Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g., air pollution, increased energy consumption and traffic congestion. Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve 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 computing is an interdisciplinary field fusing the computing science with traditional fields, like transportation, civil engineering, economy, ecology, and sociology, in the context of urban spaces.
The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of the development and applications related to urban computing, present their ideas and contributions, and set future directions in innovative research for urban computing. Particularly, at SIGSPATIAL 2021 this workshop targets people who are interesting in sensing/mining/understanding urban data so as to tackle challenges in cities and help better formulate the future of cities. This workshop also well aligns with the topic of SIGSPATIAL 2021, mining geo-spatial data and knowledge.
Ouri Wolfson, University of Illinois at Chicago, USA
Qiang Yang, Hong Kong University of Science and Technology
Philip Yu, University of Illinois at Chicago, USA
Yu Zheng, JD Technology
Jieping Ye, Ke Holdings Inc
Yanhua Li, Worcester Polytechnic Institute
Jie Bao, JD Technology
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 distinct fields in computing science, having their own communities and conferences. While people from these two communities 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 analytics 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 concatenating 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. For example, five data sources, consisting of traffic, meteorology, POIs, road networks, and air quality readings, are used to predict the fine-grained air quality throughout a city. When trying to apply this method to other cities, however, we would find that many cities cannot find enough data in each domain (e.g. do not have enough monitoring stations to generate air quality data), or may even not have the data of a domain (like traffic data) at all.
Interactive visual data analytics: Data visualization is not solely about displaying raw data and presenting results, though the two are general motivation of using visualization. Interactive visual data analytics becomes even more important in urban computing, seamlessly combining visualization methods with data mining algorithms as well as a deployment of the integration on a cloud computing platform. It is also an approach to the combination of human intelligence with machine intelligence. The interactive visual data analytics also empower people to integrate domain knowledge (such as urban planning) with data science, enabling domain experts to work with data scientist on solving a real problem in cities.
Topics of interest include, but not limited to, the following aspects :
Data mining for urban planning and city configuration evaluation
Mining urban environmental, pollution, and ecological data
Knowledge discovery from sensor data for saving energy and resources
Data mining for sustainable and intelligent cities
Urban sensing and city dynamics sensing
Knowledge fusion from data across different domains
City-wide traffic modeling, visualization, analysis, and prediction
City-wide human mobility modeling, visualization, and understanding
City-wide intelligent transportation systems
Anomaly detection and event discovery in urban areas
Mining urban economics
Social behavior modeling, understanding, and patterns mining in urban spaces
City-wide mobile social applications in urban areas
Location-based social networks enabling urban computing scenarios
Smart recommendations in urban spaces
Intelligent delivery services and logistics industries in cities
Mining data from the Internet of Things in urban areas
Managing urban big data on the cloud
Interactive visual data analytics for urban computing
Federated learning for urban computing
We will set one best paper award according to the review results and presentation of a paper.
|Workshop paper submissions:||
||Workshop paper notifications:||Oct. 10, 2021||Workshop date:||November 1, 2021|