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【Objective】 To explore the geographical environment factors that may affect serum uric acid (UA) of healthy people and explore the change trend of UA reference value at the national scale. 【Methods】 The UA reference values of 607905 healthy people from 565 loci in China were collected, and the correlation between 25 geographical environment factors and UA reference values was analyzed by correlation analysis. CatBoost model was constructed and SHAP value interpretation model was applied to predict the UA reference values of healthy people in counties and cities in China, and the geographical distribution map of UA reference values of healthy people in China was drawn by using ordinary Kriging. 【Results】 A total of 20 indicators, namely, latitude, altitude, annual average temperature, annual average relative humidity, annual precipitation, air temperature annual range, annual average wind speed, percentage of surface soil silt, surface soil bulk density, surface soil gravel content, surface soil organic matter content, surface soil PH, surface soil (clay) cation exchange capacity, surface soil (silt) cation exchange capacity, surface soil base saturation, total surface soil exchange capacity, T-CaCO3, T-CaSO4, surface soil alkalinity, and surface soil salt showed their correlation with UA reference value of healthy people nationwide. The spatial distribution of UA reference values of healthy people across the country differed, manifested as the changing trend of higher in high altitude regions, higher in coastal regions than in inland regions, lower in the mid-eastern region, and higher in Southwest China at similar altitudes. 【Conclusion】 This study lays a foundation for further studies on the mechanism of different influencing factors on UA reference value. CatBoost model was established to provide the basis for establishing reference standards using UA reference values as prognostic factors for hyperuricemia and related chronic diseases in different regions.
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The usage of spatial tools might be helpful in the optimization of decision-making regarding soil management, with technologies that assist in the interpretation of information related to soil fertility. Therefore, the present study evaluated the spatial variability of chemical attributes of the soil under an agroforestry system compared to a native forest in the municipality of Tomé-açu, Eastern Amazon, Brazil. Soil samples were performed at 36 points arranged in a 55 x 55 m grid. The soils were prepared and submitted to analysis in order to determine pH in H2O, exchangeable calcium, magnesium, potassium and aluminium, available phosphorus, potential acidity, organic matter, bases saturation and aluminium saturation. For each soil attribute, the spherical, gaussian and exponential models were adjusted. After the semivariograms fitting, data interpolation for assessment of spatial variability of the variables was performed through ordinary kriging. The spherical and gaussian models were the most efficient models in estimation of soil attributes spatial variability, in most cases. Most of variables presented a regular spatial variability in their respective kriging maps, with some exceptions. In general, the kriging maps can be used, and we can take them as logistical maps for management and intervention practices in order to improve the soil fertility in the study areas. The results principal components indicate the need for integrated management of soil chemical attributes, with localized application of acidity correctors, fertilizers and other types of incomes, using the spatial variability of these fertility variables.
Subject(s)
Soil Chemistry , ForestryABSTRACT
El cultivo de cacao en el trópico peruano presenta bajos rendimientos, lo que exige buscar alternativas para aumentar la producción. Frente a esta situación, los estudios geoespaciales facilitan el diagnóstico de fertilidad y la aplicación de planes de fertilización. Por lo tanto, se evaluó la distribución espacial de indicadores fisicoquímicos y calidad del suelo en Padre Abad, región Ucayali, Perú. Se evaluaron indicadores fisicoquímicos y la calidad del suelo y se sometieron al análisis geoestadístico, a través del interpolador Kriging, encontrándose coeficientes de variación bajos para arcilla, pH y potasio K y medios para arena, limo, materia orgánica (MO), nitrógeno (N), fósforo (P), potasio (K+), calcio (Ca2+), magnesio (Mg2+), aluminio (Al3+), capacidad de intercambio de cationes (CIC), saturación de bases (SB), acidez cambiable (AC) y saturación de aluminio (SAl). La calidad del suelo varió entre baja a muy baja, con una distribución espacial de 52,24 y 47,76 %. El análisis de componentes principales encontró significancia para arena, limo, MO, N, K+, Al3+, CIC, %AC, %BC y %SAl, todos presentan variación espacial, según su nivel de fertilidad, excepto K+. Los modelos de interpolación con mejores ajustes fue el exponencial y lineal con dependencia espacial fuerte para arena, limo y K, moderada para MO, N, Al, CIC, %SAl e índice de calidad del suelo y débil para %BC y %AC, con una eficiencia de predicción confiable. Se encontró alta variabilidad espacial con valores medios de los diferentes indicadores de fertilidad bajos, no favorables para el desarrollo del cultivo de cacao.
