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Articles 1 - 10 of 10
Full-Text Articles in Spatial Science
Mapping Responsible Workflows For Geospatial Data Science: Developing The I-Guide Data Ethics Toolkit, Peter T. Darch, Kyra M. Abrams, Ivan Y M Kong
Mapping Responsible Workflows For Geospatial Data Science: Developing The I-Guide Data Ethics Toolkit, Peter T. Darch, Kyra M. Abrams, Ivan Y M Kong
I-GUIDE Forum
AI workflows in geospatial data science offer significant societal benefits but raise ethical, transparency, and reproducibility challenges. Current ethical frameworks and tools are often hard to integrate into daily research practice. This paper introduces the I-GUIDE Data Ethics Toolkit (DET), a lightweight suite designed for users of the NSF-funded Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE). Based on a longitudinal mixed-methods study, including surveys, interviews, and observations, we identified five design priorities: usability, anticipatory planning, distributed responsibility, comprehensive coverage, and policy compliance. We integrated existing AI and data research lifecycles into an eight-stage I-GUIDE Research Lifecycle, serving …
Assessing Vulnerabilities Associated With High-Risk Dams In Utah, Courtney Flint Dr., Michael Englert, Upmanu Lall Dr.
Assessing Vulnerabilities Associated With High-Risk Dams In Utah, Courtney Flint Dr., Michael Englert, Upmanu Lall Dr.
I-GUIDE Forum
Across the United States, the age of dam infrastructure presents a substantial threat. The potential for structural failure associated with these aging dams is exacerbated by increased climate uncertainty and the threat of dam overtopping due to extreme and prolonged precipitation events. These compound threats place downstream areas at pronounced risk. These downstream areas vary widely. Some are relatively free of vulnerable elements, while others have a complex mix of infrastructure, populations, and ecologically important elements. This paper highlights the risk situation associated with aging dams and hydroclimatic extremes, the methodological and geospatial challenges and opportunities associated with mapping the …
Explainable Artificial Intelligence To Interpret Spatially-Explicit Impacts Of Future Climate Change On Species Distribution, Lei Song, Amy E. Frazier, Peter Kedron, Diogo S. A. Araujo, Diyang Cui, Brian J. Enquist, Brian Maitner, Cory Merow, Gabriel M. Moulatlet, Efthymios I. Nikolopoulos, Patrick R. Roehrdanz
Explainable Artificial Intelligence To Interpret Spatially-Explicit Impacts Of Future Climate Change On Species Distribution, Lei Song, Amy E. Frazier, Peter Kedron, Diogo S. A. Araujo, Diyang Cui, Brian J. Enquist, Brian Maitner, Cory Merow, Gabriel M. Moulatlet, Efthymios I. Nikolopoulos, Patrick R. Roehrdanz
I-GUIDE Forum
Biodiversity is essential for maintaining ecosystem balance and functionality, providing vital services such as climate regulation. The rapid decline in biodiversity, driven by habitat loss, habitat fragmentation, and climate change, poses significant threats to ecosystems. Climate change, in particular, is fundamentally altering habitats, leading to shifts in species distributions. However, existing research often lacks decomposed contribution analyses, particularly spatially, for a changing individual environmental attributes when modeling species distribution as an aggregate result of all factors and their interactions. Such analyses are crucial for identifying climate refugia and prioritizing conservation efforts. Taking endangered mammal species as an example, this study …
Statistical Downscaling Of Climate Datasets With Deep Generative Model And Bayesian Inference, Guiye Li, Guofeng Cao
Statistical Downscaling Of Climate Datasets With Deep Generative Model And Bayesian Inference, Guiye Li, Guofeng Cao
I-GUIDE Forum
Facing the challenges of global climate change, precise and high spatial resolution climate data are crucial and in pressing need for scientific research and analysis. However, most existing datasets are only available with very coarse spatial resolution and demand large-scale resolution enhancement. Meanwhile, climate datasets own much more intricate textures than natural images. Statistical downscaling or super-resolution (SR) with the deep-learning-based generative model might be a promising approach to address these challenges. It is worth noting that a learned Bayesian reconstruction with generative models (L-BRGM) method was proposed recently. The proposed Bayesian deep learning framework employs a single pre-trained generative …
Communicating Uncertainty And Cataloging Bias In Spatial Data Science Education, Peter Kedron, Jiahua Chen
Communicating Uncertainty And Cataloging Bias In Spatial Data Science Education, Peter Kedron, Jiahua Chen
I-GUIDE Forum
Uncertainty is an unavoidable part of any spatial analysis, which makes quantifying and communicating uncertainty a requirement of any spatial data science study. However, current curricula leave students understanding the importance of uncertainty and concerned about potential bias, but without an actionable framework to improve their workflows or inferences. We propose a framework rooted in Bloom’s Taxonomy for introducing these concepts to spatial data science students.
Evaluating The Environmental Impacts Of U.S. Historical Oil Spill Incidents, Yiming Liu, Hua Cai
Evaluating The Environmental Impacts Of U.S. Historical Oil Spill Incidents, Yiming Liu, Hua Cai
Graduate Industrial Research Symposium
Exposure to risks associated with the production and usage of products, particularly oil, poses significant threats to both ecological systems and human health. Notable examples include the Gulf War Oil Spill (1991) and the Deepwater Horizon Oil Spill (2010). However, numerous smaller-scale oil spills, which collectively contribute to substantial oil releases, often remain overlooked. To fill this gap, our study first developed a detailed oil spill incidents database, covering 1967 to 2023. We quantified the released amount (RA) of oil spills recorded by the National Oceanic and Atmospheric Administration (NOAA). Subsequently, we utilized life cycle impact indicators in ReCiPe to …
An Agent-Based Modeling Approach To Spatial Accessibility, Alexander C. Michels, Shaowen Wang
An Agent-Based Modeling Approach To Spatial Accessibility, Alexander C. Michels, Shaowen Wang
I-GUIDE Forum
Place-based spatial accessibility represents the ability of populations within geographic units to access goods and services, and thus is an important indicator for sustainable development. Existing spatial accessibility models treat population as simply demand, calculating statistics or optimizing average cost for the population within each geographic unit, rather than modeling individual decisions. This paper proposes AgentAccess, a general-purpose Agent-Based Model (ABM) for spatial accessibility analysis. An ABM framework brings us closer to reality by simulating individual and imperfect decision-making. We introduce the model and compare its results against existing spatial accessibility models using a case study of hospital beds in …
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
I-GUIDE Forum
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …
Geoannotator: A Collaborative Semi-Automatic Platform For Constructing Geo-Annotated Text Corpora, Morteza Karimzadeh, Alan M. Maceachren
Geoannotator: A Collaborative Semi-Automatic Platform For Constructing Geo-Annotated Text Corpora, Morteza Karimzadeh, Alan M. Maceachren
Purdue University Libraries Open Access Publishing Fund
Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks …
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
Passive Visual Analytics Of Social Media Data For Detection Of Unusual Events, Kush Rustagi, Junghoon Chae
The Summer Undergraduate Research Fellowship (SURF) Symposium
Now that social media sites have gained substantial traction, huge amounts of un-analyzed valuable data are being generated. Posts containing images and text have spatiotemporal data attached as well, having immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. However, the large volume of unstructured social media data hinders exploration and examination. To analyze such social media data, the S.M.A.R.T system provides the analyst with an interactive visual spatiotemporal analysis and spatial decision support environment that assists in evacuation planning …