The 11th International Workshop on Urban Computing Chuishi Meng JD Technology, China meng.chuishi@jd.com Yanhua Li Worcester Polytechnic Institute, USA yli15@wpi.edu Jieping Ye Ke Holdings Inc, China jieping@gmail.com Qiang Yang Hong Kong University of Science of Technology, Hong Kong SARChina WeBank, China qyang@cse.ust.hk Yu Zheng JD Technology, China msyuzheng@outlook.com Philip S. Yu University of Illinois at Chicago, USA psyu@uic.edu Ouri Wolfson University of Illinois at Chicago, USA wolfson@cs.uic.edu ABSTRACT Urbanization’s rapid progress has led to many big cities, which have modernized many people’s lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic con- gestion. Tackling these challenges were nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sens- ing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today. This is the eleventh time that we organize this workshop. The previous 10 workshops were hosted with SIGKDD and SIGSPATIAL, each of which attracted over 70 participants and 30 submissions on average. KEYWORDS Urban Computing, Spatio-temporal Data Mining 1 INTRODUCTION Urban computing is a process of acquisition, integration, and anal- ysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, build- ings, and human, to tackle the major issues that cities face. Urban computing connects unobtrusive and ubiquitous sensing technolo- gies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that im- prove urban environment, human life quality, and city operation systems. Urban computing also helps us understand the nature of urban phenomena and even predict the future of cities. Urban Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). KDD ’22, August 14–18, 2022, Washington, DC, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9385-0/22/08. https://doi.org/10.1145/3534678.3542892 computing is an interdisciplinary field fusing the computing sci- ence with traditional fields, like transportation, civil engineering, economy, ecology, and sociology, in the context of urban spaces. The goals and framework of urban computing result in four folds of challenges in the context of data mining: • Adapt machine learning algorithms to spatial and spatio- temporal data: Spatio-temporal data has unique properties, consisting of spatial distance, spatial hierarchy, temporal smoothness, period and trend, as compared to image and text data. How to adapt existing machine learning algorithms to deal with spatio-temporal properties remains a challenge. • Combine machine learning algorithms with database techniques: Machine learning and databases are two dis- tinct fields in computing science, having their own commu- nities and conferences. While people from these two commu- nities barely talk to each other, we do need the knowledge from both sides when designing data analytic methods for urban computing. The combination is also imperative for other big data projects. It is a challenging task for people from both communities to design effective and efficient data analytic methods that seamlessly and organically integrate the knowledge of databases and machine learning. • Cross-domain knowledge fusion methods: While fusing knowledge from multiple disparate datasets is imperative in a big data project, cross-domain data fusion is a non-trivial task given the following reasons. First, simply concatenat- ing features extracted from different datasets into a single feature vector may compromise the performance of a task, as different data sources may have very different feature spaces, distributions and levels of significance. Second, the more types of data involved in a task, the more likely we could encounter a data scarce problem. • Interactive visual data analytics: Data visualization is not solely about displaying raw data and presenting results, though the two are general motivations for using visualiza- tion. Interactive visual data analytics becomes even more important in urban computing, seamlessly combining visu- alization methods with data mining algorithms as well as a 4886