Dr. Di Zhu, University of Minnesota, Twin Cities, United States
🌐 Dr. Di Zhu is an Assistant Professor of Geographic Information Science at the University of Minnesota, Twin Cities. As the director of the Geospatial Data Intelligence (GeoDI) Lab (geodi.umn.edu), 🛰️ Prof. Zhu is dedicated to unlocking theoretical and actionable insights from big geo-data. His work spans Spatial Analytics, Geospatial Artificial Intelligence, and Social Sensing, pushing the boundaries of knowledge in these fields.
With a focus on Intelligent Spatial Analytics (ISA) and Geographic Knowledge Discovery, 🗺️ Prof. Zhu employs integrated spatial thinking and cross-disciplinary methods. His research delves into the intricacies of human-environment dynamics, including population studies, urban dynamics, public health, human mobility, spatial networks, socioeconomic sustainability, crime, and business optimization. 🌍💡
🎓 Professional Profiles:
. Scopus
. Google Scholar
. ORCID
🏆 Awards and Honors :
🌐 As a passionate researcher in GeoAI, He have been fortunate to secure various grants, awards, and scholarships that reflect her dedication to advancing knowledge in the field of human mobility and spatial interactions. From internal sources, He received a Seed Grant for Social Sciences Research from the Office of the Vice President for Research and OFAA, College of Liberal Arts, totaling $4,250. This grant supports her project on developing a GeoAI-based model for human mobility flow generation, scheduled to run from March 1, 2023, to June 1, 2024. Additionally, He was honored with a $43,757 grant as the Principal Investigator from the Faculty Interactive Research Program at the Center for Urban & Regional Affairs (CURA). This award, spanning from July 1, 2022, to July 1, 2023, focuses on exploring how human movements drive dynamic community structures within the Twin Cities Metro Area. 🌟
🎓 Externally, her collaborative role as a researcher in the National Spatiotemporal Population Research Infrastructure project, funded by the National Institutes of Health (NIH) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, has contributed significantly to advancing knowledge in the domain. This collaboration, which took place from September 9, 2020, to 2021, allowed me to delve into spatiotemporal aspects of population research. Furthermore, her involvement in a project funded by the China Scholarship Council (CSC) with a grant of £16,200 has enabled me to model spatial heterogeneity and spatial interactions from the big geo-data perspective, spanning from October 1, 2018, to October 1, 2019.🏛️
🌍 In recognition of her academic achievements, He received the Distinction of Doctoral Thesis award from Peking University in 2020, along with being recognized as an Excellent Graduate. He was also honored with the prestigious China National Scholarship from the Ministry of Education in 2019 and received the Early Career Scholarship from GIS Research UK in the same year. To support her academic pursuits, He was fortunate to receive a Travel Award from the Applied Geography Specialty Group of the American Association of Geographers (AAG) in 2019. These honors and scholarships motivate me to continue pushing the boundaries of knowledge in the dynamic and interconnected realm of GeoAI and spatial sciences. 🚀
🔬🌐 Research Interest :
🔍 GIScience
🌍 GeoAI
🔬 Spatial Statistics
🧪 Urban Science
🌐 Spatial Network
📚 Publication Impact and Citations :
Scopus Metrics:
- 📝 Publications: 34 documents indexed in Scopus.
- 📊 Citations: A total of 842 citations for his publications, reflecting the widespread impact and recognition of Dr. Di Zhu’s research within the academic community.
Google Scholar Metrics:
- All Time:
- Citations: 1045 📖
- h-index: 18 📊
- i10-index: 24 🔍
- Since 2018:
- Citations: 1045 📖
- h-index: 18 📊
- i10-index: 24 🔍
👨🏫 A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. 🌐🔬
Publications ( Top Note ) :
1. Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Published Year: 2019, Cited By: 136
2. Spatial interpolation using conditional generative adversarial neural networks
Journal: International Journal of Geographical Information Science
Published Year: 2020, Cited By: 112
3. Uncovering inconspicuous places using social media check-ins and street view images
Journal: Computers, Environment and Urban Systems
Published Year: 2020, Cited By: 97
4. Understanding place characteristics in geographic contexts through graph convolutional neural networks
Journal: Annals of the American Association of Geographers
Published Year: 2020, Cited By: 96
5. Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data
Journal: Applied Geography
Published Year: 2017, Cited By: 86
6. Inferring spatial interaction patterns from sequential snapshots of spatial distributions
Journal: International Journal of Geographical Information Science
Published Year: 2018, Cited By: 56
7. Spatial origin-destination flow imputation using graph convolutional networks
Journal: IEEE Transactions on Intelligent Transportation Systems
Published Year: 2020, Cited By: 55
8. A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data
Journal: International Journal of Digital Earth
Published Year: 2020, Cited By: 43
9. A stepwise spatio-temporal flow clustering method for discovering mobility trends
Journal: IEEE Access
Published Year: 2018, Cited By: 43
10. Spatial regression graph convolutional neural networks: A deep learning paradigm for spatial multivariate distributions
Journal: GeoInformatica
Published Year: 2021, Cited By: 29