--------------
Aims and Scope
--------------

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
------------------

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

---------------------
Submission Guidelines
---------------------

We invite two kinds of submissions:

Full research papers – up to 9 pages (8 pages at most for the main body and the last page can only hold references)
Vision papers and short technical papers - up to 5 pages (4 pages at most for the main body and the last page can only hold references)

All manuscripts should be submitted in a single PDF file including all content, figures, tables, and references, following the new Standard ACM Conference Proceedings Template.

For LaTeX users: unzip acmart.zip, make, and use sample-sigconf.tex as a template; Additional information about formatting and style files is available online at https://www.acm.org/publications/proceedings-template.

Papers should be submitted to https://easychair.org/conferences/?conf=urbcomp2020 .

Paper submissions need to include author information, it is not double-blinded.

Each paper will be assigned to two reviewers for a peer review.

------------------------------
Awards and Journal Publication
------------------------------

We will set one best paper award according to the review results and presentation of a paper.