大数据背景下的虚拟地理认知实验方法

doi:10.11947/j.AGCS.2018.20180103

测绘学报 ›› 2018, Vol. 47 ›› Issue (8): 1043-1050.doi: 10.11947/j.AGCS.2018.20180103

大数据背景下的虚拟地理认知实验方法

张帆^1,2, 胡明远^1,3, 林珲^1,3

1. 香港中文大学太空与地球信息科学研究所, 中国香港;
2. 北京大学遥感与地理信息系统研究所, 北京 100871;
3. 香港中文大学深圳研究院, 广东深圳 518057

收稿日期:2018-01-20 修回日期:2018-03-20 出版日期:2018-08-20 发布日期:2018-08-22
通讯作者: 林珲 E-mail:huilin@cuhk.edu.hk
作者简介:张帆(1990-),男,博士,助理研究员,研究方向为地理信息科学。E-mail:zhangfan@link.cuhk.edu.hk
基金资助:
国家973计划（2015CB954103）；国家自然科学基金（41371388；41671378）；香港研究资助局优配研究金（14606715）

Virtual Geographic Cognition Experiment in Big Data Era

ZHANG Fan^1,2, HU Mingyuan^1,3, LIN Hui^1,3

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)

摘要/Abstract

摘要： 如何构建新一代的虚拟地理认知实验研究范式，从海量人类活动和地理数据中挖掘人地交互过程中的新模式、新知识成为新一代虚拟地理认知实验需要解决的问题。本研究首先提出了在数据密集型科学研究范式下的虚拟地理认知实验框架，阐述了从环境心理学的视角，结合多源人类活动大数据、城市环境大数据构建实验平台的方法，进而支持地理知识工程的构建。其次，本文介绍了基于海量街景数据和对应的个体情感评分数据，利用深度学习的相关方法和统计模型来进行认知知识挖掘的案例。实验发现了可视域中与个体对场景的情感维度-压抑感具有较高相关性的一系列视觉要素，如植被、建筑、车辆等，并通过回归分析予以量化。

关键词: 虚拟地理环境, 认知实验, 数据密集型研究范式, 街景影像, 深度学习

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

中图分类号:

P208

张帆, 胡明远, 林珲. 大数据背景下的虚拟地理认知实验方法[J]. 测绘学报, 2018, 47(8): 1043-1050.

ZHANG Fan, HU Mingyuan, LIN Hui. Virtual Geographic Cognition Experiment in Big Data Era[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(8): 1043-1050.

