Acta Geodaetica et Cartographica Sinica ›› 2018, Vol. 47 ›› Issue (8): 1043-1050.doi: 10.11947/j.AGCS.2018.20180103

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Virtual Geographic Cognition Experiment in Big Data Era

ZHANG Fan1,2, HU Mingyuan1,3, LIN Hui1,3   

  1. 1. Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China;
    2. Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China;
    3. Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen 518057, China
  • Received:2018-01-20 Revised:2018-03-20 Online:2018-08-20 Published:2018-08-22
  • Supported by:
    The National Basic Research Program of China(973 Program)(No. 2015CB954103);The National Natural Science Foundation of China (Nos. 41371388;41671378);The General Research Fund of the Research Grants Council of Hong Kong (No. 14606715)

Abstract: Virtual geographic cognition experiment is an experimental framework to understand the perception,cognition,emotion and behavior of human to the environment.Since it has been proposed,experimental geography and other related field has been benefited from its theory and methodology.The coming big data era has brought with a huge massive of human cognitive,emotional and behavioral data,potentially bringing opportunities to establish new research paradigm in virtual geographic experiment,to help researchers look deeper into the man-land relationship.In this paper,we first proposed the framework of virtual geographic cognition experiment based on data-intensive scientific research paradigm,where we introduced how to treat human activity data and urban context data by integrating the theory in environmental psychology,and artificial intelligence.Second,we demonstrated the framework by introducing a case study,which has explored a series of visual factor in urban scene that would have an impact on depressing emotion of individual.

Key words: virtual geographic environments, cognition experiment, data-intensive scientific discovery, street-level imagery, deep learning

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