a city and could empower us to address many critical urban challenges.This project develops novel data mining techniques to help people uncover the complicated correlations in the big urban data. While each type of urban data has been previously analyzed in its own domain, we lack a principled approach to integrate and analyze the data collected from different domains in order to better understand the urban issues from multiple aspects. The project investigates systematic solutions to integrate and model the urban data, discover the hidden patterns, and present and visualize the results in an interpretable way. The key innovation lies in how to effectively harness the heterogeneous urban data and learn mutually reinforced knowledge from such data. The project explores motivating real-world problems from other research fields such as social science, transportation, ecology, and urban planning, and promises interdisciplinary impacts in these fields. Ultimately, this project strives to advance the techniques in urban computing, a nascent interdisciplinary research field that addresses the challenges and opportunities in the fast-evolving urban environments.