参考文献

[1] 林珲, 龚建华.论虚拟地理环境[J]. 测绘学报, 2002, 31(1):1-6. DOI:10.3321/j.issn:1001-1595.2002.01.001. LIN Hui, GONG Jianhua. On Virtual Geographic Environments[J]. Acta Geodaetica et Cartographica Sinica, 2012, 31(1):1-6. DOI:10.3321/j.issn:1001-1595.2002.01.001.
[2] 林珲, 黄凤茹, 闾国年. 虚拟地理环境研究的兴起与实验地理学新方向[J]. 地理学报, 2009, 64(1):7-20. LIN Hui, HUANG Fengru, LÜ Guonian. Development of Virtual Geographic Environments and the New Initiative in Experimental Geography[J]. Acta Geographica Sinica, 2009, 64(1):7-20.
[3] LIN Hui, CHEN Min, LÜ Guonian, et al. Virtual Geographic Environments (VGE):A New Generation of Geographic Analysis Tool[J]. Earth-Science Reviews, 2013, 126(1):74-84.
[4] 林珲, 陈旻. 利用虚拟地理环境的实验地理学方法[J]. 武汉大学学报(信息科学版), 2014, 39(6):689-694, 700. LIN Hui, CHEN Min. Experimental Geography Based on Virtual Geographic Environments (VGE)[J]. Geomatics and Information Science of Wuhan University, 2014, 39(6):689-694, 700.
[5] LIN Hui, CHEN Min, LÜ Guonian. Virtual Geographic Environment:A Workspace for Computer-aided Geographic Experiments[J]. Annals of the Association of American Geographers, 2013, 103(3):465-482.
[6] LIN H, BATTY M. Virtual Geographic Environments:A Primer[M]//LIN H, BATTY M. Virtual Geographic Environments. Beijing:Science Press, 2008:1-10.
[7] LÜ Guonian, CHEN Min, YUAN Linwang, et al. Geographic Scenario:A Possible Foundation for Further Development of Virtual Geographic Environments[J]. International Journal of Digital Earth, 2017:1-13.
[8] 龚建华, 李文航, 周洁萍, 等. 虚拟地理实验概念框架与应用初探[J]. 地理与地理信息科学, 2009, 25(1):18-21. GONG Jianhua, LI Wenhang, ZHOU Jieping, et al. Exploring Conceptual Framework and Application of Virtual Geographic Experiments[J]. Geography and Geo-Information Science, 2009, 25(1):18-21.
[9] XU Bingli, LIU Hui, CHIU Longsang, et al. Collaborative Virtual Geographic Environments:A Case Study of Air Pollution Simulation[J]. Information Sciences, 2011, 181(11):2231-2246.
[10] 周洁萍, 龚建华, 陈铮, 等. 协同虚拟地理环境中多用户交流交互模式及实现[J]. 地理与地理信息科学, 2015, 21(5):33-37. ZHOU Jieping, GONG Jianhua, CHEN Zheng, et al. Study on Collaborative Virtual Geographic Environment System for Multi-user Interaction and Communication[J]. Geography and Geo-Information Science, 2005, 21(5):33-37.
[11] 李毅, 龚建华, 周洁萍, 等. 协同虚拟地理实验系统设计与初步试验[J]. 高技术通讯, 2010, 20(4):431-435. LI Yi, GONG Jianhua, ZHOU Jieping, et al. Design and Preliminary Test of a Collaborative Virtual Geographic Experiment System[J]. Chinese High Technology letters, 2010, 20(4):431-435.
[12] 朱杰, 夏青. 基于虚拟地理环境的空间认知分析[J]. 测绘科学, 2008, 33(S1):25-26, 185. ZHU Jie, XIA Qing. Research on Spatial Cognition in VR Terrain Environment[J]. Science of Surveying and Mapping, 2008, 33(S1):25-26, 185.
[13] ZHANG Fan, HU Mingyuan. CHE Weitao. et al. Framework for Virtual Cognitive Experiment in Virtual Geographic Environments[J]. ISPRS International Journal of Geo-Information, 2010, 7(1):36.
[14] 林珲, 张捷, 杨萍, 等. 空间综合人文学与社会科学研究进展[J]. 地球信息科学学报, 2006, 8(2):30-37. LIN Hui, ZHANG Jie, YANG Ping, et al. Development on Spatially Integrated Humanities and Social Science[J]. Geo-Information Science, 2006, 8(2):30-37.
[15] 林珲, 黄凤茹, 鲁学军, 等. 虚拟地理环境认知与表达研究初步[J]. 遥感学报, 2010, 14(4):822-838. LIN Hui, HUANG Fengru, LU Xuejun, et al. Preliminary Study on Virtual Geographic Environment Cognition and Representation[J]. Journal of Remote Sensing, 2010, 14(4):822-838.
[16] HU Mingyuan, LIN Hui, CHEN Bin, et al. A Virtual Learning Environment of the Chinese University of Hong Kong[J]. International Journal of Digital Earth, 2011, 4(2):171-182.
[17] CHEN Min, LIN Hui, HU Mingyuan, et al. Real-Geographic-scenario-based Virtual Social Environments:Integrating Geography with Social Research[J]. Environment and Planning B:Planning and Design, 2013, 40(6):1103-1121.
[18] TAN Lu, HU Mingyuan, LIN Hui. Agent-based Simulation of Building Evacuation:Combining Human Behavior with Predictable Spatial Accessibility in A Fire Emergency[J]. Information Sciences, 2015, 295(1):53-66.
[19] SALESSES P, SCHECHTNER K, HIDALGO C A. The Collaborative Image of the City:Mapping the Inequality of Urban Perception[J]. PLoS One, 2013, 8(7):e68400.
[20] LIN Tianpeng, LIN Hui, HU Mingyuan. Three-dimensional Visibility Analysis and Visual Quality Computation for Urban Open Spaces Aided by Google SketchUp and WebGIS[J]. Environment and Planning B:Urban Analytics and City Science, 2017, 44(4):618-646.
[21] HEY T, TANSLEY S, TOLLE K. The Fourth Paradigm:Data-intensive Scientific Discovery[M]. Washington:Microsoft Research Redmond, 2009.
[22] CHEN C L P, ZHANG Chunyang. Data-intensive Applications, Challenges, Techniques and Technologies:A Survey on Big Data[J]. Information Sciences, 2014, 275(3):314-347.
[23] MODHA D S, ANANTHANARAYANAN R, ESSER S K, et al. Cognitive Computing[J]. Communications of the ACM, 2011, 54(8):62-71.
[24] BARABÁSI A L. Bursts:the Hidden Patterns Behind Everything We do, from Your E-mail to Bloody Crusades[M]. USA:Penguin, 2011.
[25] LIU Yu, LIU Xi, GAO Song, et al. Social Sensing:A New Approach to Understanding Our Socioeconomic Environments[J]. Annals of the Association of American Geographers, 2015, 105(3):512-530.
[26] LIU Yu, SUI Zhengwei, KANG Chaogui, et al. Uncovering Patterns of Inter-urban Trip and Spatial Interaction from Social Media Check-in Data[J]. PLoS One, 2014, 9(1):e86026.
[27] LIU Yu, WANG Fahui, XIAO Yu, et al. Urban Land Uses and Traffic ‘Source-sink Areas’:Evidence from GPS-enabled Taxi Data in Shanghai[J]. Landscape and Urban Planning, 2012, 106(1):73-87
[28] RATTI C, FRENCHMAN D, PULSELLI R M, et al. Mobile Landscapes:Using Location Data from Cell Phones for Urban Analysis[J]. Environment and Planning B:Urban Analytics and City Science, 2006, 33(5):727-748.
[29] CALABRESE F, COLONNA M, LOVISOLO P, et al. Real-time Urban Monitoring Using Cell Phones:A Case Study in Rome[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1):141-151.
[30] AGARWAL A, XIE Boyi, VOVSHA I, et al. Sentiment Analysis of Twitter Data[C]//Proceedings of the Workshop on Language in Social Media. Portland, Oregon:Association for Computational Linguistics, 2011:30-38.
[31] HAWELKA B, SITKO I, BEINAT E, et al. Geo-located Twitter As Proxy for Global Mobility Patterns[J]. Cartography and Geographic Information Science, 2014, 41(3):260-271.
[32] YE Mao, YIN Peifeng, LEE W C, et al. Exploiting Geographical Influence for Collaborative Point-of-interest Recommendation[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. Beijing, China:ACM, 2011:325-334.
[33] MCKENZIE G, JANOWICZ K, GAO Song, et al. POI Pulse:A Multi-granular, Semantic Signatures-based Information Observatory for the Interactive Visualization of Big Geosocial Data[J]. Cartographica the International Journal for Geographic Information and Geovisualization, 2015, 50(2):71-85.
[34] STOKOLS D. Environmental Psychology[J]. Annual Review Psychology, 1978, 29(2):253-295.
[35] GIFFORD R. Environmental Psychology:Principles And Practice[M]. 4th ed. Colville:Optimal Books, 2007.
[36] LECUN Y, BENGIO Y, HINTON G. Deep Learning[J]. Nature, 2015, 521(7553):436-444.
[37] ZHAO Hengshuang, SHI Jianping, Qi Xiaojuan, et al. Pyramid Scene Parsing Network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI:IEEE, 2017:6230-6239.
[38] ZHOU Bolei, ZHAO Hang, PUIG X, et al. Scene Parsing through ADE20K Dataset[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI:IEEE, 2017:5132-5130.
[39] ASHIHARA Y. The Aesthetic Townscape[M]. Cambridge:The MIT Press, 1983.
[40] JACOBS J. The Death and Life of Great American Cities[M].[S.l.]:Vintage, 1992.

