武汉大学主页平台管理系统卢宾宾--中文主页--首页

卢宾宾

博士生导师硕士生导师
性别：男
出生日期：1984-07-05
学历：研究生毕业
学科：地图制图学与地理信息工程
地图学与地理信息系统
办公地点：教学实验大楼1301办公室
电子邮箱：

2020曾获荣誉当选：《武汉大学学报.信息科学版》2020年度优秀作者
2020曾获荣誉当选：湖北省测绘科技进步特等奖
2017曾获荣誉当选：武汉大学遥感信息工程学院“教书育人奖”荣誉称号
2020曾获荣誉当选：第十届MathorCup高校数学建模竞赛优秀指导教师
2016曾获荣誉当选：SuperMap杯第十四届全国高校GIS大赛制图组、地理分析应用组优秀指导教师奖
2017曾获荣誉当选：《Ecological Informatics》期刊优秀审稿人奖(Outstanding reviewer)

访问量：

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个人简介

卢宾宾 , 武汉大学遥感信息工程学院副教授，博士生导师，系副主任，新疆大学“天山学者”讲座教授，中国地理学会地理模型与地理信息分析委员会委员，中国优选法统筹法与经济数学研究会数学建模与算法分会理事;《Geo-spatial Information Science》(JCR二区)期刊青年编委，《Resources, Environment and Sustainability》期刊编委，《Frontiers in Plant Science 》、《International Journal of Environmental Research and Public Health》客座编辑。长期致力于地理加权建模技术框架、地理加权回归分析技术理论和开源工具开发等方面的空间统计领域研究，建立了包括地理加权回归分析、地理加权汇总统计量、地理加权主成分分析和地理加权判别分析等技术的空间异质性地理加权建模框架，开发和维护地理加权建模技术R函数包GWmodel和shp2graph, 最新推出了地理加权建模技术高性能软件工具GWmodelS, 学术成果发表在IJGIS、CEUS、Journal of Statistical Software、Spatial Statistics、EPB、GSIS、TGIS等期刊，已授权发明专利5项、软件著作权2项, 出版《R语言空间数据处理与分析实践教程》教材一部，主持国家自然科学基金面上项目1项、青年基金项目1项，“十四五”国家重点研发项目专题1项，协作主持国家自然科学基金民航联合重点基金2项、国家自然科学基金面上基金项目1项、国家自然科学基金民航联合培育基金项目1项。

学术主页：

‪Google Scholar‬ ORICD ResearchGate

招生2024级地图学与地理信息系统、摄影测量与遥感、测绘工程等专业硕士研究生，欢迎学生联系详谈！(QQ: 232460549)

团队公众号（下载GWmodelS软件需关注此公众号）：

最新成果(动态更新)：

[1] Lu, B.; Ge, Y.*; Shi, Y.; Zheng, J.; Harris, P. Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China. Spatial Statistics 2023, 53, 100723, doi:https://doi.org/10.1016/j.spasta.2022.100723.

[2] Lu, B.; Hu, Y.; Yang, D.; Liu, Y.; Liao, L.; Yin, Z.; Xia, T.; Dong, Z.; Harris, P.; Brunsdon, C.; Comber, L., & Dong, G*. GWmodelS: A software for geographically weighted models. SoftwareX 2023, 21, 101291, doi:https://doi.org/10.1016/j.softx.2022.101291.

[3] Chen X, Xie J, Xiao C, Lu B*, Shan J. Recurrent origin–destination network for exploration of human periodic collective dynamics. Transactions in GIS. 2021.

[4] Xu, G., Jiang, Y., Wang, S., Qin, K.*, Ding, J., Liu, Y., & Lu, B.*. Spatial Disparities of Self-reported COVID-19 Cases and Influencing Factors in Wuhan, China. Sustainable Cities and Society, 103485. 2022

[5] Hu Y, Lu B*, Ge Y*, Dong G. Uncovering Spatial Heterogeneity in Real Estate Prices via Combined Hierarchical Linear Model and Geographically Weighted Regression [J]. Environment and Planning B: Urban Analytics and City Science, 2022.

[6] Comber, A*; Brunsdon, C.; Charlton, M.; Dong, G.; Harris, R.; Lu, B*; Lü, Y.; Murakami, D.; Nakaya, T.; Wang, Y.; et al. A Route Map for Successful Applications of Geographically Weighted Regression. Geographical Analysis, 2023, 55 (1), 155-178., doi:https://doi.org/10.1111/gean.12316.

