Dr Long Ying
School of Architecture, Tsinghua University
Director and Founder, Beijing City Lab
Dr Ying Long, PhD is now working in the School of Architecture, Tsinghua University, China as a tenured associate professor. His research focuses urban science, including applied urban modelling, urban big data analytics & visualisation, quantitative urban studies, planning support systems, data augmented design and future cities. He has an education background in both environmental engineering and city planning. Before joining Tsinghua University, he has worked for Beijing Institute of City Planning as a senior planner for eleven years.
Familiar with planning practices in China and versed in international literature, Dr Long’s academic studies creatively integrate international methods and experiences with local planning practices. He has published almost two hundred papers and led over twenty research/planning projects. His funded projects range from international organisations like World Bank, World Health Organization, World Resource Institute and NRDC, and Wellcome Trust, internet companies like Alibaba, Baidu, Jingdong, Tencent, Didi, Mobike and Gudong, local governments like Beijing, Chengdu, Qingdao, Hefei, Zunyi, Rongcheng and Laizhou, to central governments like NDRC and MOHURD, and the NSFC. Dr Long is also the founder of Beijing City Lab (BCL: www.beijingcitylab.com), an open research network for quantitative urban studies.
Topic: UrbanSense: Empowering Communities through Active Sensing for Sustainable Urban Development
Accurate monitoring of urban environments and their dynamics is essential for achieving the Sustainable Development Goals (SDGs) set by the United Nations. However, traditional sensing methods face challenges in meeting the needs of urban monitoring, including difficulties in balancing spatial and temporal granularity, high human and material costs, and a mismatch in study scope due to data-driven rather than demand-driven approaches. In recent years, active urban sensing methods have emerged as more flexible approaches that can adapt to varying demands. Three sensing paradigms — stationary sensing, mobile sensing and collaborative sensing — have been practiced in research.
This paper proposes a framework for an active urban sensing approach: firstly, it categorises and aggregates literature on active urban sensing techniques, refines monitoring objects and sensing paradigms, and forms an evidence-based metrics library for active urban sensing. Secondly, in order to conclude the application conditions of different sensing paradigms, the metrics are further clustered according to volatility, spatial resolution and spatio-temporal coverage, and five application scenarios are further summarised to form a decision tree for sensing paradigm selection. This framework serves as a valuable reference for data refinement in less developed areas with missing or untimely data updates, as well as developed areas with insufficient data coverage and density, enabling active urban sensing to be applied in a wider range of demand scenarios and contribute to the achievement of SDGs in community research contexts.