[01] Optimizing Intra-Urban Epidemic Control with Spatiotemporal Orderliness via Deep Reinforcement Learning. (Under Review)

[02] Estimating the Two Consecutive Epidemic Waves of SARS-CoV-2 Omicron in Shenzhen, China from November 2022 to July 2023: A Modeling Study Based on Multi-Source Monitoring and Mobility Data. (Under Review)

[03]Liu, K., Shi, Y., Wang, S., Zhao, X., & Yin, L. (2024). Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area. Computational Urban Science, 4(1), 1-19.(EI)

[04]Cao, Z., Liu, K., Jin, X., Ning, L., Yin, L., & Lu, F. (2024). STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generation. International Journal of Geographical Information Science, 1-28.(SCI)

[05] Urban-EPR: A universal model for simulating intra-urbanhuman mobility. (Under Review)

[06] Yuxiao Luo, Zhongcai Cao, Xin Jin, Kang Liu*, Ling Yin*.Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models. The 1st Workshop on Generative AI for Mobility Data, June 24, 2024, Brussels, Belgium [link]

[07] Zhu, K., Yin, L*, Liu, K., Liu, J., Shi, Y., Li, X., ... & Du, H. (2024). Generating synthetic population for simulating the spatiotemporal dynamics of epidemics. PLOS Computational Biology, 20(2), e1011810. (SCI) [link]

[08] Liu, K., Jin, X., Cheng, S., Gao, S., Yin, L., & Lu, F. (2024). Act2Loc: a synthetic trajectory generation method by combining machine learning and mechanistic models. International Journal of Geographical Information Science, 38(3), 407-431. (SCI) [link]

[09] Kunhao Shi, Kemin Zhu, Ling Yin*. Enhancing the Performance of an Agent-based Epidemic Model with a Large-Scale Contact Network Using FLAME GPU 2[C]. Proceedings of 2023 CCF National Annual Conference on High Performance Computing, 2023.

[10] 陈洁，周莹菲，尹凌*，李烨，苗芬，裴韬，刘康，任倩茹，李璇，张浩，李子垠，奚桂锴．2020年初COVID-19出院患者日常生活时空行为分析．地理学报. 2023, 78(4): 1-16. (EI) [link]

[11] 培训教材：《现代流行病学》，香港赛马会四川省卫生应急培训项目急性传染病防控子项目培训教材，四川省疾病预防控制中心，2022.6

[12] 合著：《新型冠状病毒》，汉斯出版社，2022.10

[13] Zhang, H., L. Yin*, L. Mao, S. Mei, T. Chen, K. Liu, and S. Feng, Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data. Frontiers in Public Health, 2022. 10. (SCI) [link]

[14] Jin, C., Zhang, H., Yin, L., Zhang, Y., & Feng, S. Z. (2022). Optimize data-driven multi-agent simulation for COVID-19 transmission. BMC bioinformatics, 23(1), 260. (SCI) [link]

[15] 赖圣杰，冯录召，冷志伟，吕欣，李瑞云，尹凌，骆威，李中杰，兰亚佳，杨维中. 传染病暴发早期预警模型和预警系统概述与展望. 中华流行病学杂志, 2021, 42(8): 1330-1335. (CSCD) [link]

[16] 尹 凌*, 刘 康, 张 浩, 奚桂锴, 李 璇, 李子垠, 薛建章. 耦合人群移动的COVID-19传染病模型研究进展[J]. 地球信息科学学报,2021, 23(11): 1894-1909.
Ling Yin*, Kang Liu, Hao Zhang, Guikai Xi, Xuan Li, Ziyin Li, Jianzhang Xue. Integrating Human Mobility into the Epidemiological Models of COVID-19: Progress and Challenges[J]. Journal of Geo-information Science,2021, 23(11): 1894-1909.(CSCD).      [link]

[17] Guikai Xi, Ling Yin*, Kang Liu. Intra-urban Region-based Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network Enhanced by Spatial Context. The 10th International Workshop on Urban Computing. (UrbComp 2021), held in conjunction with the 29th ACM SIGSPATIAL, 2021. (EI).      [link]

