师资队伍

2010.9-2014.7 北京师范大学数学科学学院数学与应用数学专业 学士学位

2014.9-2019.7 北京大学政府管理学院区域经济学专业 博士学位

2019.9-2021.6美国亚利桑那州立大学地理科学与城市规划学院 博士后（导师：Stewart Fotheringham院士）

2021.7-2022.6美国哈佛大学地理分析中心 博士后

2022.7-2022.9 美国哈佛大学 地理分析中心 副研究员

2022.9-2024.7 香港科技大学（广州）城市治理与设计学域 访问助理教授

2024.7-至今 重庆大学管理科学与房地产学院 副教授

中国地理学会空间综合人文社会科学工作委员会委员

中国遥感应用协会社会遥感地理计算专业委员会委员

中国人工智能学会社会计算与社会智能专业委员会青年委员

美国哈佛大学空间数据实验室区域政策和未来工作实验室联合主任

《Annals of the American Association of Geographers》、《Geographical Analysis》、

《Spatial Statistics》等20本SCI/SSCI期刊匿名审稿人

空间/时空数据分析与建模、空间计量经济学、空间统计学、地理人工智能（GeoAI）、地理信息科学

1. Yu, H. (2024). Exploring Multiscale Spatial Interactions: Multiscale Geographically Weighted Negative Binomial Regression. Annals of the American Association of Geographers, 114(3): 574-590.

2. Sachdeva, M., Fotheringham, A.S., Li, Z. & Yu, H. (2023). On the Local Modeling of Count Data: Multiscale Geographically Weighted Poisson Regression. International Journal of Geographical Information Science, 37(10): 2238-2261.

3. Yu, H., Lao, X., Gu, H., Zhao, Z., & He, H. (2022). Understanding the Geography of COVID-19 Case Fatality Rates in China: New Evidence from a Spatial Autoregressive Probit-Log Linear Hurdle Analysis. Frontiers in Public Health, 10: 751768.

4. Fotheringham, A.S., Yu, H., Oshan, T., Wolf, L.J. & Li, Z. (2022). On the Notion of ‘Bandwidth’ in Geographically Weighted Regression Models of Spatially Varying Processes. International Journal of Geographical Information Science, 36(8): 1485-1502.

5. Pramanik, S., Punia, M., Yu, H., Chakraborty S. (2022). Is Dense or Sprawl Growth More Prone to Heat-related Health Risks? Spatial Regression-based Study in Delhi, India. Sustainable Cities and Society, 81: 103808.

6. Liu, L., Yu, H., Zhao, J., Wu, H., Peng, Z., & Wang, R. (2022). Multiscale Effects of Multimodal Public Facilities Accessibility on Housing Prices based on MGWR. ISPRS International Journal of Geo-Information, 11(1): 57.

7. Fischer, J., Sersli, S., Nelson, T., Yu, H., Laberee, K., Winters, M., & Zanotto, M. (2022). Spatial Variation in Bicycling Risk Based on Crowdsourced Safety Data. The Canadian Geographer, 66(3): 556-568.

8. Lao, X., Gu, H., Deng X., Yang J., Gao Q., & Yu, H. (2022). Comparing Intercity Mobility Patterns among Different Holidays in China based on Tencent migration data. Applied Spatial Analysis and Policy, 15(4): 993-1020.

9. Liu, L., Wang, R., Guan, W., Bao, S., Yu, H., Fu, X., & Liu, H. (2022). Assessing Reliability of Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility: A Comparison between Sina Weibo and Baidu Qianxi. ISPRS International Journal of Geo-Information, 11(2): 145.

10. Yu, H., & Fotheringham, A.S. (2021). A Multiscale Measure of Spatial Dependence Based on a Discrete Fourier Transform. International Journal of Geographical Information Science, 36(5): 849-872.

11. Yu, H., Li, J., Bardin, S., Gu, H. & Fan, C. (2021). Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis. ISPRS International Journal of Geo-Information, 10(8): 510.

12. Song, J., Yu, H., Lu, Y. (2021). Spatial-scale Dependent Risk Factors Associated with Heat-related Mortality: A Multiscale Geographically Weighted Regression Analysis. Sustainable Cities and Society, 74: 103159.

13. Sachdeva M., Fotheringham, A.S., Li, Z., & Yu, H. (2021). Are We Modelling Spatially Varying Processes or Non-linear Relationships?. Geographical Analysis, 54(4): 715-738.

14. Lao, X., Gu, H., Yu, H., & Xiao, F. (2021). Exploring the Spatially-varying Effects of Human Capital on Urban Innovation in China. Applied Spatial Analysis and Policy, 14(4): 827-848.