Cocoa cultivation in the Peruvian tropics has low yields, which requires seeking alternatives to increase the production. Faced with this situation, geospatial studies facilitate the diagnosis of fertility and the efficient application of fertilization plans. Therefore, the spatial distribution of physicochemical indicators and soil quality in Padre Abad, Ucayali region, Peru were evaluated. Physicochemical indicators and soil quality were evaluated and subjected to geostatistical analysis through the Kriging interpolator, finding low coefficients of variation for clay, pH and potassium (K+), and medium for sand, silt, organic matter (OM), nitrogen (N), phosphorus (P), potassium (K+), calcium (Ca2+), magnesium (Mg2+), aluminum (Al3+), cation exchange capacity (CEC), base saturation (BS), exchangeable acid (EA) and aluminum saturation (AlS). Soil quality ranged from low to very low quality with a spatial distribution of 52.24 % and 47.76 % respectively. Principal component analysis found significance for sand, silt, OM, N, K+, Al3+, CEC, %EC, %BS and %AlS, all showing spatial variation according to their fertility level, except K+. The interpolation models with best fits were the exponential and linear with strong spatial dependence for sand, silt and K, moderate for MO, N, Al, CEC, %AlS and soil quality index (SQI), and weak for %BS and %EC, with reliable prediction efficiency. The research found high spatial variability with low mean values of the different fertility indicators, not favorable for cocoa crop development.
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The sustainable management of water and soil resources for agricultural purposes is related to the ability to store and mobilize available water for crops, particularly under a spatial analysis. The objective of the study was to design and evaluate a methodology for spatial analysis of resistance to soil penetration and infiltration on loamy-clay textures. The basic methodological principles included sampling grid planning, data capture at defined points, data fitting to empirical models, data processing, and spatial representation. A defining moment was evaluated for an established feijoa crop with permanent production. With a georeferenced rectangular sampling grid of 40m x 40m, an area of 1.36 ha was covered. Penetration resistance was measured with a penetrometer, covering 4 depths per node (sampled point). Infiltration was evaluated with ring infiltrometers. The results allowed validation of the methodology implemented through a single processing environment through RStudio. Resistance to penetration sensitively affected the variation in infiltration rates, adjusting planning activities for irrigation activities. The methodological proposal was designed to reduce processing times and graphic responses, tabulated, and integrated with a single script in the R tool, compared to traditional geostatistical techniques, which articulate the implementation of multiple tools for the generation of results.
La gestión sostenible de los recursos agua y suelo, con fines agrícolas, tiene relación con la capacidad para almacenar y movilizar agua disponible para los cultivos, particularmente, bajo un análisis espacial. El objetivo del estudio fue diseñar y evaluar una metodología de análisis espacial de la resistencia a la penetración e infiltración del suelo sobre texturas franco-arcillosas. Los principios básicos metodológicos incluyeron planificación de grilla de muestreo, captura de datos en puntos definidos, ajuste de datos a modelos empíricos, procesamiento y representación espacial de datos. Se evaluó un momento definido para un cultivo de feijoa establecido con producción permanente. Con una grilla de muestreo rectangular georreferenciada de 40m x 40m, se abarcó una superficie de 1,36 ha. La resistencia a penetración, se midió con un penetrómetro, cubriendo 4 profundidades por nodo (punto muestreado). La infiltración fue evaluada con anillos infiltrómetros. Los resultados permitieron validar la metodología implementada, mediante un entorno de procesamiento único, a través de RStudio. La resistencia a la penetración afectó sensiblemente la variación en las tasas de infiltración, ajustando actividades de planeación de actividades de riego. La propuesta metodológica fue diseñada para disminuir tiempos de procesamiento y respuestas gráficas, tabuladas e integradas en un único script en la herramienta R, comparado con técnicas tradicionales geoestadísticas, que articulan la implementación de múltiples herramientas para la generación de resultados.
