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The Microsoft Research Connections blog shares stories of collaborations with computer scientists at academic and scientific institutions to advance technical innovations in computing, as well as related events, scholarships, and fellowships.

Microsoft Research Asia funds cloud computing for urban studies

Microsoft Research Asia funds cloud computing for urban studies

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The world is becoming more urban. The movement of populations from rural to city life is nothing new in the developed countries of Europe and North America, but it has greatly accelerated in the rapidly developing countries of Asia. In China, for example, the percentage of urban dwellers has swelled from less than 30 percent in 1980 to over 50 percent—and growing—today. Given the rapid growth of cities in the developing world, the United Nations estimated that in 2008, for the first time in history, more than half of the world’s population resided in urban areas.

Rapid urbanization poses challenges, as growing cities strive to deliver services, maintain a safe and healthful environment, and promote a vibrant economy. Meetings these challenges requires actions based on the collection, analysis, and modeling of reliable data, a need that has given rise to the field of urban informatics. Think of it as the big data of big cities.

Using big data to tackle big challenges cities face
Using big data to tackle big challenges cities face

Crunching big data is one of the strengths of cloud computing, and Windows Azure, the cloud-computing platform from Microsoft, offers tremendous potential in urban informatics. With this in mind, earlier this year Microsoft Research Asia issued an invitation for proposals that use Windows Azure to accelerate urban informatics, with the winning proposals receiving grants that support the research for at least a year.

After evaluating 60 proposals from 34 Asian universities and institutions, the Microsoft Research Asia team has selected 25 projects for funding. The winning projects cover a broad spectrum of urban informatics research, from enhancing transportation, to mapping city noise, to preserving the privacy of urbanites and even tracking social happiness. The winning projects come from institutions throughout East Asia, including those in China, Hong Kong, Japan, Korea, and Singapore. All results arising from the funded projects will be broadly available, either in the public domain or under a non-restrictive license that allows modification and redistribution without significant restrictions or conditions.

We’re delighted to be funding these important studies, the results of which, we hope, will make city life more livable in years ahead.

—Kangping Liu, Senior Manager, Microsoft Research Connections Asia

Learn more

Funded projects

  • Guangzhong Sun, University of Science and Technology of China (China): Smart campus construction based on rich campus datasets
  • Han-Lim Choi, Korea Advanced Institute of Science and Technology (Korea): Scalable Gaussian Process-Enabled Bayesian Inference for Sensor Networks in Smart Buildings
  • Hojung Cha, Yonsei University (Korea): Development of a CrowdSensing Framework for Inducing User Participation in Urban Environments
  • Hong Cheng, Chinese University of Hong Kong (Hong Kong): Optimal Point of Interest Routing in a Urban Environment
  • Huayi Wu, Wuhan University (China): Collaborative Geoprocessing on Windows Azure
  • Hwasoo Yeo, Korea Advanced Institute of Science and Technology (Korea): Smart phone based Urban Travel Pattern Analysis and Prediction with Online Traffic Simulator
  • Janny Leung, Chinese University of Hong Kong (Hong Kong): Back to the Future: Sense-and-Respond Public Transit for Historic Urban Centres
  • Jitao Sang, Institute of Automation, Chinese Academy of Sciences (China): Cyber-Physical Footprint Association for Urban Computing
  • Joon Heo, Yonsei University (Korea): Does ‘Gangnam Style’ really exist? Answers from data science perspective
  • Jun Ma, Shandong University (China): Urban lifestyles Detection based on Big Heterogeneous Human Behavioral Data
  • Kohei Matsumura, Future University of Hakodate (Japan): A multimodal approach for in-car conversation sharing
  • Lei Chen, Hong Kong University of Science and Technology (Hong Kong): Urban Traffic Monitoring-based Mobile Crowdsourcing
  • Lei Zou, Peking University (China): Graph Data Management in Urban Computing
  • Long Quan, Hong Kong University of Science and Technology (Hong Kong): Large-scale Three-dimensional Urban Reconstruction
  • Rajesh Balan, Singapore Management University (Singapore): Building a Practical Location System for Tracking Consumer Movement in Indoor Public Spaces
  • Soobin Lee, Korea Advanced Institute of Science and Technology (Korea): Waste Management Planning based on Multi-Sensor Data Fusion
  • Tai-Quan Peng, Nanyang Technological University (Singapore): Tracking Dynamics of Social Happiness on Twitter: a Multi-level Study
  • Victor Li, University of Hong Kong (Hong Kong): A Big Data Stream Processing Solution for Hidden Causality Detection of Urban Dynamics
  • Xiaokui Xiao, Nanyang Technological University (Singapore): Preserving Individual Privacy in Urban Informatics
  • Xueming Qian, Xi'an Jiaotong University (China): Schedule travel life by exploring spectrums of social user and city services
  • Yanmin Zhu, Shanghai Jiao Tong University (China): NoiseSense: Crowdsourcing-Based Urban Noise Mapping with Smartphones
  • Ying-Qing Xu, Tsinghua University (China), Stephen Jia Wang, Monash University (Australia): Intelligent Sustainable Navigation Services (ISUNS): To Enhance Eco-Efficiency for Urban Transportation by Adopting Clouds and Pervasive Computing Technologies
  • Yuguo LI, University of Hong Kong (Hong Kong): SmartComfort—Use of smartphone and cloud technologies for building thermal comfort, and ventilation and health studies in megacities
  • Zhiwen Yu, Northwestern Polytechnical University (China): Understanding City Interest and Sentiment Leveraging Crowdsourced Digital Footprints from LBSNs
  • Zongjian He, Tongji University (China): Community Sensing-Based Green Driving System Using Smartphones

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