15. Zhao, Z., Lao, X., Gu, H., Yu, H., & Lei, P. (2021). How does Air Pollution Affect Urban Settlement of the Floating Population in China? New evidence from a push-pull migration analysis. BMC Public Health, 21: 1-15.

16. Yu, H., Fotheringham, A.S., Li, Z., Oshan, T., Kang, W. & Wolf, L.J. (2020). Inference in Multiscale Geographically Weighted Regression. Geographical Analysis, 52: 87-106. (ESI Highly Cited Paper, Wiley Top Downloaded Paper 2018-2019)

17. Yu, H., Fotheringham, A.S., Li, Z., Oshan, T. & Wolf, L.J. (2020). On the Measurement of Bias in Geographically Weighted Regression Models. Spatial Statistics, 38: 100453.

18. Gu, H., Yu, H., Mehak, S., & Liu, Y. (2020). Analyzing Distribution of Scientific Researchers in China: An Approach using Multiscale Geographically Weighted Regression (MGWR). Growth and Change, 52(1): 443-459.

19. Nelson, T., Roy, A., Ferster, C., Fischer, J., Brum-Bastos, V., Laberee, K., Winters, M., & Yu, H. (2020). Generalized Model for Mapping Bicycle Ridership with Crowdsourced Data. Transportation Research Part C, 125: 102981.

20. Geng, S., Ma, M., Hu, X., & Yu, H. (2020). Multi-scale Geographically Weighted Regression Modeling of Urban and Rural Construction Land Fragmentation—A Case Study of the Yangtze River Delta region. IEEE Access, 10: 7639-7652.

21. Oshan, T., Wolf, L. J., Fotheringham, A. S., Kang, W., Li, Z. & Yu, H. (2019). A Comment on Geographically Weighted Regression with Parameter-Specific Distance Metrics. International Journal of Geographical Information Science. 33(7): 1289-1299.

22. 王春超, 肖艾平, 于瀚辰. 近朱者赤，近墨者黑——教育中的非对称同伴效应研究[J]. 教育学报, 2022, 18(5): 156-171.

23. 沈体雁, 于瀚辰, 周麟, 古恒宇, 何泓浩. 北京市二手住宅价格影响机制——基于多尺度地理加权回归模型（MGWR）[J]. 经济地理, 2020, 40(3): 75-83.

24. 于瀚辰, 周麟, 沈体雁. 制造业企业区位选择集聚经济指向的空间效应[J]. 地理研究, 2019, 38(2): 273-284.

25. 于瀚辰, 沈体雁, 孙童. 中国ICT设备制造业的动态空间分异[J]. 地域研究与开发, 2019, 38(01): 1-5.

26. 王彦博, 于瀚辰, 沈体雁. 可调整个体优先级的双边匹配算法[J]. 计算机工程与应用, 2018, 54(11): 198-203+235.

27. 周麟, 沈体雁, 于瀚辰, 曹巍韡, 槐玲. 城市内部知识密集型服务业的时空格局研究——以保定市为例[J]. 城市发展研究, 2016, 23(11): 1-6.

28. 齐子翔, 于瀚辰. 区位选择、双边匹配与化解产能过剩的机制设计[J]. 改革, 2015(09): 101-111.

1. 京津冀产业跨区域转移与非首都功能疏解路径研究（编号：15CJL061），国家哲学社会规划办公室资助。参与者

2. 中国经济密度空间格局与演化机制研究（编号：41071076），国家自然科学基金资助。参与者

3. 中国空间经济学理论与实践研究（No. 13&ZD166），国家哲学社会规划办公室资助。参与者

4. 基于双边匹配理论的企业区位配置模型和区位市场设计（编号：71473008），国家自然科学基金资助。参与者

5. 中国产业集群地图系统（CCM）建设与应用研究（编号：17ZDA055），国家哲学社会规划办公室资助。参与者

6. 地方政府土地流转策略交互行为分析（No. 2018M631224），中国博士后科学基金资助。参与者

7. NSF Project: The Measurement of Scale and Process Heterogeneity Through Local Multivariate Models

8. Spatial Data Lab Project: Workflows for COVID-19 Data Collecting, Processing, and Analysis Sponsored by NSF RAPID Project and NSF/IAB Membership

9. NSF RAPID Project: Building a Spatiotemporal Platform for a Rapid Response to COVID-19

10. The Dataverse Project: An Open-source Web Application to Share, Preserve, Cite, Explore, and Analyze Research Data

中国人工智能学会-大数据与社会计算2024社会计算青年学者新星

Wiley Top Downloaded Paper 2018-2019论文奖

2015年河北省第三次全国经济普查研究课题二等奖