Author: Yingjie Hu

Our new paper on understanding and modeling cities through texts in the age of AI is published in Nature Cities. It discusses how texts can be leveraged to understand cities and people for applications from urban planning to disaster management.

Urban researchers now have access to vast amounts of textual data—from social media and news to planning documents and property listings. These textual data provide important information about the activities of people and organizations in urban environments. Meanwhile, recent advancements in computational tools, including large language models, have expanded our ability to analyze textual data. This article explores how these tools are reshaping the ways we analyze, understand, and theorize the city through text. By outlining key developments, applications, and challenges, it argues that text is no longer a ‘fringe resource’ but a central component in urban analytics with the potential to connect quantitative and qualitative researchers.

Full paper: https://www.nature.com/articles/s44284-025-00314-x [pdf]

PhD student Ryan Zhenqi Zhou won this year’s PhD Scholarship from the Cartography and Geographic Information Society (CaGIS): https://cartogis.org/awards/students/

This is a fairly prestigious student award from a national-level society. Congratulations, Ryan!

Two new members joined our lab this Fall 2025. They are:

• Liyue Zhang. Liyue obtained her bachelor degree in Urban and Rural Planning from South China University of Technology. She joined the lab as a new PhD student. Her research interests include GeoAI, urban resilience, and urban computing.

• Mengqi Li. Mengqi received her M.S. in Geographic Information Science from the University of Zurich (UZH) and B.S. from Nanjing University of Information Science and Technology (NUIST). Mengqi joined the lab as a PhD student. Her research interests include GeoAI, urban informatics, disaster resilience, and human mobility.

Welcome Mengqi and Liyue! We hope you enjoy your time here!

Abstract: The 2022 Buffalo blizzard was a catastrophic winter storm that struck Buffalo, New York in the week of Christmas in 2022. It claimed 47 lives and left much of the region stranded for the holiday week. In this disaster, the 311 call service was used by many residents to request help for issues due to the blizzard. This study examines these 311 help requests and their potential disparities across communities. Specifically, we aim to: (1) understand the spatial and temporal distributions of different types of 311 help requests; (2) identify the physical and social vulnerability factors, as well as human behavior factors, that are associated with the use of 311 calls. Methodologically, we leverage both explainable geospatial artificial intelligence (GeoAI) methods and statistical analysis to analyze 311 help requests and their associated factors. Our analysis shows significant spatial disparities in 311 help requests across communities. Results from explainable GeoAI and statistical analysis also reveal complementary insights on key factors associated with 311 help requests, such as historical 311 request behavior and percentage of minority population. These results could inform future disaster management decisions and help mitigate the negative impacts of winter storm disasters.

Full paper is available at: Zhou, R.Z., Hu, Y., Sun, K., Muldoon, R., Clark, S., & Joseph, K., (2025): Explainable GeoAI and statistical analysis reveal complementary insights about disparities of 311 help requests during the 2022 Buffalo blizzard. International Journal of Disaster Risk Reduction, 105635. [PDF]

Dr. Hu introduced geospatial technologies and AI, and the use of these technologies for disaster response in Encyclopedia Britannica.

He did an interview with Encyclopaedia Britannica previously:

In the 2024-2025 Department Award Ceremony, GeoAI Lab member Ryan Zhenqi Zhou received the 2025 Hugh Calkins GIS Award. This award is given annually to a graduate student from the Department of Geography at the University at Buffalo (UB) who demonstrates the principles of Dr. Calkins in using GIS to address real-world challenges. Congratulations, Ryan!

Lab member Jiyeon Kim receives the Michael Trapasso Climate Impact Award. This award recognizes and supports a graduate student from UB Geography, who uses meteorological or climatological data in their research to make a positive impact on the society. Congratulations, Jiyeon!

Three of our lab members, Ryan Zhou, Jiyeon Kim, and Yingjie Hu, attended and presented in this year’s AAG Annual Conference at Detroit.

Jiyeon presented her work on assessing the ability of deep learning models for wildfire spread prediction.

Ryan presented his work on the 2022 winter storm at Buffalo using explainable GeoAI

Yingjie presented the PyGRF work led by Kai Sun:

We had a great conference at AAG and Detroit! Looking forward to the conference next time!

Dr. Hu was recently interviewed by Channel 2 WGRZ for NSF-funded research on winter storm disasters focusing on the 2022 Buffalo blizzard.

– Link to the interview: https://www.wgrz.com/video/news/local/as-seen-on-tv/facebooks-impact-during-22-blizzard-studied-by-ub/71-b9a52ee6-b58d-44dc-ba70-20a324734215
– Link to the story: https://www.wgrz.com/article/news/local/buffalo/study-social-medias-role-connecting-community-2022-blizzard/71-19a08cec-9ebb-4ef7-a442-4d98d404f845
– Another story covered by Buffalo News: https://buffalonews.com/news/local/education/facebook-groups-saved-lives-during-22-blizzard-ub-studying-how-they-kept-communities-connected/article_7c13b1a0-be46-11ef-a327-778c60013a65.html

As the year 2024 comes to an end, our members at the GeoAI Lab wish you a Merry Christmas and a wonderful new year ahead in 2025!

With the support of the community, Dr. Hu has been elected as the Academic Director of the AAG Geographic Information Science and Systems (GISS) Specialty Group. He looks forward to continuing contributing to the research and education of GIScience.