Spatial autocorrelation of ndvi and gvi indices derived from landsat/tm images for soybean crops in the western of the state of Paraná in 2004/2005 crop season

This research aims at studying spatial autocorrelation of Landsat/TM based on normalized difference vegetation index (NDVI) and green vegetation index (GVI) of soybean of the western region of the State of Paraná. The images were collected during the 2004/2005 crop season. The data were grouped into five vegetation index classes of equal amplitude, to create a temporal map of soybean within the crop cycle. Moran I and Local Indicators of Spatial Autocorrelation (LISA) indices were applied to study the spatial correlation at the global and local levels, respectively. According to these indices, it was possible to understand the municipality-based profiles of tillage as well as to identify different sowing periods, providing important information to producers who use soybean yield data in their planning.

Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to December 2010. For statistical analysis, two techniques were combined: Poisson regression with generalized additive models and principal model component analysis. Those analysis techniques complemented each other and provided more significant estimates in the estimation of relative risk. The models were adjusted for temporal trend, seasonality, day of the week, meteorological factors and autocorrelation. In the final adjustment of the model, it was necessary to include models of the Autoregressive Moving Average Models (p, q) type in the residuals in order to eliminate the autocorrelation structures present in the components. RESULTS For every 10:49 μg/m3 increase (interquartile range) in levels of the pollutant PM10 there was a 3.0% increase in the relative risk estimated using the generalized additive model analysis of main components-seasonal autoregressive – while in the usual generalized additive model, the estimate was 2.0%. CONCLUSIONS Compared to the usual generalized additive model, in general, the proposed aspect of generalized additive model − principal component analysis, showed better results in estimating relative risk and quality of fit.

Spatial distribution and socioeconomic context of tuberculosis in Rio de Janeiro, Brazil

OBJECTIVE To analyze the spatial distribution of risk for tuberculosis and its socioeconomic determinants in the city of Rio de Janeiro, Brazil.METHODS An ecological study on the association between the mean incidence rate of tuberculosis from 2004 to 2006 and socioeconomic indicators of the Censo Demográfico (Demographic Census) of 2000. The unit of analysis was the home district registered in the Sistema de Informação de Agravos de Notificação (Notifiable Diseases Information System) of Rio de Janeiro, Southeastern Brazil. The rates were standardized by sex and age group, and smoothed by the empirical Bayes method. Spatial autocorrelation was evaluated by Moran’s I. Multiple linear regression models were studied and the appropriateness of incorporating the spatial component in modeling was evaluated.RESULTS We observed a higher risk of the disease in some neighborhoods of the port and north regions, as well as a high incidence in the slums of Rocinha and Vidigal, in the south region, and Cidade de Deus, in the west. The final model identified a positive association for the variables: percentage of permanent private households in which the head of the house earns three to five minimum wages; percentage of individual residents in the neighborhood; and percentage of people living in homes with more than two people per bedroom.CONCLUSIONS The spatial analysis identified areas of risk of tuberculosis incidence in the neighborhoods of the city of Rio de Janeiro and also found spatial dependence for the incidence of tuberculosis and some socioeconomic variables. However, the inclusion of the space component in the final model was not required during the modeling process.

Bacterial meningitis and living conditions

INTRODUCTION: Bacterial meningitis has great social relevance due to its ability to produce sequelae and cause death. It is most frequently found in developing countries, especially among children. Meningococcal meningitis occurs at a high frequency in populations with poor living conditions. This study describes the temporal evolution of bacterial meningitis in Salvador, Brazil, 1995-2009, and verifies the association between its spatial variation and the living conditions of the population. METHODS: This was an ecological study in which the areas of information were classified by an index of living conditions. It examined fluctuations using a trend curve, and the relationship between this index and the spatial distribution of meningitis was verified using simple linear regression. RESULTS: From 1995-2009, there were 3,456 confirmed cases of bacterial meningitis in Salvador. We observed a downward trend during this period, with a yearly incidence of 9.1 cases/100,000 population and fatality of 16.7%. Children aged <5 years old and male were more affected. There was no significant spatial autocorrelation or pattern in the spatial distribution of the disease. The areas with the worst living conditions had higher fatality from meningococcal disease (β = 0.0078117, p < 0.005). CONCLUSIONS: Bacterial meningitis reaches all social strata; however, areas with poor living conditions have a greater proportion of cases that progress to death. This finding reflects the difficulties for ready access and poor quality of medical care faced by these populations.

Spatial distribution of disseminated histoplasmosis and AIDS co-infection in an endemic area of Northeastern Brazil

Abstract: INTRODUCTION: The spatial distribution of disseminated histoplasmosis (DH) and acquired immunodeficiency syndrome (AIDS) co-infection in adult residents of Fortaleza, Ceará, Brazil was evaluated. METHODS: Socio-demographic data for the DH/AIDS cases were obtained from a reference hospital, and socio-environmental indicators were obtained from an official Brazilian institute. Kernel analysis and local indicators of spatial autocorrelation (LISA) cluster maps were used to estimate the case density within the city. RESULTS: DH/AIDS cases were concentrated in the Northwestern and Southwestern peripheral areas of the city, related with low human development indices, but different from AIDS cases distribution. CONCLUSION: Risk factors other than AIDS infection must affect histoplasmosis development in this area.

