Financial investment with collection and laboratory analysis of soil samples is an important factor to be considered when mapping agricultural areas with soybean planting. One of the alternatives is to use the spatial autocorrelation between the sample points to reduce the number of elements sampled, thus restricting the collection of redundant information. This work aimed to reduce the sample size of this agricultural area, composed of 102 sample points, and use it to analyze the spatial dependence of soil macro- and micro- nutrients, as well as the soil penetration resistance. The agricultural area used in this study has 167.35 ha, cultivated with soybean, which the soil is Red Dystroferric Latosol, and the sampling design has used in this agricultural area is the lattice plus close pairs. The reduction of the sample size was made by the multivariate effective sample size (ESSmulti) methodology. The studies with the simulation data and the soil attributes showed an inverse relationship between the practical range and the estimated value of the univariate effective sample size. With the calculation of ESSmulti, the sample configuration was reduced to 53 points. The Overall Accuracy and Tau concordance index showed differences between the thematic maps elaborated with the original and reduced sampling designs. However, the analysis of the variance inflation factor and the standard error of the spatial dependence parameters showed efficient results with the resized sample size.

A circunferência de cintura é há muito tempo o índice padrão para avaliar a gordura abdominal na identificação de pacientes com síndrome metabólica. No entanto, observa-se que uma proporção de doentes com doenças cardiovasculares exibe essa circunferência normal. Assim, o objetivo deste estudo foi avaliar se a circunferência de pescoço pode ser utilizada como marcador preditivo para Síndrome Metabólica e risco cardiovascular. Participaram deste estudo transversal, 42 indivíduos submetidos à avaliação socioeconômica e antropométrica. A circunferência do pescoço correlacionou-se positivamente com a circunferência de cintura, glicemia de jejum, pressão arterial sistólica e diastólica e triglicerídeos. A curva Receiver Operating Characteristic exibiu ponto de corte de 40 cm com alto risco cardiovascular acima deste valor. Os resultados demonstram que a circunferência de pescoço além de rápida e não invasiva pode ser útil na previsão de Síndrome Metabólica e risco cardiovascular.

Aim of study: To reduce the sample size in an agricultural area of 167.35 hectares, cultivated with soybean, to analyze the spatial dependence of soil penetration resistance (SPR) with outliers. Area of study: Cascavel, Brazil Material and methods: The reduction of sample size was made by the univariate effective sample size ( ) methodology, assuming that the t-Student model represents the probability distribution of SPR. Main results: The radius and the intensity of spatial dependence have an inverse relationship with the estimated value of the . For the depths of SPR with spatial dependence, the highest estimated value of the  reduced the sample size by 40%. From the new sample size, the sampling redesign was performed. The accuracy indexes showed differences between the thematic maps with the original and reduced sampling designs. However, the lowest values of the standard error in the parameters of the spatial dependence structure evidenced that the new sampling design was appropriate. Besides, models of semivariance function were efficiently estimated, which allowed identifying the existence of spatial dependence in all depth of SPR.Research highlights: The sample size was reduced by 40%, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in the next mappings in the agricultural area. The spatial t-Student model was able to reduce the influence of outliers in the spatial dependence structure.

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In agricultural soils with low cation exchange capacity, it is essential to analyze the bivariate spatial correlation of soybean productivity and organic matter with the soil chemical attributes. Using bivariate spatial correlation makes it possible to identify patterns and behaviors that suggest a spatial association between two soil attributes, thus enabling better soil management and more efficient use of resources. The main objective of this study was to analyze bivariate spatial correlation considering variables with different spatial dependence structures. The bivariate Lee index was also calculated for this purpose. To model and describe the spatial pattern of two spatially correlated variables, the Bivariate Gaussian Common Component Model was used. In addition to calculating the bivariate spatial correlation of soil chemical attributes with soybean productivity and organic matter, the Lee index was also calculated for pairs of simulated variables with different weight matrices and geographic distance functions. It was observed that the greater the common practical range, the higher the Lee index value, indicating a higher bivariate spatial correlation. Furthermore, shorter distances between neighboring point pairs caused higher Lee index values. The distance function to calculate the distance between the point pairs was more relevant than the weight matrix in estimating the spatial dependence radius and the Lee index value. Soybean productivity showed a direct spatial correlation with the sum of bases, as well as with the calcium and magnesium contents. Organic matter had a direct spatial correlation with the sum of bases and an inverse one with the phosphorus content

