News

The Thirteenth International Conference on Geographic Information Science (GIScience 2025) will be held in Christchurch, New Zealand, on 26-29 August 2025, hosted by the University of Canterbury in collaboration with the GIScience academic research community across New Zealand. GIScience 2025 is the flagship conference in geographic information science and continues the highly successful conference series which started in 2000.

The conference regularly attracts over 250 international participants from academia, industry, and government to discuss and advance the state-of-the-art in geographic information science. August 26 is dedicated to Workshops and Tutorials. The main conference runs from August 27 to 29 and includes a single refereed paper track and an abstract track for posters and demo submissions. GIScience research spans the gamut of interrelated discovery activities related to geographic information from the invention of new computational instruments, the gathering of data via observation or experimentation, and discovery of descriptive generalizations patterns in data through to the creation of explanatory theories and the testing of theories. The GIScience conference series seeks submissions that make fundamental advances to the field through this multifaceted process.

GIScience 2025 welcomes papers, posters and demos covering emerging topics and fundamental research findings across all sectors of geographic information science, including (but not limited to) the role of geographic information in geography, computer science, engineering, information science, linguistics, mathematics, cognitive science, philosophy, psychology, social science, and geostatistics. We welcome participation from community members sharing work at various stages of development, including position pieces, works in progress, as well as full papers for inclusion in the conference proceedings.

Proceedings papers Deadline: January 31, 2025

Abstracts and Demos Deadline: April 4, 2025

Conference Website: https://giscience2025.org

CFP: 2025 AAG Symposium on GeoAI and Deep Learning for Geospatial Research

2025 AAG Annual Meeting, Detroit, MI, March 24-28, 2025

https://bit.ly/aag2025geoai

Lead Organizers:

Song Gao, University of Wisconsin, Madison

Wenwen Li, Arizona State University

Yingjie Hu, University at Buffalo

Budhu Bhaduri, Oak Ridge National Laboratory

Orhun Aydin, Saint Louis University

Shawn Newsam, University of California, Merced

Samantha T. Arundel, United States Geological Survey

Gengchen Mai, University of Texas Austin

Krzysztof Janowicz, University of Vienna

The field of GeoAI is advancing at an astonishing speed. We are excited to witness the significant growth of GeoAI in terms of its methods, its diverse geospatial applications, and its increasing societal impacts. For example, GeoAI has been applied to advance our understanding of environmental and climate change on the earth, improve individual and population health, enhance community resilience in natural disasters, strengthen smart and connected communities, more accurately predict spatiotemporal traffic flows, support humanitarian mapping and policymaking, and more. From the perspective of methodological development, we have observed a paradigm shift from using task-specific models with supervised learning to leveraging the power of visual foundation models, large language models (LLMs), and multimodal foundation models to achieve zero-shot to few-shot geospatial learning. We have also seen an increasing body of pioneering research integrating spatial theories and principles into general AI model design to develop “spatialized” AI that best tackles spatial and spatiotemporal problems.

Building on the success of previous AAG GeoAI symposiums and continuing to push the cutting edge of GeoAI research and its societal impact, the 2025 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.

Sessions (all sessions will be accessed at: https://bit.ly/aag2025geoai):

