个人简介
刘启亮,1986年生,山东龙口人,博士,副教授,硕/博士生导师,湖南省自然科学基金优秀青年基金获得者。主要研究方向:空间聚类分析理论方法及其在智慧城市、矿产资源勘查中的应用。先后主持国家自然科学基金(面上、青年)、国防科技基础加强计划(173计划)项目、国家重点研发项目子课题、湖南省自然科学基金(优青、面上、青年)等国家级与省级科研项目10余项,并获得中南大学“猎英计划”海外高水平人才引进计划、第六批创新驱动计划资助,以第一/通讯作者在国际地理信息、地理计算顶级刊物International Journal of Geographical Information Science、Geographical Analysis等发表SCI/SSCI期刊论文20余篇,发表第一/通讯作者EI期刊论文10余篇(在国内测绘地理信息最权威刊物《测绘学报》发表学术论文6篇),出版学术专著与教材3部;长期担任International Journal of Geographical Information Science、IEEE Transactions on Knowledge and Data Engineering、Knowledge-based Systems、Applied Soft Computing、IEEE Transactions on Industrial Informatics、Computers, Environment and Urban Systems、 Cartographic and Geographical Information Science、Computers&Geosciences、Transactions in GIS 及《测绘学报》、《计算机研究与发展》等10余个国内外地理信息、数据挖掘权威刊物审稿人;曾担任The 20th Pacific Asia Conference on Information System、IEEE International Conference on Big Data Analysis等国际会议专业委员会委员;以主要完成人获得教育部自然科学二等奖、科技进步二等奖、中国地理信息科技进步一等奖以及中国测绘科技进步二等奖。
讲授课程:地理信息系统原理与应用(本科生)、空间分析(本科生)、空间统计基础(本科生)、空间数据挖掘(本科生);科技论文写作(硕士/博士),测绘遥感工程应用(博士)
招生方向:地学数据挖掘、时空统计
招生专业:地理信息科学、测绘工程、遥感科学与技术、地质资源、统计学、计算机科学及其他相关专业
1. 重视理论分析和实际应用能力的提高,尤其是应用数学和GIS工具分析和解决地学问题的能力;
2. 培养具有扎实地学基础和坚实地理信息空间分析技术的研究与应用型人才;
3. 与英国伦敦大学学院、香港理工大学等研究单位保持长期合作关系,为学生提供国内外学术交流的平台,拓宽国际视野。
欢迎有志于地学数据挖掘理论与应用研究的同学报考硕士/博士研究生、开展博士后科研合作!
科研奖励:
4. 刘启亮 (5/10), 城乡规划业务全生命周期智能化管理平台建设与应用,教育部科技进步二等奖,2016.(获奖人员:张新长,邓敏,李军,郭泰圣,刘启亮,罗国玮,黄健锋,孙颖,石岩,赵元)
3. 刘启亮 (2/10),地理空间数据挖掘的理论、方法及应用,中国测绘科技进步二等奖,2014.(获奖人员:邓敏,刘启亮,王佳璆,沙宗尧,陈江平,石岩,李朝奎,邓浩,何晋强,李光强)
2. 刘启亮 (3/5),地理空间关系建模的理论与方法,教育部自然科学奖二等奖,2014.(获奖人员:邓敏,李志林,刘启亮,刘文宝,刘新)
1. 刘启亮 (4/12),多源多尺度空间数据不一致性探测处理的理论与方法,中国地理信息科技进步奖一等奖,2013.(获奖人员:邓敏,李志林,刘文宝,刘启亮,赵彬彬,朱建军,华亮春,刘慧敏,李光强,梅小明,彭东亮,石岩)
教材与专著:
4. 邓敏, 刘启亮, 石岩, 唐建波. 2021. 空间分析(第二版). 北京:测绘出版社
3. 邓敏, 樊子德, 刘启亮. 2016. 空间分析实验教程. 北京:测绘出版社
2. 邓敏, 刘启亮, 吴静. 2015. 空间分析. 北京:测绘出版社
1. 邓敏, 刘启亮, 李光强, 黄健柏. 2011. 空间聚类分析及应用. 北京:科学出版社
近期代表性工作:
9. Liu QL, Yang J, Deng M, Liu WK, Xu R. 2022. BiFlowAMOEBA for the identification of arbitrarily shaped clusters in bivariate flow data. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2022.2072850
8. Liu WK, Liu QL(corresponding author), Deng M, Cai JN, Yang J. 2021. Discovery of statistically significant regional co-location patterns on urban road networks. International Journal of Geographical Information Science, DOI:10.1080/13658816.2021.1981335
7. Liu QL, Yang J, Deng M, Song C, Liu WK. 2021. SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows. International Journal of Geographical Information Science, DOI:10.1080/13658816.2021.1899184
6. Liu QL, Liu WK, Deng M, Cai JN, Liu YL. 2021. An adaptive detection of multilevel co-location patterns based on natural neighborhoods. International Journal of Geographical Information Science, 35(3): 556-581
5. Liu QL, Wu ZH, Deng M, Liu WK, Liu YL. 2020. Network-constrained bivariate clustering method for detecting urban black holes and volcanoes.International Journal of Geographical Information Science, 34(10):1903-1929
4. Liu QL, Liu WK, Tang JB, Deng M, Liu YL. 2019. Two-stage permutation tests for determining homogeneity within a spatial cluster. International Journal of Geographical Information Science, 33(9):1718-1738
3. Deng M, Cai JN, Liu QL(corresponding author), He ZJ, Tang JB. 2017. Multi-level method for discovery of regional co-location patterns.International Journal of Geographical Information Science, 31(9): 1849-1870
