Presenters: UCSB Center for Spatial Studies, USC Spatial Sciences Institute, Esri
How would you teach spatial thinking in a general education course to post-secondary students with no formal training in geography? If geographers and GIScientists wish to foster spatial literacy in society this is a question that must be answered. We invite colleagues to contribute to an interactive discussion about how to teach novice learners the central elements of spatial thinking – concepts of space, tools of representation, and processes of reasoning.
An intermittently updated literature discussing concepts and questions related to spatial thinking already exists. Many of those concepts and questions remain the same. However, as new environmental and social challenges emerge and geospatial technologies change, which of those concepts and questions should be prioritized in post-secondary education merits revisiting. Moreover, the existing literature tends to offer abstract, academic treatments of these concepts that are removed from the experiences of non-specialists. This session will draw from and extend that literature by restarting a practical discussion about how to teach spatial thinking with the goal of fostering spatial literacy. Colleagues will be invited to share their own conceptions of spatial thinking, the geographic questions they use to teach key concepts, their pedagogical approaches, and their classroom successes.
Devon Borthwick: Identifying Post-Disaster Damages in Anna Maria Island, Florida, With Aerial Images and Volunteers Hailey Nicole Richardson: Modeling Illicit Drug Flow on Interstates using Spatial Interaction Models Zhijie Zhou: “How Much Can We Trust Your Habitat Maps?” – Uncertainties in Multi-scale Spatial-Explicit Conservation Planning from the Area-of-Habitat (AoH) Mapping Approach Purna Saud: Time Series Analysis of Delay-Induced Stochastic Oscillations in COVID-19 Case Dynamics Tianci Guo: PHYIELD: Phenology-integrated Yield Estimation Leveraging Physics Knowledge and Deep Learning
This session will compare practices and discuss methods for teaching introduction to GIS and other geospatial courses, bringing together a diverse group of faculty. The participants will share their experience teaching the course, their format for the course, and what does and does not work in the classroom (projects vs. exams, teaching both lecture and lab vs. teaching lecture with TAs teaching labs, types of projects, working with/around AI, etc., ESRI software vs. others, hybrid vs. face-2-face vs. online, etc.).
This session will also focus on the future direction of GIS and teaching it, and how we are instructors of GIS courses prepare our students to work within today’s geospatial world while also discussing how our methods have changed over time. Anyone interested is invited to also share their experiences in teaching the course, along with ideas on how to give students the best experience concerning learning GIS.
As we focus on “Navigating the Geospatial Frontier: Future Directions for Academia and Its Partners” for UCGIS 2025, the need for discussion on building robust infrastructures to support future GIS applications is important. The rapidly advancing fields of geospatial big data science and GeoAI demand scalable, cost-efficient, and high-performance infrastructure. This panel will explore the design and implementation of infrastructures that integrate key components such as data acquisition, storage, processing, analysis, visualization, and security. The discussion will feature as a use-case Harvard CGA’s project on “Building a Robust Infrastructure for Geospatial Big Data/Data Science”. Funded by the Office of the Vice Provost for Research, this project aims to empower Harvard researchers to execute Geospatial AI Data Science projects across diverse research use cases by strengthening the university’s Geographic Information Systems (GIS) infrastructure and services for future GIS application. Utilizing the computing resources of the New England Research Cloud (NERC) and Harvard High-Performance Compute Cluster FASRC, we have developed a suite of products and solutions designed to make geospatial analytics more accessible, faster, cost-effective, and impactful for Harvard researchers. This suite of products include geospatial datasets, GIS big data solutions, GIS big data systems deployed on cloud, software and data licenses, user learning resources, open-source Github repositories, publications and more. The panel will also address strategies for managing diverse datasets—including social media, climate data, and WebAI—while promoting the use of open-source geospatial datasets for broader academic applications. Emphasis will be placed on high-performance infrastructures that support both vector and raster big data using scalable storage systems, distributed computing frameworks, and cloud-based solutions. This session aims to present Harvard’s initiatives in this area while fostering a collaborative exchange of ideas and experiences on other initiatives. By sharing case studies and demos, we seek to provide actionable insights and strategies for designing, implementing, and managing cutting-edge infrastructures tailored to the evolving demands of future geospatial analytics.
