@InProceedings{GuimarăesSousSantGome:2017:AnEsDo,
               author = "Guimar{\~a}es, Ricardo Jos{\'e} de Paula Souza e and Sousa Neto, 
                         Juliana Raiyanni and Santos, karla de Souza and Gomes, Alessandra 
                         Rodrigues",
          affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "An{\'a}lise espacial da doen{\c{c}}a de Chagas no estado do 
                         Par{\'a} no per{\'{\i}}odo de 2010-2014",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3569--3575",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Chagas disease is a very large public health problem in the 
                         Brazilian Amazon region. Transmission of vector-borne diseases is 
                         often associated with changes in vegetation cover. Thus, the use 
                         of geoprocessing is extremely important because it contributes to 
                         the structuring and analysis of risk factors for the population. 
                         The objective of this study was to evaluate the spatial behavior 
                         of Chagas'' disease in the state of Par{\'a} in the period 
                         2010-2014 using geoprocessing tools. Data from Chagas disease (CD) 
                         were obtained from SINAN-NET. Other sources of data collection 
                         were: IBGE, INPE and Google Earth Engine. The processing, analysis 
                         and interpretation of data were performed in TerraView and ArcGis 
                         software. In the spatial analysis were used the Global Moran 
                         Index, Local Moran Index (LISA), Kernel density estimation and 
                         Kernel ratio. The total of 9,737 cases of Chagas disease was 
                         obtained from 82 municipalities of Par{\'a}. The correlation test 
                         showed a positive correlation between the disease and the 
                         population and a negative correlation between the disease and EVI 
                         / NDVI. The LISA allowed the identification of clusters of areas, 
                         visualizing spatial dependence and analyzing spatial variability 
                         behavior. The Kernel presented in all the studied years a cluster 
                         of greater intensity in the municipality of Abaetetuba. The 
                         results of the correlation analysis showed that constant 
                         deforestation and population increase may be interfering with the 
                         increase in CD cases. The Kernel analysis indicated the locations 
                         for disease control and monitoring.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60207",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLT78",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLT78",
           targetfile = "60207.pdf",
                 type = "Sa{\'u}de",
        urlaccessdate = "27 abr. 2024"
}