[18] 赖圣杰，冯录召，冷志伟，吕欣，李瑞云，尹凌，骆威，李中杰，兰亚佳，杨维中. 传染病暴发早期预警模型和预警系统概述与展望. 2021, 42(8): 1330-1335. (CSCD).      [link]

[19] Ling Yin*, Hao Zhang, Yuan Li, Kang Liu, Tianmu Chen, Wei Luo, Shengjie Lai, Ye Li, Xiujuan Tang, Li Ning, Shengzhong Feng, Yanjie Wei, Zhiyuan Zhao, Ying Wen, Liang Mao, Shujiang Mei. A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities. Journal of the Royal Society Interface, 2021, 18(181): 20210112. (SCI).      [link]

[20] Xiping Yang, Zhixiang Fang, Yang Xu, Ling Yin, Junyi Li, Zhiyuan Zhao. Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data. Transactions in GIS, 2021, (11). (SCI).      [link]

[21] Sihui Guo, Tao Pei, Shuyun Xie, Ci Song, Jie Chen, Yaxi Liu, Hua Shu, Xi Wang, Ling Yin. Fractal dimension of job-housing flows: A comparison between Beijing and Shenzhen. Cities, 2021, 112: 103120. (SCI).      [link]

[22] Kang Liu, Ling Yin*, Meng Zhang, Min Kang, Aipeng Deng, Qinglan Li, Tie Song, Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images. Infectious Diseases of Poverty, 10(1), 1-16. (2021).      [link]

[23] Ruxin Wang, Chaojie Ji, Zhiming Jiang, Yongsheng Wu*, Ling Yin*, and Ye Li*. "A Short-Term Prediction Model at the Early Stage of the COVID-19 Pandemic Based on Multisource Urban Data." IEEE Transactions on Computational Social Systems (2021).      [link]

[24] 张 浩，尹 凌*，刘 康，毛 亮，冯圣中，陈 洁，梅树江.深圳市快速抑制COVID-19疫情的非药物干预措施效果评估-基于智能体的建模研究[J].地球信息科学学报,2021. Hao Zhang , Ling Yin , Kang Liu , Liang Mao , Shengzhong Feng , Jie Chen , Shujiang Mei . 2021. Effectiveness of Non-pharmaceutical Interventions on suppressing the 1st wave of COVID-19 epidemic in Shenzhen: an agent-based modelling study[J]. Journal of Geo-information Science, (2021). [link]

[25] Ling Yin, Nan Lin, and Zhiyuan Zhao. "Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data." Cities (2020): 103013.（SSCI）      [link]

[26] Sihui Guo, Tao Pei, Shuyun Xie, Ci Song, Jie Chen, Yaxi Liu, Hua Shu, Xi Wang, Ling Yin. “ Fractal dimension of job-housing flows: A comparison between Beijing and Shenzhen.” Cities (2021): 103120.（SSCI）      [link]

[27] Kang Liu, Meng Zhang, Guikai Xi, Aiping Deng, Tie Song, Qinglan Li, Min Kang, Ling Yin. “Enhancing fine-grained intra-urban dengue forecasting by integrating spatial interacion of human movements between urban regions.” PLOS Neglected Tropical Diseases. (SCI)      [link]

[28] Ze-Yu Zhao, Yuan-Zhao Zhu, Jing-Wen Xu, Shi-Xiong Hu, Qing-Qing Hu, Zhao Lei, Jia Rui, Xing-Chun Liu, Yao Wang, Meng Yang, Li Luo, Shan-Shan Yu, Jia Li, Ruo-Yun Liu, Fang Xie, Ying-Ying Su, Yi-Chen Chiang, Ben-Hua Zhao, Jing-An Cui, Ling Yin, Yan-Hua Su, Qing-Long Zhao, Li-Dong Gao & Tian-Mu Chen. "A five-compartment model of age-specific transmissibility of SARS-CoV-2." Infectious diseases of poverty 9.1 (2020): 1-15.(SCI)      [link]

[29] Bing He, Bo Kong, Ling Yin, Qin Wu, Jin xing Hu, Dian Huang, and Zhanwu Ma."Discovering the Graph-based Flow Patterns of Car Tourists Using License Plate Data: A Case Study in Shenzhen, China." Journal of Advanced Transportation 2020 (2020). (SCI)       [link]