• GeoAI and Deep Learning Symposium: Representing Geographic Locations in AI Models (Panel discussion session; In-person session; Contact: Yingjie Hu, yhu42@buffalo.edu, University at Buffalo; Panelists: Gengchen Mai, University of Texas at Austin; Morteza Karimzadeh, University of Colorado Boulder; Yiqun Xie, University of Maryland College Park, Ziqi Li, Florida State University)
• GeoAI and Deep Learning Symposium: Generative AI in GIScience: a research agenda towards autonomous GIS (Panel discussion session; In-person session; Contact: Zhenlong Li,zhenlong@psu.edu, Penn State University)
• GeoAI and Deep Learning Symposium: GeoAI for Sustainability & Resilience I: Research Advances (Panel discussion session; in-person session; The organizing team)
• GeoAI and Deep Learning Symposium: GeoAI for Sustainability & Resilience II:: From Research to Real-World Applications (Panel discussion session; in-person session; The organizing team)
• GeoAI and Deep Learning Symposium: Current Developments and Concerns in GeoAI and CartoAI (Panel discussion session; In-person session; Contact:  Aileen Buckley, Esri (abuckley@esri.com); Sam Arundel, sarundel@usgs.gov, U.S. Geological Survey)
• GeoAI and Deep Learning Symposium: GeoAI for Feature Detection and Recognition (Paper session; In-person session; Contact: Sam Arundel, sarundel@usgs.gov, U.S. Geological Survey; Co-organizers: Wenwen Li, Arizona State University; Kevin McKeehan, HNTB, and Ernie Liu, U.S. Geological Survey )
• GeoAI and Deep Learning Symposium: Spatially Explicit Machine Learning and Artificial Intelligence (Paper session; In-person session; Contact: Gengchen Mai, gengchen.mai@austin.utexas.edu, University of Texas at Austin; Co-organizers:Yao-Yi Chiang, University of Minnesota-Twin Cities; Zhangyu Wang, University of California Santa Barbara; Di Zhu, University of Minnesota-Twin Cities; Yiqun Xie, University of Maryland; Hao Yang, University of Georgia)
• GeoAI and Deep Learning Symposium: GeoAI for Spatial Analytics and Modeling (Paper session; In-person session; Contact: Di Zhu, dizhu@umn.edu, University of Minnesota; Co-organizers: Guofeng Cao, University of Colorado, Boulder; Song Gao, University of Wisconsin, Madison; Peng Luo, Massachusetts Institute of Technology)
• GeoAI and Deep Learning Symposium: UrbanAI for Sustainable, Climate-Resilient Environments (Paper session; In-person session; Contact: Steffen Knoblauch, steffen.knoblauch@uni-heidelberg.de, Heidelberg University; Co-organizers: Hao Li, Technical University Munich; Peng Luo, Massachusetts Institute of Technology; Filip Biljecki, National University of Singapore; Alexander Zipf, Heidelberg University)
• GeoAI and Deep Learning Symposium: GeoAI and Urban Mobility Analytics (Paper session; In-person session; Contact: Dan Qiang, dan.qiang@mail.mcgill.ca, McGill University; Co-organizers: Grant McKenzie, McGill University, Xiao Huang, Emory University; Yihong Yuan, Texas State University)
• GeoAI and Deep Learning Symposium: GeoAI for Disaster Resilience (Paper session; In-person session; Contact: Bing Zhou, bbz5159@psu.edu, Penn State University. Co-organizers: Zhenqi (Ryan) Zhou, University at Buffalo; Lei Zou, Texas A&M University; Yingjie Hu, University at Buffalo; Qunying Huang, University of Wisconsin-Madison; Marcela Suárez, Penn State University, Yi Qiang, University of South Florida; Manzhu Yu, Penn State University; Morteza Karimzadeh, University of Colorado Boulder)
• GeoAI and Deep Learning Symposium: GeoAI for Human Geography (Paper session; In-person session; Contact: Xiao Huang, Emory University, xiao.huang2@emory.edu; Co-organizers: Siqin Wang, University of Southern California; Peter Kedron, University of California, Santa Barbara; John Wilson, University of Southern California)
• GeoAI and Deep Learning Symposium: Human-Centered Geospatial Data Science (Paper session; Hybrid session; Contact: Jiaxin Feng, Jiaxin.Feng@dartmouth.edu, Dartmouth College; Co-organizers: Hoeyun Kwon, Lehman College – The City University of New York; Yuhao Kang, UT Austin)
• GeoAI and Deep Learning Symposium: Social Sensing and GeoAI for Public Health (Paper session; In-person session; Contact: Binbin Lin, bb2020@tamu.edu, Texas A&MUniversity; Co-organizers: Lei Zou, Texas A&M; Yi Qiang, University of South Florida; Xiao Huang, Emory University)
• GeoAI and Deep Learning Symposium: Generative AI – Opportunities and Challenges for GIScience Research (Paper session; in-person session; Contact: Junghwan Kim, Virginia Tech, junghwankim@vt.edu Co-organizer: Kee Moon Jang, MIT; Jinhyung Lee, Western University; Yuhao Kang, UT Austin)
• GeoAI and Deep Learning Symposium: Spatial Data Science and GeoAI Ethics: Bridging Research and Education (Paper session; Hybrid session; Contact: Yue Lin, liny2@uchicago.edu, University of Chicago; Co-organizers: Hongyu Zhang, University of Massachusetts Amherst; Bing Zhou, Penn State University)
• GeoAI and Deep Learning Symposium: GeoAI for Cartography (Paper session; Contact: Xiran Zhou, China University of Mining and Technology, xrzhou@cumt.edu.cn), Wenwen Li, Arizona State University)

