Browsing by Subject "Environmental justice"

Now showing 1 - 5 of 5
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    A new, lower threshold for lead poisoning in children means more kids will get tested – but the ultimate solution is eliminating lead sources
    (The Conversation US, Inc., 2021-11-05) Filippelli, Gabriel; Earth and Environmental Sciences, School of Science
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    Biden’s infrastructure plan targets lead pipes that threaten public health across the US
    (The Conversation US, Inc., 2021-05-04) Filippelli, Gabriel; Earth and Environmental Sciences, School of Science
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    Decision 2020 Electing Indiana's Future: Addressing 21st Century Environmental Challenges
    (2020-09) Kharbanda, Jesse; McCabe, Janet; Frank, Indra; Hoffman, Jill
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    The Fall Creek: A Localized Understanding of the Anthropocene Era
    (2019) Faris, Tim; Petranek, Stefan
    My creative work surrounding the Fall Creek is a photographic take on the junction of nature and the human made, as well as a personal account of the environment. It is my hope that through my work, I will begin to understand a more globalized quality of nature in the Anthropocene Era we are living in. My work seeks to examine why humans place hierarchies on the natural environment and how this affects our perception of the natural world.
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    Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network
    (Frontiers, 2022) Johnson, Daniel P.; Lulla, Vijay; Geography, School of Liberal Arts
    As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability—including environmental determinants of COVID-19 infection—into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for “what-if” analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.