[30] Yang Xu, Xinyu Li, Shih-Lung Shaw, Feng Lu, Ling Yin, and Bi Yu Chen. "Effects of data preprocessing methods on addressing location uncertainty in mobile signaling data." Annals of the American Association of Geographers (2020): 1-25. (SCI)      [link]

[31] Tian-Mu Chen, Jia Rui, Qiu-Peng Wang, Ze-Yu Zhao, Jing-An Cui, and Ling Yin."A mathematical model for simulating the phase-based transmissibility of a novel coronavirus." Infectious diseases of poverty 9.1 (2020): 1-8.(SCI)      [link]

[32] 合著：《空间流行病学》，高等教育出版社，2020.7

[33] Ling Yin, Nan Lin, Xiaoqing Song, Shujiang Mei, Shih-Lung Shaw, Zhixiang Fang, Qinglan Li, Ye Li, Liang Mao, 2020. Space-Time Personalized Short Message Service (SMS) for Infectious Disease Control – Policies for Precise Public Health. Applied Geography. (SSCI)      [link]

[34] Kang Liu, Ling Yin*, Feng Lu, Naixia Mou, 2020. Visualizing and exploring POI configurations of urban regions on POI-type semantic space. Cities, 99: 102610. （SSCI)      [link]

[35] Kang Liu, Peiyuan Qiu, Song Gao, Feng Lu, Jincheng Jiang*, Ling Yin*, 2019. Investigating urban metro stations as cognitive places in cities using points of interest. Cities, 97.（SSCI） [link]

[36] Kang Liu, Ling Yin*, Zhanwu Ma, Fan Zhang, Juanjuan Zhao, 2019. Investigating physical encounters of individuals in urban metro systems with large-scale smart card data. Physica A : Statistical Mechanics and its Applications. (SCI)      [link]

[37] Ling Yin, Jie Chen, Hao Zhang, Zhile Yang, Qiao Wan, Li Ning, Jinxing Hu, Qi Yu, 2019. Improing emergency evacuation planning with mobile phone location data. Environment and Planning B: Urban Analytics and City Science. (SSCI)      [link]

[38] Fan Zhang, Kang Liu, Ling Yin*, Fan Zhang, 2019. Investigating Evolutions of Metro Station Functions in Shenzhen with Long-term Smart Card Data. In the proceedings of Geoinformatics in Sustainable Ecosystem and Society & Geospatial Artificial Intelligence for Urban Computing. (EI)

[39] 周志峰,李学云,廖玉学,尹淩,许玉成,梁静,梅树江. 前瞻性时空重排扫描统计量法在深圳市MUMPS聚集性疫情早期预警中的应用[J]. 实用预防医学, 2020, 27(1):16-20.      [link]

[40] Qiao Wan, Ling Yin*, Liang Mao, Li Wang, Shujiang Mei, Qinglan Li, Kang Liu, 2019. Simulating human host interventions to control intra-urban dengue outbreaks with a spatially individual-based model. In Proceedings of the IEEE International Conference on Real-time Computing and Robotics. (EI)           [link]

[41] Jiangping Zhou, Yuling Yang, Peiqin Gu, Ling Yin, Fan Zhang, Fan Zhang, Dong Li, 2019. Can TODness improve (expected) performances of TODs? An exploration facilitated by non-traditional data. Transportation Research Part D: Transport and Environment, 74, 28-47. (SCI)      [link]

[42] Xiping Yang, Zhixiang Fang, Ling Yin, Junyi Li, Shiwei Lu, Zhiyuan Zhao, 2019. Revealing the relationship of human convergence–divergence patterns and land use: A case study on Shenzhen City, China. Cities, 95, 102384 (SSCI)      [link]

[43] Xiping Yang, Zhixiang Fang, Yang Xu, Ling Yin, Junyi Li, Shiwei Lu, 2019. Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data, Journal of Transport Geography, 78, 29-40. (SSCI)      [link]

[44] Zhiyuan Zhao, Shih-Lung Shaw, Ling Yin*, Zhixiang Fang, Xiping Yang, Fan Zhang, Sheng Wu, 2019. The effect of temporal sampling intervals on typical human mobility indicators obtained from mobile phone location data. International Journal of Geographical Information Science, 33:7, 1471-1495 (SCI)      [link]