Mortality from Cardiovascular Diseases in the Elderly: Comparative Analysis of Two Five-year Periods

Background:Cardiovascular diseases are the leading cause of death in Brazil. The better understanding of the spatial and temporal distribution of mortality from cardiovascular diseases in the Brazilian elderly population is essential to support more appropriate health actions for each region of the country.Objective:To describe and to compare geospatially the rates of mortality from cardiovascular disease in elderly individuals living in Brazil by gender in two 5-year periods: 1996 to 2000 and 2006 to 2010.Methods:This is an ecological study, for which rates of mortality were obtained from DATASUS and the population rates from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística). An average mortality rate for cardiovascular disease in elderly by gender was calculated for each period. The spatial autocorrelation was evaluated by TerraView 4.2.0 through global Moran index and the formation of clusters by the index of local Moran-LISA.Results:There was an increase, in the second 5-year period, in the mortality rates in the Northeast and North regions, parallel to a decrease in the South, South-East and Midwest regions. Moreover, there was the formation of clusters with high mortality rates in the second period in Roraima among females, and in Ceará, Pernambuco and Roraima among males.Conclusion:The increase in mortality rates in the North and Northeast regions is probably related to the changing profile of mortality and improvement in the quality of information, a result of the increase in surveillance and health care measures in these regions.

Assemblage of immature Odonata (Insecta, Anisoptera) in streams of the Mato Grosso do Sul State: spatial implications

ABSTRACT This study investigated the assemblages attributes (composition, abundance, richness, diversity and evenness) and the most representative genera of Odonata, Anisoptera at Água Boa and Perobão Streams, Iguatemi River basin, Brazil. Both are first order streams with similar length that are impacted by riparian forest removal and silting. Quarterly samplings were conducted from March to December 2008 in the upper, intermediate and lower stretch of each stream. The Mantel test was used to check the influence of spatial autocorrelation on the Odonata composition. Spatial variations in the composition were summarized by the Principal Coordinates Analysis (PCoA) using Mantel test residuals. The effects of spatial correlation on richness and abundance were investigated by the spatial correlogram of Moranʼs I coefficients. The most representative genera in each stream were identified by the Indicator Value Method. The spatial variations in the attributes of the assemblages were assessed using analysis of variance of null models. We collected 500 immature individuals of 23 genera and three families. Among the attributes analyzed only the composition and abundance showed significant spatial differences, with the highest mean abundance found in the Perobão Stream. Miathyria and Zenithoptera were the indicator genera of the Água Boa Stream and Erythrodiplax, Libellula, Macrothemis, Progomphus and Tramea were the indicator genera of the Perobão Stream.

Population genetic analysis of Colombian Trypanosoma cruzi isolates revealed by enzyme electrophoretic profiles

Although Colombia presents an enormous biological diversity, few studies have been conducted on the population genetics of Trypanosoma cruzi. This study was carried out with 23 Colombian stocks of this protozoa analyzed for 13 isoenzymatic loci. The Hardy-Weinberg equilibrium, the genetic diversity and heterogeneity, the genetic relationships and the possible spatial structure of these 23 Colombian stocks of T. cruzi were estimated. The majority of results obtained are in agreement with a clonal population structure. Nevertheless, two aspects expected in a clonal structure were not discovered in the Colombian T. cruzi stocks. There was an absence of given zymodemes over-represented from a geographical point of view and the presumed temporal stabilizing selective phenomena was not observed either in the Colombian stocks sampled several times through the years of the study. Some hypotheses are discussed in order to explain the results found.

Spatial analysis of the distribution of leprosy in the State of Ceará, Northeast Brazil

The aim of this study was to describe spatial patterns of the distribution of leprosy and to investigate spatial clustering of incidence rates in the state of Ceará, Northeast Brazil. The average incidence rate of leprosy for the period of 1991 to 1999 was calculated for each municipality of Ceará. Maps were used to describe the spatial distribution of the disease, and spatial statistics were applied to explore large- and small-scale variations of incidence rates. Three regions were identified in which the incidence of leprosy was particularly high. A spatial gradient in the incidence rates was identified, with a tendency of high rates to be concentrated on the North-South axis in the middle region of the state. Moran's I statistic indicated that a significant spatial autocorrelation also existed. The spatial distribution of leprosy in Ceará is heterogeneous. The reasons for spatial clustering of disease rates are not known, but might be related to an heterogeneous distribution of other factors such as crowding, social inequality, and environmental characteristics which by themselves determine the transmission of Mycobacterium leprae.