[7] Xiao R, Cao W, Liu Y, Lu B*. The impacts of landscape patterns spatio-temporal changes on land surface temperature from a multi-scale perspective: A case study of the Yangtze River Delta. Science of The Total Environment. 2022;821:153381.

[8] Lu, B.*, Hu, Y., Murakami, D., Brunsdon, C., Comber, A., Charlton, M., and Harris, P.: ‘High-performance solutions of geographically weighted regression in R’, Geo-spatial Information Science, 2022, 25:4, 536-549, DOI: 10.1080/10095020.2022.2064244

[9] Han, C., Zheng, J.*, Guan, J., Yu, D., and Lu, B.* : ‘Evaluating and simulating resource and environmental carrying capacity in arid and semiarid regions: A case study of Xinjiang, China’, Journal of Cleaner Production, 2022, 338, pp. 130646

[10] Qin, K., Wang, Q., Lu, B.*, Sun, H., & Shu, P. (2022). Flight Anomaly Detection via a Deep Hybrid Model. Aerospace, 9(6). doi:10.3390/aerospace9060329

[11] Wang Q, Qin K, Lu B*, Sun H, Shu P. Time-feature attention-based convolutional auto-encoder for flight feature extraction. Scientific Reports. 2023;13:14175.

[12] Murakami D, Tsutsumida N, Yoshida T, Nakaya T, Lu B, Harris P. A linearization for stable and fast geographically weighted Poisson regression. International Journal of Geographical Information Science. 2023:1-22.

[12] 董锦涛, 王纯, 孙华波, 卢宾宾*, 2022. 基于QAR数据的中国民航飞行排放清单估计研究. 环境科学学报 42 (12), 322-331.

[13]秦昆, 张凯, 阮建平, 卢宾宾*, 邢玲丽, 叶茹琪, 喻雪松, 周扬, 刘东海, 秦育罗. 国际航空网络与国际关系网络的特征提取及关联分析[J]. 同济大学学报(自然科学版), 2023, 51(7): 986-993..

著作

[1] 黄荣顺, 孙华波, 卢宾宾, 苗凌云, 2023. 飞行安全时空大数据理论与实践科学出版社, 北京.

早期代表性成果：

[1] Lu, B.*, Charlton, M., Harris, P., Fotheringham, A.S., 2014. Geographically weighted regression with a non-euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science 28 (4), 660-681.

[2] Lu, B.*, Harris, P., Charlton, M., Brunsdon, C., 2014. The gwmodel r package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo-spatial Information Science 17 (2), 85-101.

[3] Gollini, I., Lu, B., Charlton, M., Brunsdon, C., Harris, P., 2015. Gwmodel: An r package for exploring spatial heterogeneity using geographically weighted models. Journal of Statistical Software 63 (17), 1-50.

[4] Lu, B.*, Charlton, M., Brunsdon, C., Harris, P., 2016. The minkowski approach for choosing the distance metric in geographically weighted regression. International Journal of Geographical Information Science 30 (2), 351-368.

[5] Lu, B.*, Brunsdon, C., Charlton, M., Harris, P., 2017. Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science 31 (5), 982-998.

[6] Lu, B.*, Yang, W., Ge, Y., Harris, P., 2018. Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths. Computers, Environment and Urban Systems 71, 41-57.

[7] Lu, B.*, Sun, H., Harris, P., Xu, M., Charlton, M., 2018. Shp2graph: Tools to convert a spatial network into an igraph graph in r. ISPRS International Journal of Geo-Information 7 (8).

[8] Lu, B.*, Brunsdon, C., Charlton, M., Harris, P., 2019. A response to ‘a comment on geographically weighted regression with parameter-specific distance metrics’. International Journal of Geographical Information Science 33 (7), 1300-1312.

[9] Murakami, D., Lu, B., Harris, P., Brunsdon, C., Charlton, M., Nakaya, T., Griffith, D.A., 2019. The importance of scale in spatially varying coefficient modeling. Annals of the American Association of Geographers 109 (1), 50-70.

[10] 卢宾宾*, 葛咏, 秦昆, 郑江华, 2020. 地理加权回归分析技术综述. 武汉大学学报 ● 信息科学版 45 (9), 1356-1366.