Utilizing Large Language Models in geographic contexts - Experiences from the FIU GIS Center

South Florida GIS Explo 2023. West Palm Beach, F

Context-Based Customization of Routing Functions for Web GIS Applications

This poster presentation features three route planning applications developed by the Florida International University GIS Center and the Geomatics program at the University of Florida, and outlines their context based differences. The first route planner has been developed for cyclists in three Florida counties, i.e. Miami Dade County, Broward County, and Palm Beach County. The second route planner computes safe pedestrian routes to schools and has been developed for Miami Dade County. The third route planner combines pre-compiled cultural/eco routes and point-to-point route planning for the City of Coral Gables. This poster highlights the differences in design (user interface) and implementation (routing options) between the three route planners as a result of a different application context and target audience

MMC 4936 Web GIS for Journalists

Web GIS is a special topics course designed to give students a solid foundation in Web publishing and Web GIS. By the end of the semester, students will: Learn the basics of HTML5 and CSS3. Learn the basics of JavaScript and JQuery Produce a new layout for the Zen Garden project. Participate in a crowdsourced project on King Tide Day. Produce an interactive photo slideshow with HTML5/CSS3. Participate in a mobile-social reporting project on Election Day. Produce a multimedia map story with HTML5/CSS3. Learn how to give and receive constructive criticism

Towards Understanding the Geospatial Skills of ChatGPT: Taking a Geographic Information Systems (GIS) Exam

This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their capabilities and limitations in specialized subject areas such as GIS. Human learning of spatial concepts significantly differs from LLM training methodologies. Therefore, this study aims to assess ChatGPT\u27s performance and ability to grasp geospatial concepts by challenging it with a real GIS exam. By analyzing ChatGPT\u27s responses and evaluating its understanding of GIS principles, we gain insights into the potential applications and challenges of LLMs in spatially-oriented fields. We conduct our evaluation with two models, GPT-3.5 and GPT-4, to understand whether general improvements of an LLM translate to improvements in answering questions related to the spatial domain. We find that both GPT variants can pass a balanced, introductory GIS exam, scoring 63.3\% (GPT-3.5) and 88.3\% (GPT-4), which correspond to grades D and B+ respectively in standard US letter grading scale. In addition, we also identify specific questions and topics where the LLMs struggle to grasp spatial concepts, highlighting the challenges in teaching such topics to these models. Finally, we assess ChatGPT\u27s performance in specific aspects of GIS, including spatial analysis, basic concepts of mapping, and data management. This granular analysis provides further insights into the strengths and weaknesses of ChatGPT\u27s GIS literacy. This research contributes to the ongoing dialogue on the integration of AI models in education and can provide guidance for educators, researchers, and practitioners seeking to leverage LLMs in GIS. By focusing on specific questions or concepts that pose difficulties for the LLM, this study addresses the nuances of teaching spatial concepts to AI models and offers potential avenues for improvement in spatial literacy within future iterations of LLMs

Improving Discovery and Patron Experience Through Data Mining

As information professionals, we know simple database searches are imperfect. With rich and expansive digital collections, patrons may not find content that is buried in a long list of results. So, how do we improve discovery of pertinent materials and offer serendipitous experience? Following the example of recommendation functionality in online applications like Netflix, we have developed a recommendation function for our digital library system that provides relevant content beyond the narrow scope of patrons\u27 original search parameters. This session will outline the reasoning, methodology, and design of the recommendation system as well as preliminary results from implementation

ChatGPT as a mapping assistant: A novel method to enrich maps with generative AI and content derived from street-level photographs

This paper explores the concept of leveraging generative AI as a mapping assistant for enhancing the efficiency of collaborative mapping. We present results of an experiment that combines multiple sources of volunteered geographic information (VGI) and large language models (LLMs). Three analysts described the content of crowdsourced Mapillary street-level photographs taken along roads in a small test area in Miami, Florida. GPT-3.5-turbo was instructed to suggest the most appropriate tagging for each road in OpenStreetMap (OSM). The study also explores the utilization of BLIP-2, a state-of-the-art multimodal pre-training method as an artificial analyst of street-level photographs in addition to human analysts. Results demonstrate two ways to effectively increase the accuracy of mapping suggestions without modifying the underlying AI models: by (1) providing a more detailed description of source photographs, and (2) combining prompt engineering with additional context (e.g. location and objects detected along a road). The first approach increases the suggestion accuracy by up to 29%, and the second one by up to 20%.Comment: Submitted to The Fourth Spatial Data Science Symposiu