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Objective To explore the temporal and spatial distribution characteristics of air pollutants PM2.5, PM10, SO2, CO, and NO2, and their effects on acute cerebrovascular diseases in Jining City. Methods The data of patients with acute cerebrovascular disease treated in a 3A hospital in Jining from October 1, 2017, to November 31, 2019, were retrospectively collected. Combined with the air pollution data of 29 air quality monitoring stations in Jining City, the Kriging interpolation model was used to analyze the overall situation of air pollution in Jining. On this basis, the relationship between air pollution and acute cerebrovascular diseases in Jining City was analyzed. Results In Jining City, the incidence of acute cerebrovascular disease in male was higher than that in female, and the incidence in rural areas was significantly higher than that in urban areas. The spatial distribution showed a trend of gradual accumulation from southeast to northwest. The daily average concentrations of PM2.5 and PM10 were higher in winter and spring than in summer and autumn. The results of Kriging interpolation analysis showed that the concentrations of these air pollutants formed aggregation points in varying degrees. The spatial distribution of acute cerebrovascular disease patients in Jining City was highly consistent with the spatial distribution of air pollutant concentrations. Spearman correlation analysis showed that CO, SO2, and NO2 were positively correlated with the incidence of acute cerebrovascular disease, while the correlation between PM2.5 and PM10 and the incidence of acute cerebrovascular disease was not significant. Conclusion Some air pollutants such as CO, SO2, and NO2 have a positive correlation with the incidence of acute cerebrovascular disease, and the prevalence has a certain population and regional distribution. In the future work of cerebrovascular disease prevention, personal protection should be done according to local conditions and living environment of specific people.
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【Objective】 This paper screened the factors that may influence the spatial differentiation of Neutrophil-to-lymphocyte ratio （NLR） reference values in healthy adults in China and explored the trend of NLR reference values in China. 【Methods】 For this research， we collected the NLR of 162 681 healthy adults from 62 cities in China. Spearman regression analysis was used to analyze the correlation between NLR and 25 geography secondary indexes. We extracted 9 indexes with significant correlation， built a random forest （RF） model， and predicted the country’s urban healthy adults’ NLR reference value. By using the disjunctive Kriging method， we obtained the geographical distribution of NLR reference value of healthy adults in China. 【Results】 The reference value of NLR of healthy adults in China was significantly correlated with the 9 secondary indexes， namely， altitude， sunshine duration， annual average temperature， annual average relative humidity， annual temperature range， annual average wind speed， content of organic matter in topsoil， cation exchange capacity in topsoil （clay）， and total amount of CaSO4 in soil. The geographical distribution of NLR values of healthy adults in China showed a trend of being higher in Southeast China and lower in Northwest China， higher in coastal areas and lower in inland areas. 【Conclusion】 This study lays a foundation for further research on the mechanism of different influencing factors on the reference value of NLR index. A random forest model composed of significant influencing factors has been established to provide the basis for formulating reference criteria for the prognostic factors of the novel coronavirus using NLR reference values in different regions.
