ANISOTROPIC GEOSTATISTICAL MODELING OF CARBON DIOXIDE EMISSIONS IN THE BRAZILIAN NEGRO BASIN, PANTANAL SUL

Currently the cycle of carbon dioxide, CO2, has been methodically studied for researches on environmental science disciplines. Mainly because CO2 is one of the gases in the Atmosphere that has been considered responsible for what is now called the .Greenhouse Effect.. The other gases are Methane (CH4), Nitrous oxide (N2O), Chlorofluorocarbons (CFCs), Hydro-fluorocarbons (HFCs), and Sulfur hexafluoride (SF6

Spatial exploration of Streptococcus pneumoniae clonal clustering in São Paulo, Brazil

OBJECTIVES: To examine the spatial distribution of Streptococcus pneumoniae and its clonal patterns collected between 2002 and 2006 in São Paulo, Brazil. METHODS: As part of an observational study in São Paulo city, Brazil, S. pneumoniae isolates routinely cultured from blood, respiratory specimens, or cerebrospinal and other profound fluids were selected. Additionally, only isolates with either penicillin (PEN) intermediate (I) or resistant (R) status on routine antibiogram were included, in order to obtain a higher probability of clonal isolates. A single I/R S. pneumoniae isolate per patient was included and submitted to genotypic determination by pulsed field gel electrophoresis (PFGE). Minimum inhibitory concentrations (MICs) were determined for the isolates by Etest® to PEN and other antimicrobials. Each isolate was geocoded in a digital map. The Kernel function and ratio methods between total isolates vs. clones were used in order to explore possible cluster formations. RESULTS: Seventy-eight (78) S. pneumoniae community isolates from two major outpatient centers in São Paulo, Brazil, were selected from the databank according to their penicillin susceptibility profile, i.e. R or I to penicillin assessed by oxacillin disc diffusion. Of these, 69 were submitted to PFGE, 65 to MIC determination, and 48 to spatial analytical procedures. Preliminary spatial analysis method showed two possible cluster formation located in southwest and southeast regions of the city. CONCLUSION: Further analyses are required for precisely determining the existence of S. pneumoniae clusters and their related risk factors. Apparently there is a specific transmission pattern of S. pneumoniae clones within certain regions and populations. GIS and spatial methods can be applied to better understand epidemiological patterns and to identify target areas for public health interventions.Universidade Federal de São Paulo (UNIFESP) Special Laboratory of Clinical MicrobiologyHospital Israelita Albert EinsteinGC-2 Gestão do Conhecimento Científico LtdInstituto Nacional de Pesquisas Espaciais Department of Image ProcessingUNIFESP, Special Laboratory of Clinical MicrobiologySciEL

Uso de Modelo Aditivo Generalizado para Análise Espacial da Suscetibilidade a Movimentos de Massa

Neste artigo, é analisada a distribuição espacial da suscetibilidade a movimentos de massa da Bacia Hidrográfica do Rio Luís Alves, localizada no estado de Santa Catarina.  A modelagem empregada baseia-se em processos pontuais espaciais, na qual se define uma medida de suscetibilidade que varia continuamente sobre a região de estudo e é estimada por meio de métodos de modelos aditivos generalizados (GAM). A suscetibilidade a movimentos de massa, neste contexto, é quantificada por níveis de probabilidades. O procedimento empregado incorpora ao modelo fatores condicionantes de suscetibilidade, de forma simples e de fácil interpretação. O método viabiliza a construção de superfícies de decisão que permitem a geração de mapas com contornos de tolerância baseado em medidas de probabilidade. Tais mapas auxiliam na identificação de áreas de alta/baixa suscetibilidade, uma vez que a hipótese nula de suscetibilidade constante na região de estudo pode ser testada. O resultado da aplicação do modelo mostrou que a variação espacial da suscetibilidade na área de estudo foi significativa a certos fatores condicionantes, apontando um caminho para avanços nos sistemas técnicos de monitoramento e alerta a estas situações, e ampliando as possibilidades para as decisões necessárias que possam minimizar os impactos de processos geomorfológicos danosos, tais como movimentos de massa.This paper analyzes spatial distribution of mass movements susceptibility from Luís Alves watershed, Santa Catarina State, Brazil. The modeling framework adopted in this research is based on spatial point processes, which defines a susceptibility measure that varies continuously over the study region and is estimated by means of generalized additive modeling methods. In this paper, the mass movements susceptibility is quantified by probability levels. The procedure employed allows susceptibility factors to be incorporated into the model in a simple way and easy interpretation. The procedure also allows the construction of maps with tolerance contours which help identify areas of significantly high/low susceptibility and an overall test for the null hypothesis of constant risk over the region. The application of the model to the data of susceptibility to mass movements, presented results consistent with the geomorphology of the study region, showed that the spatial variation in the susceptibility is significant, and pointing a way to the advance of monitoring and decisions making support systems

A proposal for using data from antimicrobial prescriptions: the EUREQA experience

