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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!

Our MS in GIS student Tev’ye Davis is graduating! Over the past years, Tev’ye has developed strong technical skills in GIS and machine learning, and he will become a GIS specialist in the Osmose Utilities company. Congratulations! Proud of you!

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]

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.

Yingjie has been promoted to Associate Professor with tenure. He is very grateful to his mentors, colleagues, students, friends, and family members for all your support and guidance. He is looking forward to continuing contributing to GIScience research and education in the coming years!

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.

We are excited to have Dr. Kai Sun come back and join our GeoAI Lab again! Previously, he was a visiting PhD student in our GeoAI Lab from 2019 to 2021. He obtained his B.S. from Wuhan University and his M.S. and Ph.D. from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. His current research focuses on using GeoAI approaches for advancing disaster management.

Welcome back, Kai!

Abstract:

Background: Obesity is a serious public health problem. Existing research has shown a strong association between obesity and an individual’s diet and physical activity. If we extend such an association to the neighborhood level, information about the diet and physical activity of the residents of a neighborhood may improve the estimate of neighborhood-level obesity prevalence and help identify the neighborhoods that are more likely to suffer from obesity. However, it is challenging to measure neighborhood-level diet and physical activity through surveys and interviews, especially for a large geographic area.

Methods: We propose a method for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data, and examine the extent to which the derived measurements can enhance obesity estimation, in addition to the socioeconomic and demographic variables typically used in the literature. We conduct case studies in three different U.S. cities, which are New York City, Los Angeles, and Buffalo, using anonymized mobile phone location data from the company SafeGraph. We employ five different statistical and machine learning models to test the potential enhancement brought by the derived measurements for obesity estimation.

Results: We find that it is feasible to derive neighborhood-level diet and physical activity measurements from anonymized mobile phone location data. The derived measurements provide only a small enhancement for obesity estimation, compared with using a comprehensive set of socioeconomic and demographic variables. However, using these derived measurements alone can achieve a moderate accuracy for obesity estimation, and they may provide a stronger enhancement when comprehensive socioeconomic and demographic data are not available (e.g., in some developing countries). From a methodological perspective, spatially explicit models overall perform better than non-spatial models for neighborhood-level obesity estimation.

Conclusions: Our proposed method can be used for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone data. The derived measurements can enhance obesity estimation, and can be especially useful when comprehensive socioeconomic and demographic data are not available. In addition, these derived measurements can be used to study obesity-related health behaviors, such as visit frequency of neighborhood residents to fast-food restaurants, and to identify primary places contributing to obesity-related issues.

Keywords: Obesity, diet, physical activity, anonymized mobile phone location data, GeoAI.

More details are available at: Ryan Zhenqi Zhou, Yingjie Hu, Jill Tirabassi, Yue Ma, and Zhen Xu (2022): Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation. International Journal of Health Geographics, 21(1), 1-18. [PDF]

A new paper led by graduate student Yue Ma on “Forecasting vegetation dynamics in an open ecosystem by integrating deep learning and environmental variables” is published in the International Journal of Applied Earth Observation and Geoinformation.

Abstract: Open (i.e., non-forest) ecosystems, such as savannas, shrublands, and grasslands, contain over 40 % of the global total ecosystem organic carbon and harbor a substantial portion of the world’s biodiversity. Accurately forecasting vegetation dynamics is critical for managing biodiversity, fire, water, and carbon in these open ecosystems. Unlike forests or other relatively stable ecosystems, open ecosystems can have dramatically changing vegetation states since they are prone to natural disturbances, long-term trends, and short-term events. Consequently, it is challenging to accurately predict vegetation state in this type of ecosystems. This paper investigates the use of deep learning based approaches for forecasting vegetation dynamics in an open ecosystem, the fynbos shrublands of the Cape Floristic Region of South Africa, a global biodiversity hotspot. We experiment with different deep learning models and examine the ability of thirteen environmental variables, such as precipitation, fire history, and temperature, to enhance the forecasting. We find that the ConvLSTM model can forecast vegetation state more accurately than four other compared baseline approaches. The environmental variable mean precipitation in July (winter) provides the most prominent enhancement for forecasting among the tested variables. Finally, we discuss the pros and cons of using a deep learning based approach for vegetation forecasting in open ecosystems from a conservation management perspective.

