FAQ

Early participants in the Urban Computing Foundation include developers from Facebook, Google, Senseable City Labs of MIT, HERE Technologies an Uber.

Anyone can participate in this work, To get involved in the Urban Computing Foundation, please contact info@uc.foundation and join the technical community mailing list.

The first project hosted at Urban Computing Foundation is Kepler.gl, an open source geospatial analysis tool released in 2018 to help make it easier to create meaningful visualizations of location data without the need for coding. Kepler.gl is used by developers, data scientists, visualization specialists and engineers around the world to explore and analyze a variety of scenarios that include transportation patterns and safety trends. Some of the companies using Kepler.gl include Airbnb, Atkins Global, Cityswifter, HERE Technologies, Limebike, Mapbox, Sidewalk Labs, Uber and UBILabs, among others. Are you interested in contributing a project? Please contact us.

The MIT license https://github.com/uber/kepler.gl/blob/master/LICENSE

To start participating, please visit the Github repo:  https://github.com/ucfoundation

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The Foundation uses an open governance model, with review and curation by a Technical Advisory Council (TAC) that is representative of contributors to the urban computing space, to determine tools and software inclusion in the Foundation.

TAC members include:

• Drew Dara-Abrams, principal, Interline Technologies
• Oliver Fink, director HERE XYZ, HERE Technologies
• Travis Gorkin, engineering manager of data visualization, Uber
• Shan He, project leader of Kepler.gl, Uber
• Randy Meech, CEO, StreetCred Labs
• Michal Migurski, engineering manager of spatial computing, Facebook
• Drishtie Patel, product manager of maps, Facebook
• Paolo Santi, senior researcher, MIT
• Max Sills, attorney, Google

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
• novel visualization methods
• spatio-temporal machine learning techniques
• city-scale simulation of urban systems

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.

Adapted 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 central to Urban Computing.

Combining machine learning algorithms with database/data engineering techniques:
Machine learning and data engineering 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.

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:
The interactive visual data analytics empower people to integrate domain knowledge (such as urban planning) with data science, enabling domain experts to work with data scientists on solving a real problems in cities.