To present your research in one of these sessions, please register and submit your abstract at https://aag.secure-platform.com/aag2025/. When you receive confirmation of your submission, please forward your confirmation email to the session organizers by November 14, 2024.

We are very glad to invite you to mark your calendar for joining the forthcoming Geospatial Data Science Speaker Series 2024-2025 events, which are hosted by the GeoDS lab in Geography and co-sponsored by the Data Science Institute @UW-Madison. 

The first event of this semester will be jointly with the Geography Yi-Fu Lectures. We will first have Dr. Kevin Mwenda, an Associate Professor of Population Studies (Research) at the Population Studies and Training Center (PSTC) and the Director of the Spatial Structures in the Social Sciences (S4), visiting UW-Madison and will present “Beyond Maps: Integrating Place and Space for Community Resilience” at 3:30 p.m.-4:30p.m., on October 18, 2024 (Friday), Science Hall 180.

The American Association of Geographers (AAG) Fellows is a recognition and service program that applauds geographers who have made significant contributions to advancing geography.  Congratulations to Professor Song Gao who was recently selected into the 2024 AAG Fellows! 

Source: https://www.aag.org/2024-aag-awards-recognition/#fellows

Dr. Song Gao is an associate professor of geography and the director of the Geospatial Data Science Lab at the University of Wisconsin Madison. He has established himself as one of the thought leaders and highly cited scholars in the field of geospatial artificial intelligence (GeoAI) and was heavily involved in the geospatial modeling of the spread of COVID-19. He has successfully mentored young scholars and students in GIScience, offered workshops and webinars for the AAG and other organizations, and is an associate editor for AAG’s International Encyclopedia of Geography and International Journal of Geographical Information Science. Dr. Gao’s involvement with cutting-edge data science and AI techniques, his commitment to taking on and solving important challenges, and his enthusiasm for working with different international organizations make him a strong asset to the AAG.

The 12th International Conference on Geographic Information Science (GIScience 2023), which is a flagship conference in the field of GIScience, will be held 12 – 15th September, 2023. Leeds, UK. GeoDS lab members have two papers accepted as oral presentations.

Yuhan Ji, Song Gao (2023) Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations. In the Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), No. 43; pp. 43:1–43:6

Qianheng Zhang, Yuhao Kang, Robert Roth (2023) The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography. In the Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), No. 93; pp. 93:1–93:6

Recently, Prof. Song Gao received the 2023 Emerging Scholar Award by the American Association of Geographers (AAG) Spatial Analysis and Modeling (SAM) Specialty Group.

The AAG SAM Emerging Scholar Award The emerging scholar award honors early- to mid-career scholars who have made significant contributions to education and research initiatives that are congruent with the mission of AAG-SAM. The candidates must have received their Ph.D. within the last 10 years and must be a member of the AAG-SAM at the time that the person is being nominated.