大数据背景下的虚拟地理认知实验方法

Virtual Geographic Cognition Experiment in Big Data Era

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	张良培, 何江, 杨倩倩, 肖屹, 袁强强. 数据驱动的多源遥感信息融合研究进展[J]. 测绘学报, 2022, 51(7): 1317-1337.
[2]	程涛, 张洋, James Haworth. 基于网络和图的时空智能——概念、方法和应用[J]. 测绘学报, 2022, 51(7): 1629-1639.
[3]	龚健雅, 宦麟茜, 郑先伟. 影像解译中的深度学习可解释性分析方法[J]. 测绘学报, 2022, 51(6): 873-884.
[4]	张勤, 赵超英, 陈雪蓉. 多源遥感地质灾害早期识别技术进展与发展趋势[J]. 测绘学报, 2022, 51(6): 885-896.
[5]	龚健雅, 张觅, 胡翔云, 张展, 李彦胜, 姜良存. 智能遥感深度学习框架与模型设计[J]. 测绘学报, 2022, 51(4): 475-487.
[6]	毛文婧, 王卫林, 焦利民, 刘安宝. 基于深度学习的中国连续空间覆盖PM_2.5浓度预报[J]. 测绘学报, 2022, 51(3): 361-372.
[7]	金飞, 官恺, 刘智, 韩佳容, 芮杰, 李庆高. 无监督密集匹配特征提取网络性能分析[J]. 测绘学报, 2022, 51(3): 426-436.
[8]	何直蒙, 丁海勇, 安炳琪. 高分辨率遥感影像建筑物提取的空洞卷积E-Unet算法[J]. 测绘学报, 2022, 51(3): 457-467.
[9]	姚翔宇, 黄丽娜, 于洋. 数字环境下面状要素分级设色的适宜方案分析[J]. 测绘学报, 2022, 51(2): 290-300.
[10]	张永生, 张振超, 童晓冲, 纪松, 于英, 赖广陵. 地理空间智能研究进展和面临的若干挑战[J]. 测绘学报, 2021, 50(9): 1137-1146.
[11]	艾廷华. 深度学习赋能地图制图的若干思考[J]. 测绘学报, 2021, 50(9): 1170-1182.
[12]	龚健雅, 许越, 胡翔云, 姜良存, 张觅. 遥感影像智能解译样本库现状与研究[J]. 测绘学报, 2021, 50(8): 1013-1022.
[13]	杨必胜, 韩旭, 董震. 适用于城市场景大规模点云语义标识的深度学习网络[J]. 测绘学报, 2021, 50(8): 1059-1067.
[14]	陶超, 阴紫薇, 朱庆, 李海峰. 遥感影像智能解译:从监督学习到自监督学习[J]. 测绘学报, 2021, 50(8): 1122-1134.
[15]	晏雄锋, 艾廷华, 杨敏, 郑建滨. 地图空间形状认知的自编码器深度学习方法[J]. 测绘学报, 2021, 50(6): 757-765.