[45] Guikai Xi, Ling Yin, Ye Li, and Shujiang Mei, 2018. A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale. In 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI’18), November 6, 2018, Seattle, WA, USA. (EI)      [link]

[45] 萧世瑜，方志祥，陈碧宇，尹凌，陈洁，杨喜平，《城市人群活动GIS分析》，科学出版社，2018.1

[47] Zhihan Fang, Fan Zhang, Ling Yin, Desheng Zhang, 2018. MutliCell: Urban Population Modeling based on Multiple Cellphone Networks. In: The 2018 acm international joint conference on pervasive and ubiquitous computing, 8-12 October 2018 Singapore.      [link]

[48] Jialu Xie, Ling Yin, Liang Mao, 2018. A Modeling Framework for Individual-based Urban Mobility Based on Data Fusion. In: International Association of Chinese Professionals in Geographic Information Sciences, 28-30 June 2018 Kunming, Yunnan, China. (EI)      [link]

[49] Xiping Yang, Zhixiang Fang, Ling Yin, Shiwei Lu, 2018. Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China. Sustainability, 10(5), 1435. (SCI)      [pdf]

[50] Zhiyuan Zhao, Ling Yin, Shih‐Lung Shaw, Zhixiang Fang, Xiping Yang, Fan Zhang, 2018. Identifying stops from mobile phone location data by introducing uncertain segments. Transactions in GIS, 22(4), 958-974. (SSCI)      [link]

[51] Zhiyuan Zhao, Ling Yin, Zhixiang Fang, Shihlung Shaw, Xiping Yang, 2018. Impacts of Temporal Sampling Intervals on Stay Detection and Movement Network Construction in Trajectory Data. Geomatics and Information Science of Wuhan University, 43(8): 1152-1158.
赵志远,尹凌,方志祥,萧世伦,杨喜平. 轨迹数据的时间采样间隔对停留识别和出行网络构建的影响[J]. 武汉大学学报·信息科学版, 2018, 43(8): 1152-1158. (CSCD)      [link]

[52] Nan Lin, Ling Yin, Zhiyuan Zhao, 2018. Detecting Individual Stay Areas from Mobile Phone Location Data Based on Moving Windows. Journal of Geo-information Science, 20(6): 762-771.
林楠,尹凌,赵志远. 基于滑动窗口的手机定位数据个体停留区域识别算法[J]. 地球信息科学学报, 2018, 20(6): 762-771. (CSCD)      [link]

[53] Jingwei Zhu, Zhixiang Fang, Xiping Yang, Ling Yin, 2018. Flow Synchronization of Mobile Communication Network in Cities Areas. Journal of Geo-information Science, 20(6): 844-853.
朱菁玮,方志祥,杨喜平,尹凌. 城市邻近基站间人群流动时空变化同步性分析[J]. 地球信息科学学报, 2018, 20(6): 844-853. (CSCD)      [link]

[54] Xiping Yang, Zhixiang Fang, Ling Yin, 2018. Exploring the Relationship between Urban Spatial Structure and the Stability of Human Convergence-divergence. Journal of Geo-information Science, 20(6): 791-798.
杨喜平,方志祥,尹凌. 城市空间结构要素与人群聚散稳定性的关联性探索[J]. 地球信息科学学报, 2018, 20(6): 791-798. (CSCD)      [link]

[55] Zhiyuan Zhao, Ling Yin, Jinxing Hu, Shengzhong Feng, Silin Huang, 2018. A road section selection algorithm for monitoring the OD flow of motor vehicle travels. Journal of Geo-information Science, 20(5): 656-664.
赵志远,尹凌,胡金星,冯圣中,黄思林. 面向机动车出行OD监测的目标路段选择算法[J]. 地球信息科学学报, 2018, 20(5): 656-664. (CSCD)      [link]

[56] Ling Yin, Renrong Jiang, Zhiyuan Zhao, Xiaoqing Song, Xiaoming Li, 2017. Exploring the Bias of Estimating 24-hour Population Distributions Using Call Detail Records. Journal of Geo-information Science, 19(6): 763-771.
尹凌,姜仁荣,赵志远,宋晓晴,李晓明. 利用手机通话位置数据估计城市24 h人口分布误差[J]. 地球信息科学学报, 2017, 19(6): 763-771. (CSCD)      [link]