This study demonstrates that the use of information from medical prescriptions is essential for understanding the dynamics of community bacterial resistance. The resulting analysis can also influence and help establish more adequate public health policies on the control and optimization of antimicrobial use. The article demonstrates the use of a logical model developed by the EUREQA project for acquisition, classification, interpretation, and analysis of data from prescriptions for oral antimicrobial use.A presente nota pesquisa demonstra que o uso das informações de receituário ou prescrição médica tem fundamental valor para a compreensão das correlações da dinâmica da resistência bacteriana comunitária. Além disso, a análise dos dados gerada pode ajudar a estabelecer medidas e políticas de saúde pública mais adequadas para o controle e a otimização do consumo de antimicrobianos. Para isso, o artigo usa como base o modelo lógico desenvolvido pelo Projeto EUREQA voltado para aquisição, classificação, interpretação e análise das informações relacionadas à prescrição dos antimicrobianos de uso oral.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Instituto Nacional de Pesquisas EspaciaisUniversidade Federal de São Paulo (UNIFESP) Escola Paulista de MedicinaUniversidade Federal do Paraná Laboratório de Estatística e GeoinformaçãoUNIFESP, EPMSciEL

Uso de simulação estocástica não linear para inferências de atributos espaciais numéricos

O presente artigo explora o uso de uma ferramenta geoestatística conhecida por simulação Sequencial por Indicação para inferir atributos numéricos a partir de um conjunto pontual de amostras. No trabalho utilizou-se um conjunto amostral de elevações, obtidos em uma fazenda experimental do Brasil para geração de modelos numéricos, representados por grades regulares, em ambiente de Sistemas de Informação Geográfica. Os métodos geoestatísticos assumem que o atributo umérico se comporta como uma variável aleatória em cada localização da superfície terrestre. Assim, os dados de altimetria, são inferidos a partir de um conjunto de realizações das variáveis aleatórias definidas para cada nó da grade. Além disso, essa técnica possibilita a obtenção de mapas de incertezas relacionados com as inferências de altimetria. Por isso, o trabalho explora, também, a definição de métricas de incerteza a partir dos conjuntos de realizações de mapas de altimetria, representados como grades regulares. ABSTRACT: This work explores the use of a geostatistical tool named Indicator sequential Simulation to infer numerical attributes from a sample point set. Elevation samples from a Brazilian experimental farm are used as input for creation of regular grids to be used as numerical models in Geographical Information Systems environment. The geostatistical procedures consider the numerical attribute as a random variable for each location of the earth surface. In this way, the elevation values are estimated from a set of random variable realizations simulated for each grid location. Furthermore, the presented simulation technique allows the generation of uncertainty maps related to the elevation inferences. On that account, this work also explores metrics for uncertainty definitions from a set of realizations of elevation grid maps.Pages: 437-44

Spatial Variability Analysis of CBERS CCD Images in Forest Regions

This paper analyses the spatial variability of remote sensing images obtained by the Charge Coupled Device (CCD) sensor presented in the China Brazil Earth Remote Satellite (CBERS) at spatial regions of forests. Semivariograms are mostly used to model the spatial variability of environmental data as elevation, temperature, health risks, geology, hydrology and mining information, etc... In this work the semivariograms were used to model the variability of the spectral information represented in remote sensing images. A limited number of sample points of images were considered, instead of full images, due to the size constraints of the input data to calculate the semivariances. A very large amount of input points requires a very high processing time to evaluate the semivariograms. Thus, this work aims to explore the possibility of representing the spatial variability of an entire image using a limited amount of samples draw from it. Semivariogram analysis were done with random sample sets of different sizes. Two spatial regions with different patterns were considered, one with typical forested area and other with partially deforested area. It was found that is feasible to get representative semivariograms of the whole image from small sample sets. In addition it was obtained different semivariograms for the two regions showing that stationary hypothesis cannot be assumed for forest images with different patterns.Pages: 2180-218

Integrating geostatistical tools in geographical information systems

Geostatistical analysis can be used for spatial modelling in a diversity of geographic applications. In particular, geostatistical methods and tools are of great utility when searching for surface models for fitting a data set sampled as points. By exploring the spatial structure of the data being modeled, the geostatistics yields procedures for surface estimation that contemplates the spatial variability inherent to, for instance, a large set of environmental data. At the same time these developments can generate a spatial estimate of the uncertainty of the information conveyed by those surface representing the attribute information. When developing a geographical application on a GIS platform that has no geostatistical tools, these analysis are performed by exchanging the data to be modeled between the GIS and the geostatistical package where the analysis will take place. These back and forth procedure can be very cumbersome, and in fact , can take a lot of time out that should be used in the task of being thinking on the modelling for that data. These are some reasons for the current trend to incorporate geostatistical facilities in the most popular GIS packages. Initial efforts has been done in the direction to integrate a geostatistical module in the GIS software. Unfortunately these is not enough to guarantee that the geostatistical power is fully incorporated in geographic analysis using GIS packages. Geostatistical procedures must be implemented as operators to be used in spatial modelling languages. Also, the uncertainty related to the modeled data should be considered and propagated to the results of a spatial model that contains non-deterministic parameters and data. This paper addresses the central aspects involved in the incorporation of geostatistical facilities in a GIS modelling environment. The requirements for a geostatistical module to be aggregated into a GIS package and the issues related to geostatistical and error propagation operators in GIS modeling languages are presented and discussed