This summer, Yunlei, a Ph.D. student from the GeoDS lab, was selected for a Data Science Fellowship program provided by Correlation One. It is a unique fellowship program designed for Ph.D./Master’s students looking to transition from academia into data science roles with a 5% acceptance rate of over 6,000 global applicants. Here she shares her experience with this valuable educational opportunity:

Through the 7-week program, I was involved in various formats of Data Science learning. Every Saturday, I attended online lectures built on real-world cases and delivered by lecturers from universities like Carnegie Mellon, Duke, & Columbia. We learned skills such as exploratory data analysis (EDA), SQL basics, and statistical testing. I also had the opportunity to connect with an assigned mentor from the industry, and career success coaches to improve professional development. Most importantly, I collaborated with a team of 6 with various backgrounds to develop a capstone project using machine learning and data science skills we learned through the lectures.

I greatly appreciate this opportunity to learn from people who are passionate and enthusiastic about data and to expand my network. If you are interested, here is the link to the program: https://www.correlation-one.com/data-science-for-all-women

The University Consortium for Geographic Information Science (UCGIS) is pleased to announce that Dr. Song Gao, Associate Professor of Geography, University of Wisconsin – Madison, has been selected to receive its inaugural Early-Mid Career Research Award.

As a young scholar in the field of GIScience, Dr. Gao’s scholarly output constitutes an impressive list of well-cited publications that are proving to furnish innovative ideas and methods impacting the theory and practice of GIScience within the interface of geospatial artificial intelligence (particularly machine learning), big spatial data, and a more humanistic oriented place-based GIS. In addition, Dr. Gao has secured substantial sums of external funding to support his research, and has begun filling GIScience Community leadership roles. The UCGIS Research Awards Review Committee assesses that Dr. Gao has achieved a national and international GIScience profile and reputation that far exceeds expectations for a junior scholar.

The UCGIS Early-Mid Career Research Award is to celebrate an outstanding early-mid career research record of innovative ideas or methods that lead to research impacts on the theory and/or practice of GIScience or geographic information technology.

UCGIS will honor Song Gao and other award recipients as part of its Symposium 2022 programming activities.

Please join us congratulating our junior student Wen (Wendy) Ye, who is currently an undergraduate triple-majoring in computer science, data science, and statistics as well as a research assistant in the GeoDS Lab under Prof. Song Gao’s mentorship, just got the UW-Madison “Hilldale Undergraduate/Faculty Research Fellowship” and will be awarded  in the 2022 Chancellor’s Undergraduate Awards Ceremony! 

The awarded research project is: Understanding spatial inequality to health care access in Wisconsin through deep learning-based network analysis.

In 2019, our GeoDS Lab’s alumnus Timothy Prestby (who is currently a PhD student at PSU Geography) also got this university fellowship under Prof. Gao’s mentorship.

Other Previous Hilldale Fellows at the University of Wisconsin-Madison:

https://awards.advising.wisc.edu/campus-wide-award-recipients/test-hilldale-fellows/

Recently, Prof. Song Gao is invited to join the Editorial Board of Transactions in GIS, which is a key international journal for publishing geographic information systems/science related research.

Aims and Scope

Transactions in GIS is an international, peer-reviewed journal that publishes original research articles, review articles, and short technical notes on the latest advances and best practices in the spatial sciences. The spatial sciences include all of the different ways in which geography may be used to organize, represent, store, analyze, model and visualize information. The submission of manuscripts that focus on one or more of the following topics among others – is strongly encouraged:

• GIS, GPS, Remote Sensing and related geospatial technologies;
• geospatial data acquisition and sensing; maps and spatial reasoning;
• spatial data infrastructures; standardization and interoperability;
• spatial data structures and databases; geocomputation;
• spatiotemporal analysis, integration and modeling;
• spatial data quality and uncertainty;
• GIS education and certification; GIS and society;
• location privacy;
• and desktop, mobile and Web-based spatially-enabled applications and services.