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Resumen El aguacate (Persea americana) es una especie cuyo cultivo es de gran importancia nutricional y económica para México; sin embargo, como cualquier otro cultivo, a menudo se ve afectado por plagas y enfermedades que limitan su comercialización a nivel mundial. El hongo fitopatógeno Colletotrichum gloeosporioides es el agente causal de la antracnosis en el aguacate y se manifiesta en las etapas tempranas del desarrollo del fruto, así como en poscosecha y durante el almacenamiento, en condiciones de alta humedad relativa (80%) y temperaturas desde los 20 ◦C. Las pérdidas económicas a causa de este hongo pueden ser de hasta el 20% de la producción. En el presente estudio se aplicaron métodos geoestadísticos para definir la distribución espacial de antracnosis en frutos de aguacate cultivar Hass en cuatro municipios del Estado de México, durante el periodo de enero a junio de 2017. La distribución de la antracnosis se ajustó a modelos gaussianos y exponenciales en la mayoría de los casos. Los mapas de infestación realizados mediante krigeado muestran más de un centro de agregación de la enfermedad. Este análisis permitió estimar la superficie infestada: se encontró una infestación de más del 50% en los primeros muestreos y de hasta un 98% en los muestreos de junio en todas las zonas estudiadas. © 2019 Publicado por Elsevier Espana, S.L.U. en nombre de Asociacion Argentina de Microbiologıa. Este es un art´ıculo Open Access bajo la licencia CC BY-NC-ND (http://creativecommons. org/licenses/by-nc-nd/4.0/).
Abstract Persea americana is a species of great nutritional and economic importance for Mexico, however, like any other agricultural crop, it is affected by pests and diseases that limit its worldwide commercialization. The phytopathogenic fungus Colletotrichum gloeosporioides is the causative agent of anthracnose in avocado and manifests itself in the early stages of fruit development as well as in post-harvest and storage, under conditions of high relative humidity (80%) and at temperatures from 20°C, causing losses economic up to 20% of production. Applying geostatistical methods the present study aims to define the spatial distribution of anthracnose in Hass avocado fruits in four municipalities of the State of Mexico during the period from January to June 2017. The results show that the distribution of anthracnose was adjusted to gaussian and exponential models in most, the infestation maps made through the kriging show more than one centerof aggregation of the disease, based on it the infested surface was estimated, finding an infestation of more than 50% in the first samples and up to 98% in the samplings belonging to the month of June in all the areas studied. © 2019 Published by Elsevier Espana, S.L.U. on behalf of Asociación Argentina de Microbiología. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/ licenses/by-nc-nd/4.0/).
Subject(s)
Plant Diseases/microbiology , Colletotrichum/isolation & purification , Persea/microbiology , Geography , MexicoABSTRACT
Geostatistics is a tool that can be used to produce maps with the distribution of nutrients essential for the development of plants. Therefore, the present study aimed to analyze the spatial variation in chemical attributes of soils under oil palm cultivation in agroforestry systems in the eastern Brazilian Amazon, and their spatial dependence pattern. Sixty spatially standardized and georeferenced soil samples were collected at each of three sampling sites (DU1, DU2, and DU3) at 0-20 cm depth. Evaluated soil chemical attributes were pH, Al3+, H+Al, K+, Ca2+, Mg2+, cation exchange capacity (CEC), P, and organic matter (OM). The spatial dependence of these variables was evaluated with a semivariogram analysis, adjusting three theoretical models (spherical, exponential, and Gaussian). Following analysis for spatial dependence structure, ordinary kriging was used to estimate the value of each attribute at non-sampled sites. Spatial correlation among the attributes was tested using cokriging of data spatial distribution. All variables showed spatial dependence, with the exception of pH, in one sampling site (DU3). Highest K+, Ca2+, Mg2+, and OM levels were found in the lower region of two sampling sites (DU1 and DU2). Highest levels of Al3+ and H+Al levels were observed in the lower region of sampling site DU3. Some variables were correlated, therefore cokriging proved to be efficient in estimating primary variables as a function of secondary variables. The evaluated attributes showed spatial dependence and correlation, indicating that geostatistics may contribute to the effective management of agroforestry systems with oil palm in the Amazon region.
