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@Article{LiCaDeCoAlBaRo:2017:ExStAm,
               author = "Li, Zhichao and Catry, Thibault and Dessay, Nadine and Costa 
                         Gurgel, Helen da and Almeida, Cl{\'a}udio Aparecido de and 
                         Barcellos, Christovam and Roux, Emmanuel",
          affiliation = "{Tsinghua University} and {Institut de Recherche pour le 
                         D{\'e}veloppement (IRD)} and {Institut de Recherche pour le 
                         D{\'e}veloppement (IRD)} and {Universidade de Bras{\'{\i}}lia 
                         (UnB)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Funda{\c{c}}{\~a}o Oswaldo Cruz (FIOCRUZ)} and {Institut de 
                         Recherche pour le D{\'e}veloppement (IRD)}",
                title = "Regionalization of a landscape-based hazard index of malaria 
                         transmission: an example of the state of Amap{\'a}, Brazil",
              journal = "Data",
                 year = "2017",
               volume = "2",
               number = "4",
                pages = "37",
             keywords = "malaria, landscape-based hazard index, large-scale, Amazon.",
             abstract = ": Identifying and assessing the relative effects of the numerous 
                         determinants of malaria transmission, at different spatial scales 
                         and resolutions, is of primary importance in defining control 
                         strategies and reaching the goal of the elimination of malaria. In 
                         this context, based on a knowledge-based model, a normalized 
                         landscape-based hazard index (NLHI) was established at a local 
                         scale, using a 10 m spatial resolution forest vs. non-forest map, 
                         landscape metrics and a spatial moving window. Such an index 
                         evaluates the contribution of landscape to the probability of 
                         human-malaria vector encounters, and thus to malaria transmission 
                         risk. Since the knowledge-based model is tailored to the entire 
                         Amazon region, such an index might be generalized at large scales 
                         for establishing a regional view of the landscape contribution to 
                         malaria transmission. Thus, this study uses an open large-scale 
                         land use and land cover dataset (i.e., the 30 m TerraClass maps) 
                         and proposes an automatic data-processing chain for implementing 
                         NLHI at large-scale. First, the impact of coarser spatial 
                         resolution (i.e., 30 m) on NLHI values was studied. Second, the 
                         data-processing chain was established using R language for 
                         customizing the spatial moving window and computing the landscape 
                         metrics and NLHI at large scale. This paper presents the results 
                         in the State of Amap{\'a}, Brazil. It offers the possibility of 
                         monitoring a significant determinant of malaria transmission at 
                         regional scale.",
                  doi = "10.3390/data2040037",
                  url = "http://dx.doi.org/10.3390/data2040037",
                 issn = "2306-5729",
                label = "lattes: 1240868188538349 5 LiCaDeCoApBaRo:2017:ExStAm",
             language = "en",
           targetfile = "data-02-00037-v2.pdf",
        urlaccessdate = "27 nov. 2020"
}


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