More details are available in: Yue Ma, Yingjie Hu, Glenn R Moncrieff, Jasper A Slingsby, Adam M Wilson, Brian Maitner, and Ryan Zhenqi Zhou (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. [PDF]

We are currently organizing the 2023 AAG GeoAI Symposium. If you are interested, please see the call for sessions below:

Call for Sessions: 2023 AAG Symposium on GeoAI and Deep Learning for Geospatial Research

AAG Annual Meeting, Denver, March 23-27, 2023

Lead Organizers:
Yingjie Hu, University at Buffalo (yhu42@buffalo.edu)
Song Gao, University of Wisconsin, Madison (song.gao@wisc.edu)
Wenwen Li, Arizona State University (wenwen@asu.edu)
Dalton Lunga, Oak Ridge National Laboratory (lungadd@ornl.gov)
Orhun Aydin, Saint Louis University (orhun.aydin@slu.edu)
Shawn Newsam, University of California, Merced (snewsam@ucmerced.edu)

The past few years have witnessed significant advances in the interdisciplinary field of GeoAI. With the increasing availability of geospatial big data, advances in hardware computing, and novel AI models, researchers have integrated these three to address some of the most challenging problems facing our society and deliver positive social impacts. Examples include improving individual and population health, enhancing community resilience in natural disasters, predicting spatiotemporal traffic flows, forecasting the impacts of climate change on ecosystems, building smart and connected communities and cities, and supporting humanitarian mapping and policymaking. From a perspective of method development, researchers have infused spatial principles into AI models, and developed spatially explicit AI models that can better address geospatial problems, such as enhancing geoprivacy protection and better representing geographic features in embedding space. Great research efforts have also been made to increase the explainability of GeoAI models for supporting decision making, and identify and reduce potential biases in training data and the trained models.

Building on the success of previous AAG GeoAI symposiums, the 2023 Symposium aims to bring together geographers, GI scientists, remote sensing scientists, computer scientists, health researchers, urban planners, transportation professionals, disaster response experts, ecologists, earth system scientists, stakeholders, and many others to share recent research outcomes and discuss challenges for GeoAI research in the following years. We are calling for sessions on all topics related to GeoAI, including but not limited to:

• Advances in GeoAI methods and spatially explicit models
• GeoAI for deriving novel measurements and statistics
• GeoAI for enhancing community resilience in the face of disasters
• Ethics, geoprivacy issues, and social implications in GeoAI research
• GeoAI for improving individual and population health
• GeoAI for urban analytics, social sensing, and smart communities
• GeoAI for traffic flow predictions and transportation management
• GeoAI for supporting precision agriculture
• GeoAI for predicting the impact of climate change on communities
• GeoAI for ecosystem conservation and biodiversity
• GeoAI for earth system modeling and forecasting
• GeoAI for crime analysis and public safety
• Cyberinfrastructures and knowledge graphs for advancing GeoAI
• Data resources, tools, and benchmarking platforms for GeoAI
• …

Different types of sessions, such as Paper Session, Panel Session, and Lightning Talk Session, are all welcome. If you are interested in organizing a session in this symposium, please send an email to Yingjie Hu at yhu42@buffalo.edu before October 24, 2022. In your email, please indicate a working title of your session (which can be revised later), the session type (e.g., Paper Session or Panel Session), and session modality (whether it will be in-person or virtual). For any questions, please feel free to contact one of our organizers. Thank you and we look forward to seeing you in AAG 2023!

Best wishes,
Yingjie Hu, University at Buffalo (yhu42@buffalo.edu)
Song Gao, University of Wisconsin, Madison (song.gao@wisc.edu)
Wenwen Li, Arizona State University (wenwen@asu.edu)
Dalton Lunga, Oak Ridge National Laboratory (lungadd@ornl.gov)
Orhun Aydin, Saint Louis University (orhun.aydin@slu.edu)
Shawn Newsam, University of California, Merced (snewsam@ucmerced.edu)