Geography Research Works

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Now showing 1 - 5 of 41
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    Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling
    (Springer Nature, 2023-02-13) Memarsadeghi, Natalie; Stewart, Kathleen; Li, Yao; Sornsakrin, Siriporn; Uthaimongkol, Nichaphat; Kuntawunginn, Worachet; Pidtana, Kingkan; Raseebut, Chatree; Wojnarski, Mariusz; Jongsakul, Krisada; Jearakul, Danai; Waters, Norman; Spring, Michele; Takala-Harrison, Shannon
    Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes. A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type. The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand–Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand. The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.
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    An Evaluation of the Climate Change Preparedness of Terrestrial Protected Areas
    (2022-05-01) Panday, Frances Marie; Hurrt, George; Lamb, Rachel
    The rate at which the climate changes and the direction of these shifts is highly variable across the landscape. As proposed by Loarie et al. (2009), the concept of a climate change velocity (CV) adds a spatial component to the rate at which the temperature increases across the landscape. Identifying where regions will experience the most significant changes in climate conditions is highly valuable for the management of areas with high ecological and societal value, such as protected areas (PAs). To examine the relationship between climate velocity and protected areas, Loarie et al. (2009) proposes the concept of a climate residence time (CRT), which estimates the length of time current climate conditions will remain in a given spatial location before shifting. Current infrastructure design managing protected areas is outdated and may be ill-equipped to handle future changes in climate. Current work examining the relationship between protected area and the CV is relatively new, but results are promising. Here, we evaluate the climate-change preparedness of terrestrial protected areas in MD by first, quantifying the magnitude of future changes using the climate residence time, and second, evaluating their capacity to manage changes by qualitatively scoring their associated management plans for climate adaptation and/or mitigation language. This two-fold approach showed that most PAs have climate residence times less than or equal to 1.5 years and had plans with little to no language addressing climate change and its associated impacts. This suggests that PAs in MD are poorly prepared for future changes in climate. Given these results, including CVs and CRTs within PA management plans would improve a park’s adaptive capacity but also signal the need for a cross-coordinated management effort that transcends different management and governance scales.
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    Peer-Reviewed Offset Protocol for U.S. Forest Projects 1.0
    (2022-04-29) Albee, Madeleine; Hoffman Delett, Camille; Panday, Frances Marie; Lamb, Rachel; Hurtt, George
    The protocol was developed for submission into Second Nature's Peer Review Offset Network by the Campus Forest Carbon Project. As of October 2022, the protocol is still under review. This offset protocol is a modified version of a previously adopted protocol created by the California Environmental Protection Agency Air Resources Board as a Compliance Offset Protocol for U.S. Forest Projects. The Campus Forest Carbon Project modified this protocol using NASA Carbon Monitoring System science to integrate a high-resolution remote sensing and modeling based quantification methodology into the voluntary carbon offset market for forest projects. Accompanying the protocol is a background and development document that outlines the specific changes to the protocol and the context surrounding its development.
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    Campus Forest Carbon Project Technical Guidance Document
    (2022-08-11) Panday, Frances Marie; Howerton, Michael; Kopp, Katelyn; Hurtt, George; Lamb, Rachel
    The technical guidance document was created for the Office of Sustainability to support the inclusion of forest carbon into UMD's Greenhouse Gas Inventory. This document outlines the Campus Forest Carbon's project role within UMD's climate action plan and the approach to calculating forest carbon dynamics on UMD managed and owned properties.
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    Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling
    (Springer Nature, 2013-11-05) Thelen, Brian; French, Nancy HF; Koziol, Benjamin W; Billmire, Michael; Owen, Robert Chris; Johnson, Jeffrey; Ginsberg, Michele; Loboda, Tatiana; Wu, Shiliang
    A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.