The PhD program of UB Geography has become a STEM program

The PhD program of UB Geography has officially been classified as a STEM program under the category “Geography and Environmental Studies” (30.4401). According to the definition from the U.S. government, this category refers to a program “that focuses on interactions between people and the natural and built environments. Includes instruction in climate science, sustainability, environmental science and policy, research methods, geographic information systems (GIS), human geography, physical geography, remote sensing, and public policy.

Being classified as STEM allows our program to encourage more students to participate in STEM-related research. International students graduating from our PhD program will also have 36 months OPT which allows them to further hone their research and technical skills in the U.S. after graduation. Our Master’s in GIS program is in STEM too. If you are interested in applying for the graduate programs of UB Geography, please apply at:

GeoAI lab receives a new NSF award to study anomalous human mobility patterns under natural disasters

In collaboration with Dr. Dane Taylor (Mathematics; PI), GeoAI lab receives a new NSF award that integrates big geospatial data, GeoAI methods, and network modeling to study anomalies in spatio-temporal multilayer networks encoding human mobility:

Human mobility data from anonymized mobile phone devices are becoming increasingly available, enabling the detection of anomalies and potential threats from human movements. Due to the massive scale of data, it is difficult to detect anomalies that occur at different spatial and temporal resolutions. In addition, human movements are often associated with different categories of places (e.g., grocery stores and schools), and anomalies that are evident in one category may be obscure in another. This project will furnish new mathematical models and theories for encoding different categories of human movements as spatial-temporal multilayer networks. It will develop new algorithms to detect movement-pattern anomalies, which can help better forewarn anomalous events concerning national security. It will also advance our understanding of the impacts of anomalous and disastrous events on different categories of human movements. This project will contribute toward education by supporting graduate students and will facilitate interdisciplinary research between mathematics and geography. Open-source software tools will be implemented and publicly shared to help researchers and decision makers better predict, detect, and plan responses to future anomalous events.

To represent human movements in different categories and effectively detect the associated anomalies, the investigators will pursue three targets in this project. Target 1 will develop spatial-temporal multilayer network models encoding anonymized mobile phone location data of the United States. The network-structural properties associated with both normal and expected anomalous situations (e.g., holidays) will be extensively examined to develop a family of realistic generative models. Target 2 will build algorithms to detect and characterize movement-pattern anomalies based on the multilayer network models using unsupervised spectral algorithms. Random matrix theory will be employed to obtain theoretic guidelines for how to optimally preprocess data to maximize the “detectability” of anomalies at different spatial and temporal resolutions. Target 3 will apply the developed multilayer network models and anomaly detection algorithms to two case studies, to further refine our models and algorithms and to gain new insights into the evolutions and impacts of the two important events.

Dr. Hu receives CPGIS Young Scholar Award

Dr. Hu receives the 2023 CPGIS Young Scholar Award from the International Association of Chinese Professionals in Geographic Information Sciences (CPGIS). He is very grateful for the support from his mentors, colleagues, and students. He looks forward to continuing contributing to GIScience in the coming years!

Lab member Yue Ma completed her MS in GIS study

Lab member Yue Ma completed her master’s study in GeoAI Lab and UB Geography. Her master’s research focuses on forecasting vegetation dynamics in an open ecosystem in the Cape Floristic Region in South Africa, which is a global biodiversity hotspot. Yue also received the 3rd place Award in the 2023 AAG Student Poster Competition. After receiving PhD offers from multiple top universities, Yue eventually decided to continue her study at the University of Maryland College Park. We wish Yue all the best to her bright career ahead!

New paper led by Dr. Kai Sun on conflating points of interest (POI) data published in Computers, Environment and Urban Systems

Abstract: Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research.

More details are available in the full paper: Kai Sun, Yingjie Hu, Yue Ma, Ryan Zhenqi Zhou, and Yunqiang Zhu (2023): Conflating point of interest (POI) data: A systematic review of matching methods. Computers, Environment and Urban Systems, 103, 101977. [PDF]

Lab member Ryan Zhenqi Zhou received Hugh W. Calkins Applied GIS Award

Ryan Zhenqi Zhou received Hugh W. Calkins Applied GIS Award. This award is given annually to a graduate student from the Department of Geography at the University at Buffalo who demonstrates the principles of Dr. Calkins in using GIS to address real-world challenges. Congratulations, Ryan!

About Dr. Hugh W. Calkins (excerpted from the website of UB Geography):

Hugh West Calkins was a pioneer in the development of geographic information systems. He was a faculty member in the Department of Geography in UB since 1975, and served as department chair from 1999 to 2002. He advised dozens of students at the doctoral, graduate and undergraduate levels. He received his bachelor’s degree from the University of California at Berkeley, and his master’s and doctoral degrees in urban planning from the University of Washington.

At a session in Hugh’s honor at the annual meeting of the American Association of Geographers in Denver in April 2005, ESRI President Jack Dangermond stated that “Hugh Calkins defined what it meant to be a GIS professional”. Hugh served as a member of the National Committee on Digital Cartographic Data Standards of the American Congress on Surveying and Mapping. He also served as co-leader on two research initiatives of the National Center for Geographic Information and Analysis at UB, one of the center’s three sites nationwide, which focused on the use of geographic information in decision making and institutional data sharing. Through his service on numerous national, state and local advisory committees and boards, he was a leader in the establishment of information exchange standards for GIS.

Congratulations to lab member Yue Ma on winning the 3rd place in the 2023 AAG Student Poster Competition

Lab member Yue Ma’s poster presentation “Forecasting vegetation dynamics in an open ecosystem by integrating deep learning and environmental variables” won the 3rd place in the 2023 AAG RSSG Student Illustrated Paper Competition. Congratulations, Yue!!!

Link to the full paper is here: Ma, Y., Hu, Y., Moncrieff, G.R., Slingsby, J.A., Wilson, A.M., Maitner, B., & Zhou, R.Z. (2022): Forecasting vegetation dynamics in an open ecosystem by integrating deep learning and environmental variables. International Journal of Applied Earth Observation and Geoinformation, 114, 103060.