considering the high rates of traffic accidents, care provided to victims must be fast and efficient. The objective was to analyze the density of traffic accidents and the profile of the victims assisted by the 4th Fire Department in Cascavel. It was also intended to analyze traffic signs and a place to build a new Fire Station. Geographic Information System and geoprocessing resources were used to identify the traffic accidents hotspots applying kernel density estimation. The victims' profile and their association with the severity of the injuries were obtained based on the chi-square statistic and the correspondence analysis. Field data were investigated to verify traffic signs at accident locations that involved victims with severity of more serious injuries. Most accidents occurred in the afternoon, involving men between 18 and 30 years old. Running over was the occurrence that most stood out considering the severity of the victims, with locations without any traffic signs. Moreover, a plot that meets all the requested criteria and is located in a region of high occurences of traffic accidents was found to build the new station.

Aim of study: To evaluate the influence of the parameters of the geostatistical model and the initial sample configuration used in the optimization process; and to propose and evaluate the resizing of a sample configuration, reducing its sample size, for simulated data and for the study of the spatial variability of soil chemical attributes under a non-stationary with drift process from a commercial soybean cultivation area. Area of study: Cascavel, Brazil Material and methods: For both, the simulated data and the soil chemical attributes, the Genetic Algorithm was used for sample resizing, maximizing the overall accuracy measure. Main results: The results obtained from the simulated data showed that the practical range did not influence in a relevant way the optimization process. Moreover, the local variations, such as variance or sampling errors (nugget effect), had a direct relationship with the reduction of the sample size, mainly for the smaller nugget effect. For the soil chemical attributes, the Genetic Algorithm was efficient in resizing the sampling configuration, since it generated sampling configurations with 30 to 35 points, corresponding to 29.41% to 34.31% of the initial configuration, respectively. In addition, comparing the optimized and initial configurations, similarities were obtained regarding spatial dependence structure and characterization of spatial variability of soil chemical attributes in the study area. Research highlights: The optimization process showed that it is possible to reduce the sample size, allowing for lesser financial investments with data collection and laboratory analysis of soil samples in future experiments.

Aim of study: In precision agriculture, the definition of Application Zones (AZs) in agricultural areas consists in delimiting the area in subareas with similar characteristics, using soil chemical attributes. To such end, the use of clustering methods is common. Therefore, the AZs make up a database that can be used to target future soil sampling, thus seeking a possible sample reduction. The objective of this paper is to assess the acquisition of sample configurations, with reduced sample size, contained in application zones generated by spatial multivariate clustering. The sampling protocol proposed in this work evaluated five clustering methods (C-means, Fanny, K-means, Mcquitty, and Ward) for the creation of AZs, and, through these AZs, to obtain reduced sample configurations with 50% and 75% of the initial sampling points. Area of study: Commercial agricultural area, Cascavel, Brazil. Material and methods: Data of the soil chemical attributes from a commercial agricultural area were used, referring to three soybean harvest years (2013-2014; 2014-2015; and 2015-2016). The clustering methods considered a dissimilarity matrix that aggregates the information about the Euclidean distance between the sample elements and the spatial dependence structure of the attributes. Main results: The results indicated division of the agricultural area into two or three AZs for the aforementioned harvest years, considering the K-means method. Comparing all the reduced sample configurations with the initial one, it was observed that the one proportionally reduced by 25% was the most effective to obtain a reduced sample configuration. Research highlights: The sampling protocol using AZs showed that it is possible to reduce the sample size.