Local-scale spatial variation in diversity of social wasps in an Amazonian rain forest in Caxiuanã, Pará, Brazil (Hymenoptera, Vespidae, Polistinae)

Polistine wasps are important in Neotropical ecosystems due to their ubiquity and diversity. Inventories have not adequately considered spatial attributes of collected specimens. Spatial data on biodiversity are important for study and mitigation of anthropogenic impacts over natural ecosystems and for protecting species. We described and analyzed local-scale spatial patterns of collecting records of wasp species, as well as spatial variation of diversity descriptors in a 2500-hectare area of an Amazon forest in Brazil. Rare species comprised the largest fraction of the fauna. Close range spatial effects were detected for most of the more common species, with clustering of presence-data at short distances. Larger spatial lag effects could also be identified in some species, constituting probably cases of exogenous autocorrelation and candidates for explanations based on environmental factors. In a few cases, significant or near significant correlations were found between five species (of Agelaia, Angiopolybia, and Mischocyttarus) and three studied environmental variables: distance to nearest stream, terrain altitude, and the type of forest canopy. However, association between these factors and biodiversity variables were generally low. When used as predictors of polistine richness in a linear multiple regression, only the coefficient for the forest canopy variable resulted significant. Some level of prediction of wasp diversity variables can be attained based on environmental variables, especially vegetation structure. Large-scale landscape and regional studies should be scheduled to address this issue.

Selecting statistical models to study the relationship between soybean yield and soil physical properties

Statistical models allow the representation of data sets and the estimation and/or prediction of the behavior of a given variable through its interaction with the other variables involved in a phenomenon. Among other different statistical models, are the autoregressive state-space models (ARSS) and the linear regression models (LR), which allow the quantification of the relationships among soil-plant-atmosphere system variables. To compare the quality of the ARSS and LR models for the modeling of the relationships between soybean yield and soil physical properties, Akaike's Information Criterion, which provides a coefficient for the selection of the best model, was used in this study. The data sets were sampled in a Rhodic Acrudox soil, along a spatial transect with 84 points spaced 3 m apart. At each sampling point, soybean samples were collected for yield quantification. At the same site, soil penetration resistance was also measured and soil samples were collected to measure soil bulk density in the 0-0.10 m and 0.10-0.20 m layers. Results showed autocorrelation and a cross correlation structure of soybean yield and soil penetration resistance data. Soil bulk density data, however, were only autocorrelated in the 0-0.10 m layer and not cross correlated with soybean yield. The results showed the higher efficiency of the autoregressive space-state models in relation to the equivalent simple and multiple linear regression models using Akaike's Information Criterion. The resulting values were comparatively lower than the values obtained by the regression models, for all combinations of explanatory variables.

Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Probabilistic structure of an annual extreme rainfall series of a coastal area of the State of São Paulo, Brazil

This study aimed to describe the probabilistic structure of the annual series of extreme daily rainfall (Preabs), available from the weather station of Ubatuba, State of São Paulo, Brazil (1935-2009), by using the general distribution of extreme value (GEV). The autocorrelation function, the Mann-Kendall test, and the wavelet analysis were used in order to evaluate the presence of serial correlations, trends, and periodical components. Considering the results obtained using these three statistical methods, it was possible to assume the hypothesis that this temporal series is free from persistence, trends, and periodicals components. Based on quantitative and qualitative adhesion tests, it was found that the GEV may be used in order to quantify the probabilities of the Preabs data. The best results of GEV were obtained when the parameters of this function were estimated using the method of maximum likelihood. The method of L-moments has also shown satisfactory results.

Management zones definition using soil chemical and physical attributes in a soybean area

Several equipments and methodologies have been developed to make available precision agriculture, especially considering the high cost of its implantation and sampling. An interesting possibility is to define management zones aim at dividing producing areas in smaller management zones that could be treated differently, serving as a source of recommendation and analysis. Thus, this trial used physical and chemical properties of soil and yield aiming at the generation of management zones in order to identify whether they can be used as recommendation and analysis. Management zones were generated by the Fuzzy C-Means algorithm and their evaluation was performed by calculating the reduction of variance and performing means tests. The division of the area into two management zones was considered appropriate for the present distinct averages of most soil properties and yield. The used methodology allowed the generation of management zones that can serve as source of recommendation and soil analysis; despite the relative efficiency has shown a reduced variance for all attributes in divisions in the three sub-regions, the ANOVA did not show significative differences among the management zones.

Spatial statistics applied to soybean production data from Paraná State for 2003-04 to 2009-10 crop-years

In the current study, we performed a soybean production spatial distribution analysis in Paraná State. Seven crop-year data, from 2003-04 to 2009-10, obtained from the Paraná Department of Agriculture and Supply (SEAB) were used to develop a Boxmap for each crop-year, show soybean production throughout this time interval. Moran's index was used to measure spatial autocorrelation among municipalities at an aggregate level, while LISA index local correlation. For each index, different contiguity matrix and order were used and there was a significance level study. As a result, we have showed spatial relationship among cities regarding the production, which allowed the indication of high and low production clusters. Finally, identifying main soybean-producing cities, what may provide supply chain members with information to strengthen the crop production in Paraná.