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https://hdl.handle.net/10217/189447

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Browsing Research Data by Author "Keys, Patrick"

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    ItemOpen Access
    Dataset associated with "A machine-learning approach to human footprint index estimation with applications to sustainable development"
    (Colorado State University. Libraries, 2020) Keys, Patrick; Barnes, Elizabeth; Carter, Neil
    Fundamental to the success of sustainable development is a foundation of intact ecosystems. While the United Nations Sustainable Development Goal 15, “Life on Land”, seeks to protect biodiversity in terrestrial ecosystems, accelerating human-driven changes across the Earth system are undermining efforts to preserve biodiversity. Understanding this tension has never been more urgent and requires tools that reveal pathways for development that also support biodiversity. Here we introduce a near-present, global-scale machine learning-based human footprint index which is capable of routine update. By comparing global changes in the machine learning human footprint index between 2000 and 2019 to national-scale biodiversity metrics for Goal 15, we find that some countries are experiencing increases in their human footprint while biodiversity metrics are improving as well. We further examine development and policy dynamics to uncover enabling mechanisms for balancing increased human pressure with biodiversity gains. This has immediate policy relevance, since the majority of countries globally are not on track to achieve Goal 15 by the declared deadline of 2030. Moving forward, the machine learning human footprint index can be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system.
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    ItemOpen Access
    Dataset associated with "Visions of the Arctic Future: Blending Computational Text Analysis And Structured Futuring to Create Story-based Scenarios"
    (Colorado State University. Libraries, 2021) Keys, Patrick; Meyer, Alexis
    The future of Arctic social systems and natural environments is highly uncertain. Climate change will lead to unprecedented phenomena in the pan-Arctic region, such as regular shipping traffic through the Arctic Ocean, urban growth, military activity, expanding agricultural frontiers, and transformed indigenous societies.While intergovernmental to local organizations have produced numerous synthesis-based visions of the future, a challenge in any scenario exercise is capturing the possibility space of change. In this work, we employ a computational text analysis to objectively generate unique thematic input for novel, story-based visions of the Arctic. Specifically, we develop a corpus of more than 2,000 articles in publicly accessible, English-language Arctic newspapers that discuss the future in the Arctic. We then perform a latent Dirichlet allocation, resulting in ten distinct topics and sets of associated keywords. From these topics and keywords, we design ten story-based scenarios employing the Mānoa mashup, science fiction prototyping, and other methods. Our results demonstrate that computational text analysis can feed directly into a creative futuring process, whereby the output stories can be traced clearly back to the objectively identified topics and keywords. We discuss our findings in the context of the broader field of Arctic scenarios, and show that the results of this computational text analysis produce complementary stories to the existing scenario literature. We conclude that story-based scenarios can provide vital texture toward understanding the myriad possible Arctic futures.