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Matemática e Estatística / Mathematics and Statistics

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Browsing Matemática e Estatística / Mathematics and Statistics by Sustainable Development Goals (SDG) "15:Proteger a Vida Terrestre"

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  • Big data sets in environmental studies
    Publication . Oliveira, Amilcar
    Big Data datasets for environmental studies play a crucial role in understanding, monitoring and addressing large-scale environmental issues. Big Data datasets for environmental studies deal with huge volumes of data coming from various sources such as satellites, remote sensors, weather stations, sensor networks and mobile devices. These datasets include detailed information on climate change, biodiversity, air quality, water resources and other environmental parameters. Integrating and analyzing data from different sources allows for a more comprehensive understanding of environmental standards and helps in making informed decisions. The generation of environmental data occurs in real time, especially with the increased use of sensors and continuous monitoring technologies. The ability to handle the velocity of data is essential for detecting rapid changes in the environment and responding to critical events such as natural disasters. Predictive models help predict climate patterns, identify areas of environmental risk and assess the impacts of human activities on the ecosystem. This data is crucial for developing mitigation strategies, adapting to climate change and conserving biodiversity. In summary, Big Data datasets play a fundamental role in environmental studies, providing a comprehensive and real-time understanding of environmental challenges, enabling the implementation of effective strategies for conservation and sustainability.
    2023Conference object Open access Show more
  • Statistical models for categorical data: brief review for applications in ecology
    Publication . Ramos, Maria do Rosário; Oliveira, Manuela M.; Borges, José G.; McDill, Marc E.
    A brief review of statistical models for prediction of categorical data is presented, with emphasis on the binary type. Several methods have been adopted to build predictive models for binary and other types of categorical data and response variables. The focus here is on generalized linear models and generalized additive models, widely applied in problems in Ecology, when the goal is to fit a model to data of presence/absence type or any other categorical response.The estimation methods used for generalized linear models and generalized additive models as well its statistical properties are discussed. Some examples in ecology are addressed.
    2015Conference object Restricted access Show more