This paper discusses the use of spatial data for risk and natural disaster management. The importance of remote-sensing (RS), Geographic Information System (GIS) and Global Navigation Satellite System (GNSS) data is stressed by comparing studies of the use of these technologies for natural disaster management. Spatial data sharing is discussed in the context of the establishment of Spatial Data Infrastructures (SDIs) for natural disasters. Some examples of SDI application in disaster management are analyzed, and the need for participation from organizations and governments to facilitate the exchange of information and to improve preventive and emergency plans is reinforced. Additionally, the potential involvement of citizens in the risk and disaster management process by providing voluntary data collected from volunteered geographic information (VGI) applications is explored. A model relating all of the spatial data-sharing aspects discussed in the article was suggested to elucidate the importance of the issues raised.

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Resumo No Brasil existe a perspectiva de crescimento expressivo do volume de dados a ser processado pelas prestadoras de serviços de rastreamento de veículos em decorrência do aumento natural do uso de sistemas de rastreamento. Esse crescimento gera a necessidade da incorporação de ferramentas analíticas nos sistemas de gerenciamento do rastreamento e monitoramento de veículos e na gestão de risco, para aumentar a sua eficiência e atender ao crescimento do mercado. O presente estudo tem como objetivo o desenvolvimento de um modelo para identificação do Padrão de Comportamento Espacial de Veículos em Movimento – PCEVM, utilizando uma ferramenta de análise espacial para auxiliar o processo de tomada de decisão no monitoramento de veículos e na gestão do sistema de transporte urbano. Embora os resultados obtidos sejam válidos para a particular configuração apresentada neste artigo, a metodologia de análise proposta permite a caracterização de padrões espaciais de movimentação de veículos na área e no período considerados, o que é útil, por exemplo, para planejadores municipais, empresas de logística e empresas seguradoras. Conclui-se que o resultado da comparação entre o posicionamento coletado em tempo real com o modelo de distribuição espacial obtido na caracterização dos padrões de comportamento espaciais é capaz de detectar anormalidades do comportamento de movimentação do veículo e oferecer subsídios para a tomada de decisão em sistemas de monitoramento e gerenciamento de veículos, que visem à segurança patrimonial e pessoal, identificando comportamentos de movimentação não esperados ou de comportamentos de risco.

This article attempts to characterize urban land use patterns by variograms parameters from multispectral high spatial resolution satellite images. The variography is used to characterize variability and to characterize a land use urban pattern. Dataset compiled from Salvador, Bahia, Brazil, consists in single QuickBird satellite scene, geometrically corrected, obtained on August 2nd, 2005. Four land urban patterns were identified at the study area and characterized using remote sensing image classification parameters. The principal components were calculated over the variance-covariance matrix of the four spectral channels of the images and while the variograms were calculated on the first principal components of each of the four urban patterns. In general the use of the variogram to aid the classification has proved satisfactory for the study area. Parameters (sill, range and nugget effect) were valuable tools for classification areas and to characterize occupation patterns.

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Background: The 2019 coronavirus disease pandemic (COVID 19) spread rapidly across Brazil. The country has 27 federative units, with wide regional differences related to climate, lifestyle habits, socioeconomic characteristics and population density. Therefore, we aimed to document and monitor the increase in COVID 19 cases across each federative unit in Brazil, by tracking its progression from inception to 15 May 2020. Methods: Observational study. Results: The first confirmed COVID 19 case in the country was notified in Sao Paulo on 26 February, while the first death occurred on 17 March, in Rio de Janeiro. Since then, there has been a dramatic increase in both confirmed cases and deaths from the disease. Sao Paulo, in the Southeast region, was initially considered the COVID 19 epidemic epicentre in Brazil. However, 10 states in the North and Northeast regions were ranked among the 14 highest incidences (over 100 cases per 100,000 people) observed on 15 May. Higher incidence rates (>100 cases per 100,000) were associated to higher rates of inadequate water supply and sewerage (OR, 5.83 (95% CI, 1.08 to 29.37, P=0.041)). North and Northeast states with the highest social vulnerability index scores had higher increases in the incidence rate between 14 April and 15 May. States with medium human development index (HDI) showed higher incidence increases from 14 April to 15 May, being seven of them with ratios in the range from 27.49 to 63.73 times. Conclusion: Spreading of COVID 19 in Brazil differs across both regions and federative units, being influenced by different socioeconomic contexts.