Keywords

Geographic Knowledge Discovery and Data Mining; Geographic Information Retrieval; Geosensor Networks; Geosimulation; Geospatial Data Integration; Geospatial Semantic Web; Geovisualization; Geographic Information Science; Geographic Information Systems; GIS Architectures and Middleware; GIS and Society; GIS Standardization and Interoperability; GIS&T Education; Global Positioning Systems; Local, Enterprise, Mobile and Web Applications; Location-Based Services; Location Privacy, Data Sharing and Security Maps and Map Services; Ontologies and taxonomies; Public Participation; GIS Remote Sensing; Spatial Analysis; Spatial Cognition and Reasoning; Spatial Data Infrastructures; Spatial Data Quality and Uncertainty; Spatial Databases, Data Structures and Algorithms; Spatial Decision Support Systems; Spatial Dynamics; Spatial Modeling; Spatial Networks; Spatial Thinking; Spatiotemporal Analysis and Modeling; The Spatial Sciences

Here are a set of sessions in which the GeoDS members will make presentations during the American Association of Geographers (AAG) 2022 Annual Meeting. The time is in the US East Time Zone.

Why geoprivacy matters:An international perspective

Day: 2/26/2022
Start Time: 5:20 PM
End Time: 6:40 PM

Panelist : Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3154

Teaching and Research with ArcGIS: Best Practices, Challenges and Opportunities

Day: 2/27/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/5101

Is Artificial Intelligence good for Geography?

Day: 2/27/2022
Start Time: 2:00 PM
End Time: 3:20 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3749

Geospatial Health Symposium #10: Access and Utilization of Health Care Services 2

Day: 2/27/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Yunlei Liang: Spatially-Constrained Community Detection for Health Professional Shortage Area Delineation with Human Mobility Data 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: CyberGIS-enabled spatial epidemiology

Day: 2/27/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Panelist: Song Gao https://aag-annualmeeting.secure-platform.com/a/solicitations/19/sessiongallery/3391

Role of GIS in planning smart and resilient cities III

Day: 3/1/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Yuhan Ji: A Data-driven Method for Identifying Potential Zones for Airport Shuttle Bus Services 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: UCGIS GeoAI & CyberGIS Research Initiative- GeoAI and CyberGIS for Advancing Spatial Decision Making

Day: 3/1/2022
Start Time: 11:20 AM
End Time: 12:40 PM

Song Gao: University of Wisconsin; Reflections on the Development of Spatially Explicit Methods for GeoAI 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: Urban Visual Intelligence

Day: 3/1/2022
Start Time: 2:00 PM
End Time: 3:20 PM

Jacob Kruse: Places for play: Understanding human perception of playability in cities using street view images and deep learning 

Yuhao Kang: University of Wisconsin-Madison; Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling 

AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: GeoAI for Social Sensing

Day: 3/1/2022
Start Time: 3:40 PM
End Time: 5:00 PM

Jinmeng Rao: University of Wisconsin – Madison; CATS: Conditional Adversarial Trajectory Simulation for Privacy-Preserved Data Publication 

Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao, Bo Yan, Rui Zhu, Ling Cai & Ni Lao (2022): A review of location encoding for GeoAI: methods and applications. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2021.2004602

Abstract: A common need for artificial intelligence models in the broader geoscience is to encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters, in a hidden embedding space so that they can be readily incorporated into deep learning models. One fundamental step is to encode a single point location into an embedding space, such that this embedding is learning-friendly for downstream machine learning models. We call this process location encoding. However, there lacks a systematic review on location encoding, its potential applications, and key challenges that need to be addressed. This paper aims to fill this gap. We first provide a formal definition of location encoding, and discuss the necessity of it for GeoAI research. Next, we provide a comprehensive survey about the current landscape of location encoding research. We classify location encoding models into different categories based on their inputs and encoding methods, and compare them based on whether they are parametric, multi-scale, distance preserving, and direction aware. We demonstrate that existing location encoders can be unified under one formulation framework. We also discuss the application of location encoding. Finally, we point out several challenges that need to be solved in the future.

The Center for Spatial Studies (spatial@ucsb) at the University of California, Santa Barbara hosts the 2nd Spatial Data Science Symposium virtually this year with a focus on “Spatial and Temporal Thinking in Data-Driven Methods.”

