Upcoming Webinars & Workshops

Previous Webinars & Workshops

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Recruiting and Mentoring Graduate Students in GIScience and Cartography

Panelists: Dr. Kathleen Stewart (Univ. of Maryland), Dr. Angela Yao (Univ. of Georgia), Dr. Caglar Koylu (Univ. of Iowa), and Dr. Junghwan Kim (Virginia Tech)
Hosts: Dr. Hoeyun Kwon (Univ. of Colorado-Boulder) and Dr. Armita Kar (The Ohio State University)

Key questions:

• What qualifications (e.g., research fit, academic achievements, GPA, etc.) do professors value the most for students? What will make students stand out in the application pool?
• How do professors help students find their research interests and develop their own work?  What will help students stay motivated when they experience common struggles such as imposter syndrome, procrastination, and unsuccessful research experiments?
• What different expectations or considerations do professors have when selecting and advising international students? Do professors find any struggles particularly for international students?

Sponsored in partnership with AAG, CaGIS, and GISphere

Measuring the Unmeasurable: Models of Geographical Context
Presenter: Stewart Fotheringham (Arizona State University)
Moderator: Ling Bian (University at Buffalo)

The issue of whether place significantly affects spatial behavior has long created both a philosophical and an operational schism within geography. Here we show how these schisms can be bridged by identifying how place and behavior can be linked through recognizing and incorporating what we term intrinsic contextual effects and behavioral contextual effects into models of spatial behavior. We argue that spatial modeling frameworks that attempt to relate spatial behavior to aspects of people and places might be seriously misspecified if they do not incorporate both types of contextual effects. We compare three popular statistical modeling frameworks that encompass placed-based contextual effects: spatial error models, multilevel models, and multiscale geographically weighted regression (MGWR). Using data from the 2020 US Presidential election, we demonstrate the reassuring similarity of the results from the three frameworks but also the superiority of MGWR. The inclusion of essentially unmeasurable effects within a nomothetic framework provides an important bridge between two previously distinct philosophies within geography and acts as a binding force within the discipline.

Air Pollution, Extreme Weather, and Urban Exposome: Evolution of Exposure Science in the Era of AI and Precision Health
Presenter: Dr. Kai Zhang (University at Albany - State University of New York)
Moderator: Dr. Zhe Zhang (Texas A&M University)

As urban populations continue to increase in the U.S. and globally, there is growing concern about the impact of urban environmental and social determinants on population health, ranging from traditional pollutants to emergent issues such as natural disasters. This talk will focus on modern exposure science approaches to characterize exposures to air pollution, natural disaster, social determinants and urban exposome as well as to incorporate them in health research to understand potential biological mechanisms. These approaches include advanced statistical models, GIS, and machine learning methods. The approaches highlighted throughout the talk are applicable to epidemiological studies, risk assessment and public health actions.

My first 275 days at NSF
Presenter: Dr. May Yuan (University of Texas at Dallas)
Moderator: Dr. Xinyue Ye (Texas A&M University)

I joined NSF on July 18, 2022 as a rotator via an Intergovernmental Personnel Act (IPA) assignment. With fascinations and optimism, I venture into the country’s most respectful and at times mysterious funding agency that supports basic science research across all disciplines. Besides standard federal training, I get to know NSF’s culture and principles that guide operations within and outside the agency, including interactions with prospective principal investigators (PI) and managing merit reviews. In this talk, I would like to share what I, as a geographic information scientist, have learned at NSF, what common issues I see in proposals, and what funding opportunities are available for advancing research and education in GIScience, broadly defined. I welcome questions and comments on how GIScience communities can leverage NSF support to advance research and education throughout the webinar.

Modeling weather-related power outages: Applying analytics to inform electrical utilities
Presenter: Dr. Steven Quiring (The Ohio State University)
Moderator: Dr. Lei Zou (Texas A&M University)

Storms can cause significant damage to the electrical power system, leading to prolonged power interruptions to a large number of customers. The number of weather-related outages has increased significantly in recent years. This presentation summarizes the data-driven power outage models that we have developed for investor-owned electrical utilities in the United States.

AAG Workshop (in-person only)

Professional Development for Current and Future Geospatial Data Scientists
(part of I-GUIDE, https://i-guide.io)

AAG Workshop (in-person only)

Reinforcing and Enhancing Skills Sets of GIS Librarians and other GIS Support Personnel
(part the GLOW program, https://www.lib.uchicago.edu/conferences/glow/)

The Convergence Curriculum for Geospatial Data Science
Presenter: Shaowen Wang (Univ. of Illinois - Urbana-Champaign), Eric Shook (Univ. of Minnesota)
Moderator: Forrest Bowlick (Univ. of Massachusetts - Amherst)

The Convergence Curriculum for Geospatial Data Science is a major initiative supported by the National Science Foundation Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE); and represents an integrative framework for preparing scholars, students, and professionals to solve complex convergence problems through critical computational and spatial thinking.

Urban Informatics for Transport: Measuring Networks and Access around the World
Presenter: Dr. Geoff Boeing (University of Sothern California)
Moderator: Dr. Yingjie Hu (University at Buffalo)

Cities worldwide exhibit a variety of transport and land use patterns and configurations that shape human mobility, equity, health, and livelihoods. This talk discusses recent developments using open data and open source tools, in conjunction with local collaborators, to model and measure these patterns around the world.

Using movement as a marker to study behavior responses to environmental disruptions
Presenter: Dr. Somayeh Dodge, UC Santa Barbara | Moderator: Dr. Qiusheng Wu, UT Knoxville

Intentional movement through space is one of the traits shared by humans and animals to perform activities. These spatial movements are fundamental to the dynamics of ecosystems, cities and environments, and can be utilized as a key to understand and model environmental and behavioral variability in social and ecological systems. From the lens of geographic information science, this webinar presents a number of cases studies using computational approaches to analyze the effects of environmental disruptions on peoples and animal’s behavior across different geographic and temporal scales.

Tobler’s First Law in GeoAI
Presenter: Dr. Wenwen Li (Arizona State University) | Moderator: Dr. Yongmei Lu (Texas State University)

GeoAI, or geospatial artificial intelligence, is powerful to solve complex geospatial problems. Leveraging recent breakthroughs in deep learning and advanced computing, GeoAI enables scalable processing and intelligent analysis of geospatial big data. A core issue is how to make AI integral to the geospatial science framework. This presentation will introduce a new GeoAI model that enables automated spatial object detection, especially of natural features, in a weakly supervised manner. It will also cover discussions about the enablement of spatially explicit machine learning by integrating Tobler’s First Law of Geography; as well as the generalizability of the GeoAI model, in detecting both manmade and natural features on the surface of the Earth and other planets.