A geoestatística é uma ferramenta utilizada para produzir mapas de distribuição de nutrientes essenciais para o desenvolvimento das plantas. O presente estudo teve como objetivo analisar a variação espacial dos atributos químicos do solo sob cultivo de dendê em sistemas agroflorestais na Amazônia Oriental brasileira, e seu padrão de dependência espacial. Sessenta amostras de solo espacialmente padronizadas e georreferenciadas foram coletadas em cada um de três locais de amostragem (UD1, UD2 e UD3), na profundidade de 0-20 cm. Os atributos químicos do solo avaliados foram: pH, Al3+, H+Al, K+, Ca2+, Mg2+, capacidade de troca catiônica do solo (CTC), P e matéria orgânica (MO). A dependência espacial dos atributos foi avaliada com análise semivariográfica, ajustando-se três modelos teóricos (esférico, exponencial e gaussiano). Após a análise de dependência espacial, a krigagem ordinária foi empregada para estimar os valores de cada atributo em locais não amostrados. A correlação espacial entre os atributos foi testada utilizando a cokrigagem para espacialização dos dados. Todas as variáveis mostraram dependência espacial, exceto pH em UD3. Os maiores teores de K+, Ca2+, Mg2+ e MO foram encontrados na região mais baixa da paisagem, em UD1 e UD2. Os maiores teores de Al3+ e H+Al foram observados na região mais baixa da paisagem, em UD3. Algumas variáveis foram correlacionadas, portanto a cokrigagem mostrou-se eficiente na estimativa das variáveis primárias em função das secundárias. Os atributos avaliados mostraram dependência e correlação espacial, indicando que a geoestatística pode contribuir para o manejo efetivo de sistemas agroflorestais com dendê na região amazônica.
Subject(s)
Forestry , Spatial Analysis , Soil Characteristics/analysis , Elaeis guineensis , Data Interpretation, Statistical , Brazil , Amazonian EcosystemABSTRACT
El interés por introducir verdolaga (Portulaca oleracea L.) como planta cultivable se ha incrementado paulatinamente debido a sus propiedades medicinales. Sin embargo, estudios acerca de su distribución espacial y relación con el tipo de suelo son escasos. Los objetivos del presente trabajo fueron determinar la probabilidad de ocurrencia de verdolaga, así como la relación entre su distribución espacial y las características edáficas en la Región Lagunera. Se obtuvieron datos de presencia/ausencia de esta planta y muestras de suelo superficial de sitios en distintas clases de tierra, y se determinaron las características físicas y químicas de los suelos. Los datos obtenidos se utilizaron para estimar la probabilidad de ocurrencia de verdolaga con una regresión logística y se cartografiaron. Los resultados muestran que la arena, la conductividad eléctrica y el pH influyen en la presencia de la verdolaga y su distribución espacial es diferente, donde los suelos de tierras blancas arenosas tuvieron la mayor probabilidad de ocurrencia.
Subject(s)
Humans , Phytochemicals , Plants, Edible , Portulaca , Antioxidants , MexicoABSTRACT
ABSTRACT Litter corresponds to the layer of decomposing dead organic matter present on the soil surface. This layer is very important for nutrient cycling and contributes with organic matter accumulation in the soil, besides the carbon stock. The objective herein was to quantify the carbon biomass, both content and stock, and map the litter C-stock in the Cerrado biome, which is formed by Savanna Grassland (SG), Cerrado Stricto Sensu (CE) and Forest Savanna (FS), in Minas Gerais state, southeastern Brazil. The data were collected in 26 fragments in Minas Gerais state, totaling 210 sampling locations. A variographic study was conducted and, for mapping, the ordinary kriging method was used for delimitation of homogeneous zones. It was possible to detect high variability in the carbon biomass, carbon content and C-stock in the Cerrado biome litter in Minas Gerais state. The carbon content presented lower variability, ranging from 40 to 44%, so that it is not responsible for explaining the variability of the litter C-stock. Savanna Grassland and Savanna Forest present, respectively, the lowest and highest C-stocks. C-stock presented a considerable spatial structure dependence, allowing to use the geostatistical procedures for mapping it in the Cerrado biome of the Minas Gerais state. The C-stock kriging map showed good accuracy, allowing to verify that the lowest C-stocks in the litter are found from the center to the northern of the Minas Gerais since the highest air temperatures are also verified in this direction.