The symposium aims to bring together researchers from both academia and industry to discuss experiences, insights, methodologies, and applications, taking spatial and temporal knowledge into account while addressing their domain-specific problems.

Professor Song Gao joins as one of the speakers for the following panel discussion sessions:

Webinar: Ethical Issues of Using Geospatial Data in Health Research or Policies During the COVID-19 Pandemic and Beyond
Date and Time: Thursday, December 2, 2021 9:00 am – 11:00 am U.S. Eastern Time

Registration: https://aag-geoethics-series.secure-platform.com/a/solicitations/10/sessiongallery/200

This conversation is co-organized by AAG and the Institute of Space and Earth Information Science (ISEIS), at The Chinese University of Hong Kong (CUHK). During this webinar you will first hear presentations from speakers who are longtime scholars in the field of health geography. Presentations from academic speakers will set the stage for a discussion with panelists who are non-academic stakeholders on this topic in and outside the U.S.

Advances in geospatial technologies and the availability of geospatial big data have enabled researchers to analyze and visualize geospatial data in great detail. Geospatial methods are now widely used to uncover the complex patterns of diverse social phenomena, such as human mobility and the COVID-19 pandemic. However, using or mapping individual-level confidential geospatial data (e.g., the locations of people’s residences and activities) involves certain risk of disclosure and privacy violation. Such risk of geoprivacy violation has recently become a widespread concern as many COVID-19 control measures (e.g., digital contact tracing; self-quarantine methods; and disclosure of location visited by infected persons) used by governments or public health agencies collected individual-level geospatial data. These COVID-19 control measures pose a particularly serious geoprivacy threat because recent advances in geospatial artificial intelligence (GeoAI) and high-performance computing may significantly increase the accuracy of spatial reverse engineering (e.g., by linking high-resolution geospatial data with other data such as census or survey data to discover the identity of specific individuals). On the other hand, false inference, such as false positives from facial recognition for example, can result in big consequences.

This webinar will focus on ethical issues of using geospatial data analytics in health research and practices, especially in the context of the COVID-19 pandemic and beyond. The presentations will cover a wide range of topics, including uncertainties in analyzing relationships between disease spread and geographic environment, geoprivacy concerns for different COVID-19 control measures (e.g., digital contact tracing), addressing people’s concerns for geoprivacy in times of pandemics, IRB issues in health research during COVID-19, legal issues arose and policy implications of using individual-level confidential geospatial for controlling the spread of pandemics. Questions to be explored include: How can researchers protect people’s geoprivacy when using individual-level geospatial data to gain insights into the dynamics and patterns of infectious diseases? What disease control measures have higher risk of geoprivacy violation, which may significantly affect people’s acceptance of these measures and undermine their effectiveness in controlling the spread of COVID-19 or future pandemics? How can public health authorities balance the need for disease control and individual geoprivacy protection? What are the legal and technical issues in data sharing? How to minimize the unintended negative consequences such as the stigmatization of and discrimination against infected persons as a result of geoprivacy breaches or location disclosure?

Beginning in 2021, GISRUK launched a series of international seminars celebrating innovation in Geographical Information Science, Chaired by Dr. Peter Mooney.

Dr. Song Gao was invited to give a seminar titled “GeoAI for Human Mobility Analytics and Location Privacy Protection” on 3rd November 2021.

Geographical Information Science Research UK (GISRUK) is the largest academic conference in Geographic Information Science in the UK. For the last 30 years, GISRUK has attracted international researchers and practitioners in GIS and related fields, including geography, data science, urban planning and computer science, to share and discuss the latest advances in spatial computing and analysis. The event in 2022 will be the 30th annual GISRUK conference. The conference will be held on the 5th – 8th April 2022 and hosted by the Geographic Data Science Lab and Department of Geography and Planning at the University of Liverpool. We look forward to welcoming you in person to the conference next year.

