Author: Yingjie Hu

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.

Abstract: Geographical random forest (GRF) is a recently developed and spatially explicit machine learning model. With the ability to provide more accurate predictions and local interpretations, GRF has already been used in many studies. The current GRF model, however, has limitations in its determination of the local model weight and bandwidth hyperparameters, potentially insufficient numbers of local training samples, and sometimes high local prediction errors. Also, implemented as an R package, GRF currently does not have a Python version which limits its adoption among machine learning practitioners who prefer Python. This work addresses these limitations by introducing theory-informed hyperparameter determination, local training sample expansion, and spatially weighted local prediction. We also develop a Python-based GRF model and package, PyGRF, to facilitate the use of the model. We evaluate the performance of PyGRF on an example dataset and further demonstrate its use in two case studies in public health and natural disasters.

Full paper link: https://doi.org/10.1111/tgis.13248
Preprint PDF: https://www.acsu.buffalo.edu/~yhu42/papers/2024_TGIS_PyGRF.pdf

Our lab receives a new NSF grant to study disrupted human mobility, help requests, and voluntary support during natural disasters. Focusing on a type of understudied disasters, i.e., winter storms, this project examines the impacts of natural disasters on community resilience from a geographical perspective. It examines three aspects of the human responses to natural disasters. First, anonymized mobile phone location data and GeoAI methods will be used to investigate spatial variation in the disrupted patterns of mobility. Second, we investigate spatial disparities in the extent to which residents request assistance from municipal authorities. Third, we study the geography of help-seeking behavior among social media users and the roles of digital platforms in facilitating voluntary support. We will also examine the socioeconomic and demographic factors associated with the variation of human behaviors and community resilience. This project is empirically grounded on a complement of datasets, engages theories in disaster resilience and disparities, and leverages a combination of methods in geographical and statistical analysis, GeoAI, and network modeling. Insights from this project and the developed methodological frameworks have the potential to inform future disaster studies on disaster-caused human mobility disruptions, community-initiated responses, and the roles that social media play in shaping these responses and spatial disparities.

NSF Award Link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2416886

At the 2024 CaGIS+UCGIS Symposium, lab member Kai Sun received the ICA Scholarship from the International Cartographic Association (ICA). The ICA Scholarship is awarded to “support early career scholars and professionals in advancing their career in cartography and GIScience.” Kai also made a poster presentation, “GALLOC: A GeoAnnotator for Labeling LOCation descriptions from disaster-related text messages”, at UCGIS.

Lab member Jiyeon Kim made an oral presentation, “Assessing the ability of deep learning models integrated with environmental and weather variables for predicting fire spread: A case study of the 2023 Maui wildfires.” Jiyeon’s presentation received the 3rd Place Award in the Student Presentation Competition.

Dr. Yingjie Hu was selected as the recipient of the 2024 Early/Mid-Career Research Award from the University Consortium for Geographic Information Science (UCGIS). This award is “given to an individual who has made a particularly outstanding research contribution to geographic information science.” The contribution can be “a single contribution or a series of research works that are seminal and have significant impacts on Geographic Information Science community.” This is an international award that is given to “researchers in the Early/Mid-Career level that is 15 years or less after the terminal degree or within 15 years of accumulated professional service, worldwide are eligible for the award.”

Yingjie is extremely honored to receive this award. He really appreciates the great encouragement from the UCGIS community and is grateful for the support of his mentors, colleagues, and students. He looks forward to continuing contributing to GIScience research, education, and community service.

Lab member Ryan Zhenqi Zhou receives both the 2024 Hugh Calkins Applied GIS Award and the 2024 Michael Trapasso Climate Impacts Award!

The Hugh Calkins Applied GIS Award is given annually to a graduate student from UB Geography, who best applies the principles that Dr. Calkins developed and taught for many years.

The Dr. L. Michael Trapasso award is given annually to a current graduate student from UB Geography in order to advance his/her research involving the use of meteorological or climatological data in the following year. For Ryan, he uses meteorological or climatological data to advance his research on winter storms and blizzards, and to help communities become more resilient during these disasters.

Congratulations, Ryan!!

Yingjie is extremely honored to receive the 2024 Emerging Scholar Award from the American Association of Geographer (AAG) Spatial Analysis and Modeling (SAM) Specialty Group. This award “honors early- to mid-career scholars who have made significant contributions to education and research initiatives.”

Due to family care responsibilities (his second child was born a few weeks ago), Yingjie couldn’t travel to Hawaii to receive this award. Many thanks to UB PhD student Qingqing Chen who brought back the award plaque for him. Yingjie is extremely grateful for the great support from his mentors, colleagues, and students, and he looks forward to continuing contributing to GIScience, SAM, and beyond!

In a new research led by PhD student Ryan Zhou, we examined the impacts of the 2021 Texas Winter storm on local communities in three stages and impact disparities. Winter Storm Uri slammed Texas between February 13–17, 2021 and caused widespread power outages. Understanding the impacts of this catastrophic event on local communities has important meaning. In this study, we examine the impacts of this winter storm and its impact disparities on different population groups over three stages of this disaster: the initial-hit stage, power-outage stage, and recovery stage. The study focuses on Harris County, Texas which was severely affected by the winter storm. We leverage home-dwelling time information from anonymized mobile phone location data to study the constrained mobility of people due to the winter storm as a way to quantify its impacts on local communities. Considering that mobile phone location data may be affected by the power outages, we further integrate nighttime light (NTL) images into our analyses to assess disaster impacts during the power-outage stage, and use home-dwelling time to assess the impacts during the other two stages (i.e., the initial-hit stage and recovery stage). The results reveal disparate impacts of this winter storm on local communities in the three stages of this disaster. We also find impact disparities on population groups with different socioeconomic and demographic backgrounds, especially during the initial-hit stage. These results help us better understand the impacts of this catastrophic event, and could inform future response and mitigation efforts in identifying vulnerable communities, allocating resources, and curtailing negative impacts of similar disasters.

More information about this article is at: Ryan Zhenqi Zhou, Yingjie Hu, Lei Zou, Heng Cai, and Bing Zhou. (2024): Understanding the disparate impacts of the 2021 Texas winter storm and power outages through mobile phone location data and nighttime light images. International Journal of Disaster Risk Reduction, 104339. [PDF]

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panelists to discuss the advances and limitations in GeoAI research. The panelists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.

Full paper is available at: Hu, Y., Goodchild, M., Zhu, A.X., Yuan, M., Aydin, O., Bhaduri, B., Gao, S., Li, W., Lunga, D., & Newsam, S. (2024): A five-year milestone: reflections on advances and limitations in GeoAI research. Annals of GIS, in press.

Figure 1. The GeoAI panel at AAG 2023