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Fires are commonly used in the Cerrado biome to renovate pastures and clean agricultural land. However, its use is also associated with deforestation and the implementation of agricultural activities. In this sense, the objective of this work is to provide the mapping and subsequent analysis of spatial patterns and temporal trends in the occurrence of fires in the Cerrado biome recorded between 1999 and 2018. For this purpose, the fire outbreaks for Fire Database (BDQueimadas/INPE) were counted according to the microregions defined by the IBGE and analyzed using spatial attributes, spatial autocorrelation statistics and the Emerging Hot Spot Analysis tool. The results showed concentration and high spatial association of fire outbreaks in micro-regions located near the states of Maranhão, Tocantins, Piauí and Bahia (MATOPIBA region), in addition to Mato Grosso (Deforestation Arch) and northern Goiás, regions notably recognized as frontiers of expansion of agricultural activities in the biome. [ABSTRACT FROM AUTHOR]

The objective of this study is to apply a composite index of social deprivation in the study of the urban social inequalities in Santa Maria, Rio Grande do Sul. It is a methodological exercise developed with a technique originally proposed by Carstairs and Morris (1991) and based on the selection of three variables related to urban social inequalities. The data were collected in the Brazilian Institute of Geography and Statistics (IBGE) census and modeled on the Geographic Information System (GIS). The analyzes were performed using techniques of exploratory analysis of spatial data, including the spatial autocorrelation of Moran I global and local. The results indicated three situations of spatial deprivation: a central area of low social deprivation index (SDI), a peripheral zone of high deprivation and an extensive area of transition. A fourth situation was observed in the eastern of the city, under the influence of the university use of the territory and represented by less social deprivation and the disruption of the center-periphery model. It is concluded that the IPS is an important indicator for the elaboration of diagnostic scenarios of the spatial inequalities and for the production of public policies adapted to the needs of the territory. [ABSTRACT FROM AUTHOR]

It is frequent to face estimation problems when dealing with mineral deposits involving multiple correlated variables. The resulting model is expected to reproduce data correlation. However, is not guaranteed that the correlation observed among data will be reproduced by the model, if the variables are estimated independently, and this correlation is not explicitly taken into account. The adequate geostatistical approach to address this estimation problem is co-kriging which requires cross and direct covariance modeling of all variables, satisfying the LMC. An alternative is to decorrelate the variables and estimate each independently, using for instance, the minimum/maximum autocorrelation factors (MAF) approach, which uses a linear transformation on the correlated variables, transforming them to a new uncorrelated set. The transformed data can be estimated through kriging. Afterwards, the estimates are back-transformed to the original data space. The methodology is illustrated in a case study where three correlated variables are estimated using the MAF method combined with kriging and through co-kriging, used as a benchmark. The results show less than a 2% deviation between both methodologies. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the quality fit of nonlinear models, gompertz and logistic data outside diameter, longitudinal (DEL) and transverse (DET) fruits of green dwarf coconut trees, as well as verification of the existence of residual autocorrelation. The results showed that for both, DEL and DET, the logistic model is most appropriate for describing the fruits growth. The fit of the models, for both variables, showed positive residual autocorrelation. For DEL, the residual was modeled by a first order autoregressive process, andfor DET, the autoregressive process was considered of second order. Consideration of the autoregressive process resulted in more accurate estimates of the parameters. According to the logistic model, the fruit of the dwarf green, fully developed, have external diameters, longitudinal and transverse, respectively, of 21.56cm and 15.35cm. [ABSTRACT FROM AUTHOR]

In competition experiments of citrus cultivations one generally uses many treatments, which requires the use of big blocks and plots with few plants. One has debated that in these conditions there can occur the correlation between neighboring plots, violating, thus, the presupposition of errors independent from the variance analysis. The present work has had as objective to evaluate different model parametrizations, considering or not the spatial dependence between plots, in two competition experiments of Pera orange tree clones (Citrus sinensis L. Osbeck). One has utilized the separable auto-regressive structure of first order (AR1 × AR1) as a model of spatial dependence between the errors. The results found indicate that the spatial modeling of the errors by utilizing separable auto-regressive models of first order for selection experiments of Pera orange tree clones normally bring small gains in terms of quality of adjustment. The analysis not considering the block factor plus the separable auto-regressive spatial adjustment of first order has presented better quality of adjustment between the models evaluated. [ABSTRACT FROM AUTHOR]

This study tests and compares two versions of the general equilibrium relationship for the prediction of expected returns: the static version of the CAPM and the conditional CAPM version that considers non-stationary estimates of the coefficients over a given period. The first concern was about the explanatory power of each model and compared the effects of economic variables, and the second was to examine the occurrence of structural changes, and which periods impacted the behavior of the coefficients. The analysis was conducted on daily data of indexes representing stocks traded on the Bovespa during the period from 1st December 2005 to October 31, 2008 (721 observations). To have a minimum number of observations and estimate consistently the parameters of the regression models and so that the Newey-West correction could correct the problem of inefficiency of the coefficients violated when the classical assumptions of absence of autocorrelation and heteroscedasticity waste in some cases, had to consider breaks that were statistically more significant through the control of the monitoring period. Thus, after applying the tests to verify hypotheses classical OLS, it was found that the model chosen was the conditional CAPM, showing small Akaike information criteria (AIC) and Schwarz (BIC) without the presence of structural break. When considering the presence of structural breaks in time series, there were changes in systemic risk and total over the period observed. From these results, it is recommended to investigate the possible events that led to disruption in the series, noting the factors that contributed to such interference. [ABSTRACT FROM AUTHOR]

The aim of the study was to evaluate the presence of temporal persistence, periodicals components, and trends within the ten-day rainfall series (PRE), observed during 1890-2009 at the weather station of Campinas, State of São Paulo, Brazil. Based on the autocorrelation function and on the Run test it was observed no significant temporal persistence within the data sample. The spectral analysis carried on the time-frequency domain has shown no remarkable periodicity associated with the variance peaks in the temporal signal under evaluation. Based on non parametric methods and considering the significance levels commonly adopted in the scientific literature (10 and 5%) it was detected no important climate trend within the PRE series. However, during the end of the dry season and the beginning of the rainy season it was observed an unexpected high number of decreasing (negative) trends cases that may be an indication of changes in the climatic conditions observed during 1890-2009. This last feature may reveal a delay in the resumption of the rainy season. [ABSTRACT FROM AUTHOR]