Yunlei Liang, Kyle W. McNair, Song Gao, Aslıgül Göçmen. (2021). Exploring Store Visit Changes During the COVID-19 Pandemic Using Mobile Phone Location Data. In Shih-Lung Shaw and Daniel Sui (Eds): Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic (Chapter 13). pp. 253-275, Springer.

Abstract:

When the World Health Organization (WHO) announced the pandemic of COVID-19, people around the globe scattered to stores for groceries, supplies, and other miscellaneous items in preparation for quarantine. The dynamics of retail visits changed dramatically due to the pandemic outbreak. The study intends to analyze how the store visit patterns have changed due to the lockdown policies during the COVID-19 pandemic. Using mobile phone location data, we build a time-aware Huff model to estimate and compare the visiting probability of different brands of stores over different time periods. We are able to identify certain retail and grocery stores that have more or fewer visits due to the pandemic outbreak, and we detect whether there are any trends in visiting certain retail establishments (e.g., department stores, grocery stores, fast-food restaurants, and cafes) and how the visiting patterns have adjusted with lockdowns. We also make comparisons among brands across three highly populated U.S. cities to identify potential regional variability. It has been found that people in large metropolitan areas with a well-developed transit system tend to show less sensitivity to long-distance visits. In addition, Target, which is a department store, is found to be more negatively affected by longer-distance trips than other grocery stores after the lockdown. The findings can be further applied to support policymaking related to public health, urban planning, transportation, and business in post-pandemic cities.

Highlighted results:

• The dwell time distribution of visitors in Target.

• Frequency of Visits from home Census Block Groups to Whole Foods Markets.

Jacob Kruse, Yuhao Kang, Yu-Ning Liu, Fan Zhang, and Song Gao. “Places for play: Understanding human perception of playability in cities using street view images and deep learning.” Computers, Environment and Urban Systems 90 (2021): 101693.

Abstract: Play benefits childhood development and well-being, and is a key factor in sustainable city design. Though previous studies have examined the effects of various urban features on how much children play and where they play, such studies rely on quantitative measurements of play such as the precise location of play and the duration of play time, while people’s subjective feelings regarding the playability of their environment are overlooked. In this study, we capture people’s perception of place playability by employing Amazon Mechanical Turk (MTurk) to classify street view images. A deep learning model trained on the labelled data is then used to evaluate neighborhood playability for three U.S. cities: Boston, Seattle, and San Francisco. Finally, multivariate and geographically weighted regression models are used to explore how various urban features are associated with playability. We find that higher traffic speeds and crime rates are negatively associated with playability, while higher scores for perception of beauty are positively associated with playability. Interestingly, a place that is perceived as lively may not be playable. Our research provides helpful insights for urban planning focused on sustainable city growth and development, as well as for research focused on creating nourishing environments for child development.

Highlighted results:

• Our deep learning model was able to produce playability scores whose distribution closely matched that of the training data.

• Using images labeled by our deep learning model, we produced a map of playability scores for Boston, Seattle, and San Francisco.

• Downtown areas in the three cities studied had high lively scores but low playability scores.

Today, the U.S. National Science Foundation (NSF) announced the establishment of 11 new NSF National Artificial Intelligence Research Institutes. Each institute will receive $20 million for a total $220 million investment by NSF. Building off of seven institutes funded in 2020, the new program is meant to broaden access to AI to solve complex societal problems.

Prof. Song Gao joins the Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE).

Led by The Ohio State University, ICICLE will build the next generation of Cyberinfrastructure to render Artificial Intelligence (AI) more accessible to everyone and drive its further democratization in the larger society.

ICICLE will build and prove its system around three use-inspired science application domains: smart foodsheds, digital agriculture, and animal ecology. Analogous to watersheds, foodsheds define the geographical and human elements that affect how, when and where food is grown and consumed. Digital agriculture seeks to use technology to improve the yield and efficiency of crops, while animal ecology focuses on the roles of animals in agriculture and the environment.

More information on: https://icicle.ai/