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地理计算与可持续发展

姓       名：乐阳

职       称：教授

研究方向：面向城市可持续发展的社会地理计算理论与方法

电子邮箱：yueyang@szu.edu.cn

个人介绍

博士、教授、博导，现任深圳市空间信息智能感知与服务重点实验室主任、领域权威期刊《Computers, Environment and Urban Systems》副主编、 ACM SIGSPATIAL中国分会副主席、中国遥感应用协会社会遥感地理计算专业委员会副理事。曾获微软亚洲研究院青年铸星学者，任职于武汉大学测绘遥感信息工程国家重点实验室，2013年加入深圳大学。

主要从事城市大数据及AI、社会地理计算及城市可持续发展的交叉研究。担任国家“十三五”国家重点研发计划及多项国家自然科学基金负责人，以及来自微软、西门子、腾讯、华为等工业界的合作科研项目，发表论文百余篇，中英文皆有高被引论文。获广东省高等教育教学成果特等奖、“测绘地理信息创新巾帼人物”称号。


Dr. Yang Yue is a professor of urban informatics and the founding head of the Department of Urban Informatics at Shenzhen University, where she also directs the Shenzhen Key Laboratory of Spatial Smart Sensing and Services. She is an associate editor of the SSCI top journal Computers, Environment and Urban Systems (since 2021), vice-chair of the ACM SIGSPATIAL China Chapter (since 2019), and co-chair of the Special Committee on Social & Remote Sensing for Computational Social Science (since 2023).

Dr. Yue got degrees from the University of Hong Kong and Wuhan University, respectively, with an interdisciplinary background in geomatics, transportation, and urban modeling. Her research and teaching focus on innovations at the nexus of urban big data, GeoAI, and social sustainability. She has led four national research projects over the last decade and collaborated with top industrial partners such as Tencent, Huawei, and Microsoft Research Asia. She has authored approximately 100 peer-reviewed articles, including highly-cited works in both English and Chinese. She was recognized as one of the top 20 female innovators by the Chinese Society for Geodesy, Photogrammetry, and Cartography (2023).

Her education philosophy revolves around nurturing the next generation of geospatial professionals with a focus on smart and sustainable cities, and her teaching approach emphasizes fostering critical thinking and problem-solving skills. She won teaching and course awards from the university, Guangdong province, and the National Ministry of Education.


讲授课程： 

    本科：思辨与写作，城市时空数据分析方法与案例，城市信息技术前沿（城市规划专业）

    博士生：研究方法与论文写作


Google 学术主页：http://scholar.google.com/citations?user=Or3pqN8AAAAJ&hl=en


欢迎有GIS、计算机、地理、城市研究背景的学生报考硕士或博士研究生。


研究项目              
1 城市空间结构知识图谱与构建方法，国家自然科学基金面上项目（42171449）, 2022-2025 年
2 城市多规数据融合与动态认知平台关键技术研究与示范（2018YFB2100700），国家重点研发计划项目，2019-2022 年
3 大数据支撑的城市发展状态动态认知与评估技术研究 (2018YFB2100704), 国家重点研发计划（课题）, 2019.07-2022.06 年
4 大数据驱动的空间选择行为机制研究  (41671387), 国家自然科学基金面上项目,  2017-2020 年
  5 基于多源位置数据的城市居民出行调查与分析系统技术研发 (CXZZS20150504141623042), 深圳市科创委, 2015-2017 年
6 基于空间商业智能分析的城市综合体吸引力及选址研究 (JCYJ20130329144141856), 深圳市科创委, 2014-2016 年
7 社交网络多尺度空间社区探测与识别 (CCF Tencent ARG 20130115), CCF-腾讯犀牛鸟, 2013-2014 年
8 面向异常交通状态快速识别的移动对象流数据分析与管理 (41171348), 国家自然科学基金面上项目, 2012-2015 年
9 超大城市区域的可持续交通与均等化：模式、机理与治理(71961137003), 国家自然科学基金（中欧合作项目）, 负责人-李清泉, 参与人-乐阳，涂伟等, 2019-2022 年
10 基于多源时空数据群体活动识别与城市空间结构演化研究(JCYJ20121019111128765), 深圳市战略性新兴产业发展专项资金项目, 负责人-李清泉, 参与人-乐阳, 2012-2015 年