2. Liu QL, Zhu SC, Deng M, Liu WK, Wu ZH. 2020. A spatial scan statistic to detect spatial communities of vehicle movements on urban road networks. Geographical Analysis, DOI:10.1111/gean.12278
1. Cai JN, Deng M., Liu QL(corresponding author), He ZJ, Tang JB, Yang XX. 2019. Nonparametric significance test for discovery of network-constrained spatial co-Location patterns. Geographical Analysis, 51(1): 3-22
其他英文论文:
15. Liu QL, Huan WH,Deng M, Zheng XL, Yuan HT. 2021. Inferring urban land use from multi-source urban mobility data using latent multi-view subspace clustering. ISPRS International Journal of Geo-Information, 10(5), 274
14. Liu QL, Liu WK,Tang JB, Deng M, Liu YL. 2019. Permutation-test-based clustering method for detection of dynamic patterns in spatio-temporal datasets. Computers, Environment and Urban Systems, 2019,75:204-216
13. Cai JN, Liu QL(corresponding author), Deng M, Tang JB, He ZJ. 2018. Adaptive detection of statistically significant regional spatial co-location patterns. Computers, Environment and Urban Systems, 68:53-63
12. Li ZL, Liu QL(corresponding author), Tang JB, Deng M. 2018.An adaptive method for clustering of spatio-temporal events.Transactions in GIS,22(1):323-347
11. Deng M, Yang WT, Liu QL(corresponding author), Jin R, Xu F, Zhang YF. 2018. Heterogeneous space–time artificial neural networks for space–time series prediction. Transactions in GIS, 22(1):183-201
10. Deng M, Tang JB, Liu QL(corresponding author), Wu F. 2018. Recognizing building groups for generalization: a comparative study. Cartography and Geographic Information Science, 45(3): 187-204.
9. Deng M, He ZJ, Liu QL(corresponding author),Cai JN, Tang JB. 2017. Multi-scale approach to mining significant spatial co-location patterns. Transactions in GIS, 22(5):1023-1039
8. Deng M, Fan ZD, Liu QL(corresponding author), Gong JY. 2016. A hybrid method for interpolating missing data in heterogeneous spatio-temporal datasets. ISPRS International Journal of Geo-Information, 3(13)
7. Liu QL, Li ZL, Deng M. 2015. Modeling the effect of scale on clustering of spatial points. Computers, Environment and Urban Systems, 2015,52:81-92.
6. Liu QL, Tang JB, Deng M, Shi Y. 2015. An iterative detection and removal method for detecting spatial clusters of different densities. Transaction in GIS, 19(1):82-106.
5. Liu QL, Deng M, Bi JT, Yang WT. 2014. A novel method for discovering spatio-temporal clusters of different sizes, shapes, and densities in the presence of noise. International Journal of Digital Earth, 2014,7(2):138-157
4. Deng M, Liu QL, Wang JQ. 2013. A general method of spatio-temporal clustering analysis. Science China Information Sciences,56:1-14
3. Liu QL, Deng M, Shi Y. 2013. Adaptive spatial clustering in presence of obstacles and facilitators. Computers & Geosciences, 56: 104-118.
2. Liu QL, Deng M, Wang JQ, Shi Y. 2012. A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity. Computers & Geosciences, 46: 296-309.
1. Deng M, Liu QL, Cheng T, Shi Y. 2011. An adaptive spatial clustering algorithm based on Delaunay triangulation. Computers, Environment and Urban Systems, 35:320-332
教育经历
[1] 2011.9-2014.9
香港理工大学 | 地理信息系统 | 哲学博士 | 博士研究生毕业
[2] 2008.9-2011.6
中南大学 | 大地测量学与测量工程 | 硕士学位 | 硕士研究生毕业
[3] 2004.9-2008.6
中南大学 | 信息物理工程/测绘工程 | 学士学位 | 大学本科毕业