[57] Wei Wang, Ling Yin, 2017. Privacy protection method for mobile phone location data based on matching point sets of frequent activity locations. Application Research of Computers, 34(3), 867-870.
汪伟,尹凌.基于频繁活动点集的手机位置数据隐私保护方法[J/OL].计算机应用研究, 2017, 34(03): 867-870. (CSCD)      [link]

[58] Zhixiang Fang, Xiping Yang, Yang Xu, Shih-Lung Shaw, Ling Yin, 2017. Spatiotemporal model for assessing the stability of urban human convergence and divergence patterns. International Journal of Geographical Information Science, 1-23. (SCI)      [link]

[59] Xiaoming Li, Zhihan Lv, Weixi Wang, Baoyun Zhang, Jinxing Hu, Ling Yin, Shengzhong Feng, 2016. WebVRGIS Based Traffic Analysis and Visualization System. Advances in Engineering Software, 93:1-8.（SCI） [link]

[60] Liang Mao, Ling Yin, Xiaoqing Song, Shujiang Mei, 2016. Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data. Acta Tropica, 162:188-195. (SCI) [link]

[61] Ling Yin, Jinxing Hu, Qian Wang, Wei Wang, Zhiling Cai, 2016. Re-identification risk versus data utility for aggregated mobility research using mobile phone location data. Journal of Integration Technology, 5(2), 19-28.
尹凌,胡金星,王倩,汪伟,蔡芷铃. 大规模手机位置数据研究中的个体重识别风险及其与数据可用性的关系[J]. 集成技术, 2016, 5(02): 19-28.      [link]

[62] Ziliang Zhao, Shih-Lung Shaw, Yang Xu, Feng Lu, Jie Chen, Ling Yin, 2016. Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science, 30(9), 1738-1762. (SCI)      [link]

[63] Yang Xu, Shih-Lung Shaw, Ziliang Zhao, Ling Yin, Zhixiang Fang, Qingquan Li, 2016. Another Tale of Two Cities — Understanding Human Activity Space using Actively Tracked Cellphone Location Data. Annals of the Association of American Geographers, 106:2, 489-502 (SSCI)      [link]

[64] Aftab Ahmed Chandio, Nikos Tziritas, Fan Zhang, Ling Yin, Cheng-Zhong Xu, 2016. Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories. Frontiers of Information Technology & Electronic Engineering, 17(12): 1305-1319. (SCI)      [link]

[65] Yang Xu, Shih-Lung Shaw, Zhixiang Fang, Ling Yin, 2016. Estimating Potential Demand of Bicycle Trips from Mobile Phone Data—An Anchor-Point Based Approach. ISPRS International Journal of Geo-information, 5(8), 131; doi:10.3390/ijgi5080131. (SCI)      [pdf]

[66] Xiaoqing Song, Zhixiang Fang, Ling Yin, Lihan Liu, Xiping Yang, Shih-Lung Shaw, 2016. A Method of Deriving the Boarding Station Information of Bus Passengers Based on Comprehensive Transfer Information Mined from IC Card Data. Journal of Geo-information Science, 18(8): 1060-1068.
宋晓晴,方志祥,尹凌,刘立寒,杨喜平,萧世伦. 基于IC卡综合换乘信息的公交乘客上车站点推算[J]. 地球信息科学学报, 2016, 18(8): 1060-1068. (CSCD)      [link]

[67] Ling Yin, Qian Wang, Shih-Lung Shaw, Zhixiang Fang, Jinxing Hu, Ye Tao, Wei Wang, 2015. Re-identification risk versus data utility for aggregated mobility research using mobile phone location data. PLoS ONE 10(10): e0140589. doi:10.1371/journal.pone.0140589 (SCI)      [pdf]

[68] Ling Yin, Shih-Lung Shaw, 2015. Exploring space-time paths in physical and social closeness spaces: a space-time GIS approach. International Journal of Geographical Information Science, 29(5): 742-761. (SCI)      [link]

[69] Yang Xu, Shih-Lung Shaw, Zhiliang Zhao, Ling Yin, Zhixiang Fang, Qingquan Li, 2015. Understanding aggregated human mobility patterns using passive mobile phone location data——A home based approach. Transportation, 42(4): 625-646. (SCI)      [link]