论文
1 Gao, Q., Yue, Y., Tu, W., Cao, J., & Li, Q. (2021). Segregation or integration? Exploring activity disparities between migrants and settled urban residents using human mobility data. Transactions in GIS, 25(6), 2791–2820. https://doi.org/10.1111/tgis.12760
2 Wang, Z., Yue, Y., He, B., Nie, K., Tu, W., Du, Q., & Li, Q. (2021). A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data. International Journal of Geographical Information Science, 35(1), 116–134. https://doi.org/10.1080/13658816.2020.1798967
3 Zhou, X., Chen, Z., Yeh, A. G., & Yue, Y. (2021). Workplace segregation of rural migrants in urban China: A case study of Shenzhen using cellphone big data. Environment and Planning B: Urban Analytics and City Science, 48(1), 25–42. https://doi.org/10.1177/2399808319846903
4 Gao QL, Li Q. , Zhuang Y.,  Yue Y.,  Liu ZZ, SQ Li, D Sui. (2020). Urban commuting dynamics in response to public transit upgrades: A big data approach. PloS ONE 14 (10), e0223650
5 Jiang JC,  Li Q., Tu, W., SL Shaw & Yue, Y. (2019) A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility, International Journal of Geographical Information Science, 33:3, 618-644, DOI: 10.1080/13658816.2018.1552964
6 Gao, Q.L., Li, Q., Yue, Y., Zhuang, Y., Chen, Z.-P., & Kong, H. (2018). Exploring changes in the spatial distribution of the low-to-moderate income group using transit smart card data. Computers, Environment and Urban Systems, 72, 68–77. https://doi.org/10.1016/j.compenvurbsys.2018.02.006
7 Zhou, X., Yeh, A. G. O. , & Yue, Y. . (2018). Spatial variation of self-containment and jobs-housing balance in shenzhen using cellphone big data. Journal of Transport Geography, 68, 102-108.
8 Yue, Y., Zhuang, Y., Yeh, A. G., Xie, J., Ma, C., & Li, Q. (2017). Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science, 31(4):658-675.
9 Zhou, M., Yue, Y., Li, Q., & Wang, D. (2016). Portraying Temporal Dynamics of Urban Spatial Divisions with Mobile Phone Positioning Data: A Complex Network Approach. ISPRS International Journal of Geo-Information, 5(12), 240.
10 Zou, H., Yue Y., & Li Q., 2014, Urban Traffic State Explained by Road Networks and Spatial Variance: Approach Using Floating Car Data, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2467:40-48.
11 乐阳，李清泉， 郭仁忠. 融合式研究趋势下的地理信息教学体系探索, 地理学报 75 (8), 1790-179
12 李丁杰, 乐阳, & 郭莉. (2021). 基于人口合成技术的居民出行调查数据扩样. 交通科技与经济, 23(5), 24.
13 李水泉, 乐阳, 李清泉, & 庄严. (2021). 一种Haar小波概要的流数据压缩方法. 武汉大学学报∙ 信息科学版, 46(8), 1216–1223.

书目章节：

  • Yeh, A. G. O., Yue, Y., Zhou, X., & Gao, Q.-L. (2020). Big data, urban analytics and the planning of smart cities. In Handbook of Planning Support Science. Edward Elgar Publishing https://www.elgaronline.com/view/edcoll/9781788971072/9781788971072.00020.xml

  • Tu, W., Li, Q., Zhang, Y., & Yue, Y. (2021). User-generated content and its applications in urban studies. In W. Shi, M. F. Goodchild, M. Batty, M.-P. Kwan, & A. Zhang (Eds.), Urban Informatics (pp. 523–539). Springer. https://doi.org/10.1007/978-981-15-8983-6_29