Library-Based Data Curation, Management and Interdisciplinary Research at Florida International University: Reciprocal Use of Data through Collaboration

This paper shows the Florida International University (FIU) Libraries\u27 efforts in research data management, implementation, and practice. The FIU Library-based data team has led research projects that are directly built upon institutional and other data repositories, data hubs, and data visualization tools, through collaboration with the user community. We will present the technology setup and configuration of such a data framework, which includes Dataverse, ESRI’s ArcGIS Data Hub, and other data collection and visualization tools. We will also discuss the fiscal and organizational structure needed to support research data management initiatives. Using a couple of our applied research projects, we will demonstrate how users interact, collaborate, and contribute. We will discuss the challenges we encounter when it comes to data sharing, data curation, management, and most importantly, serving users at all levels of data literacy. In addition, we will present from a researcher’s perspective how he/she can manage his/her publications and associated research data and make them discoverable. We will showcase the websites of interdisciplinary research projects, such as the FIU Jack D. Gordon Institute for Public Policy’s (JGI) Security Research Hub (see also: https://srh.fiu.edu/home/) and the USAID-funded Global Water for Sustainability Program (see also: http://dpanther.fiu.edu/glows/). These project websites use FIU institutional repository and research data management platforms (see also: http://rdm.fiu.edu/dataverse/). We will demonstrate how we support a community-based research project using FIU’s research data repository and data hub, as well as community-based data repositories such as ESRI’s Living Atlas of the World

dPanther FIU

Presentation on dPanther, FIU\u27s digital platform and framework designed under service oriented architecture (SOA) with comprehensive GIS capabilities

Library-Based Data Curation, Management and Interdisciplinary Research at Florida International University: Reciprocal Use of Data through Collaboration

This paper shows the Florida International University (FIU) Libraries\u27 efforts in research data management, implementation, and practice. The FIU Library-based data team has led research projects that are directly built upon institutional and other data repositories, data hubs, and data visualization tools, through collaboration with the user community. We will present the technology setup and configuration of such a data framework, which includes Dataverse, ESRI’s ArcGIS Data Hub, and other data collection and visualization tools. We will also discuss the fiscal and organizational structure needed to support research data management initiatives. Using a couple of our applied research projects, we will demonstrate how users interact, collaborate, and contribute. We will discuss the challenges we encounter when it comes to data sharing, data curation, management, and most importantly, serving users at all levels of data literacy. In addition, we will present from a researcher’s perspective how he/she can manage his/her publications and associated research data and make them discoverable. We will showcase the websites of interdisciplinary research projects, such as the FIU Jack D. Gordon Institute for Public Policy’s (JGI) Security Research Hub (see also: https://srh.fiu.edu/home/) and the USAID-funded Global Water for Sustainability Program (see also: http://dpanther.fiu.edu/glows/). These project websites use FIU institutional repository and research data management platforms (see also: http://rdm.fiu.edu/dataverse/). We will demonstrate how we support a community-based research project using FIU’s research data repository and data hub, as well as community-based data repositories such as ESRI’s Living Atlas of the World

AI for Archives: Using Facial Recognition to Enhance Metadata

The goal of this research project was to determine the most effective facial recognition applications that could be implemented into digital archive image collections from libraries, museums, and cultural heritage institutions. Computer scientists and librarians at Florida International University collaborated to conduct qualitative assessments of both face detection and face search using photographs from FIU’s digital collections. Specifically, the facial recognition platforms OpenCV, Face++, and Amazon AWS were analyzed. This project seeks to assist LYRASIS community members who wish to incorporate facial recognition and other artificial intelligence technology into their digital collections and repositories as a method to reduce research time and enhance their collections with more complete metadata