[70] Ling Yin, Jinxing Hu, Qi Yu, 2015. Accelerating agent-based emergency evacuation planning using a knowledge database based on population distribution regularity. In Proceedings of the 13th International Conference on GeoComputation. (EI)      [link]

[71] Jinlei Xu, Zhixiang Fang, Shih-Lung Shaw, Ling Yin, 2015. The Spatio-temporal Heterogeneity Analysis of Massive Urban Mobile Phone Users' Stay Behavior: A Case Study of Shenzhen City, 17(2): 197-205.
徐金垒, 方志祥, 萧世伦, 尹淩. 城市海量手机用户停留时空分异分析——以深圳市为例[J]. 地球信息科学学报, 2015, 17(2): 197-205. (CSCD)      [link]

[72] Shuqiang Wang, Jinxing Hu, Yanyan Shen, Ling Yin, Yanjie Wei, 2015. Modeling and analysis of gene regulatory networks with a Bayesian-driven approach. International Symposium on Communications and Information Technologies (pp.289-293). IEEE.      [link]

[73] Ling Yin, Jinxing Hu, Lian Huang, Fan Zhang, Peng Ren, 2014. Detecting illegal pickups of intercity buses from their GPS traces. In Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems. IEEE, Piscataway, NJ, USA, 2162-2167. (EI)      [link]

[74] Chao Li, Zhongying Zhao, Jun Luo, Ling Yin, Qiming Zhou, 2014. A spatial-temporal analysis of users’ geographical patterns in social media: A case study on microblogs. Database Systems for Advanced Applications. Springer Berlin Heidelberg, 296–307. (EI)      [link]

[75] Ning Xu, Ling Yin, Jinxing Hu, 2014. Identifying home-work locations from short-term, large-scale, and regularly sampled mobile phone tracking data. Geomatics and Information Science of Wuhan University, 39(6),750-756.
许宁, 尹凌, 胡金星. 从大规模短期规则采样的手机定位数据中识别居民职住地[J]. 武汉大学学报(信息科学版), 2014, 39(6): 750-756. (CSCD)      [link]

[76] Ling Yin, Shih-Lung Shaw, Dali Wang, Eric A. Carr, Michael W. Berry, Louis J. Gross, and E. Jane Comiskey, 2012. A framework of integrating GIS and parallel computing for spatial control problems - A case study of wildfire control. International Journal of Geographical Information Science, 26(4), 621–641. (SCI)      [link]

[77] Chao Li, Zhongying Zhao, Shuguang Liu, Ling Yin, Jun Luo, 2012. Relationships between geographical cluster and cyberspace community: A case study on microblog. In Proceedings of the 2012 Geoinformatics. IEEE, GRSS, Piscataway, NJ, USA, 1–5. (EI)      [link]

[78] Ling Yin, Shih-Lung Shaw, and Hongbo Yu, 2011. Potential effects of ICT on face-to-face meeting opportunities: A GIS-based time-geographic approach. Journal of Transport Geography, 19(3), 422–433. (SSCI)      [link]

[79] Ling Yin, Shih-Lung Shaw, 2011. A space-time GIS for dynamics in potential face-to-face meeting opportunities. In Proceedings of the 2011 international workshop on Trajectory data mining and analysis (TDMA '11). ACM, New York, NY, USA, 15–22. (EI)     [link]

[80] Ling Yin, Manchun Li, Ye Tao, 2006. Impact assessment of overall land use planning at town level on rural people accessibility. Geography and Geo-Information Science, 22(1), 62-66.
尹凌, 李满春, 陶冶. 乡镇土地利用总体规划对农村居民出行可达性的影响研究[J]. 地理与地理信息科学, 2006, 22(1), 62–66. (CSCD)      [link]

[81] Ling Yin, Manchun Li, Ye Tao, 2006. Computer Assessment of Point Factors on Land Parcel Price Based on Shortest Path. Application Research of Computers, 23(9), 143-145.
尹凌, 李满春, 陶冶. 基于最短路径距离的宗地地价点状因素自动化评价[J]. 计算机应用研究, 2006, 23(9), 143–145. (CSCD)      [link]