RESUMO A serrapilheira corresponde à camada de matéria morta em decomposição presente sobre o solo. Esta camada é de grande importância na ciclagem de nutrientes e aporte de matéria orgânica sobre o solo, além de estocar carbono. Objetivou-se quantificar a biomassa, teor e estoque de carbono e espacializar o estoque de C da serrapilheira do Cerrado de Minas Gerais. Os dados foram coletados em 26 fragmentos de Cerrado no Estado de MG, totalizando 335 pontos amostrados em todo estado. Foi realizado o estudo variográfico e, para o mapeamento, utilizou-se a Krigagem, para delimitação de zonas homogêneas. Foi possível detectar alta variabilidade nas características avaliadas, biomassa, teor e estoque de carbono na serrapilheira do Cerrado em Minas Gerais, Brasil. O teor de C foi a característica que apresentou menor variabilidade, com intervalo de 40-44%, de modo que não é um atributo crítico para explicar o estoque de C na serrapilheira. O Campo Cerrado tem o mais baixo estoque de C, e o Cerradão o mais alto. O estoque de C apresenta considerável dependência da estrutura espacial, permitindo o uso de procedimentos geoestatísticos para mapeá-lo no bioma Cerrado do estado de Minas Gerais. O mapa de krigagem de estoque de C mostrou boa precisão e com base nele, foi possível verificar que os estoques de menor teor de carbono na serrapulheira são encontrados do centro para o norte do estado de Minas Gerais, onde tem-se as maiores temperaturas médias anuais.
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RESUMO O objetivo deste trabalho foi analisar os dados de atributos geoquímicos a fim de verificar sua estacionaridade e correlacionar a normalidade estatística com o uso da técnica de krigagem ordinária. A escolha da krigagem ordinária como método geoestatístico aplicado ao trabalho deve-se ao fato de essa ser aconselhada para a realização de estudos em áreas onde existam dados com variáveis que possam apresentar dependência espacial, como é o caso das variáveis geoquímicas, e por ser indicada para dados que apresentam estacionaridade. A metodologia utilizada para a realização desta pesquisa envolveu, além da revisão de literatura, a obtenção de dados dos metais-traço (Cu, Zn, Mn, Fe, Cr e Pb) extraídos parcialmente de amostras superficiais (0 a 10 cm) de solos e sedimentos coletados em campo. Também foram determinados os valores de pH, salinidade, nitrogênio total, fósforo, matéria orgânica e granulometria. Foram conduzidas análises estatísticas, construções de semivariogramas, aplicação da krigagem ordinária e, por fim, validação cruzada para medir a incerteza da medição prévia dos dados. Neste trabalho, por meio dos variogramas, comprovou-se que, apesar de os dados não serem normais, eles apresentaram estacionaridade. Além disso, o parâmetro da estatística descritiva que mais possui correlação direta com a krigagem ordinária é a variância.
ABSTRACT The aim of this work was to analyze geochemical data in order to check their stationarity and to correlate the statistical normality using the ordinary kriging technique. The ordinary kriging technique was chosen as the geostatistical method applied to work because such technique is advised for studies in areas where there are data with variables that might present spatial dependence, like the geochemical variables, and also because it is indicated for data presenting stationarity. The methodology used for this research involved, besides literature review, data collection of trace metals (Cu, Zn, Mn, Fe, Cr and Pb) that were partially extracted from surface samples (0 to 10 cm) of soils and sediments collected in the field. We also determined the values of pH, salinity, total nitrogen, phosphorus, organic matter and particle size. Statistical analyzes, semivariogram development, ordinary kriging use and, lastly, cross validation were performed to measure the uncertainty of the previous measurement of data. It was found, in this work, by means of the variograms that although data were ordinary, they showed stationarity. In addition, the parameter of descriptive statistics that mostly correlates directly with the ordinary kriging is variance.
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ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.
RESUMO Modelos de terreno de rios são usados para análise de mudanças geomorfológicas e para simulações hidrológicas. Estes modelos são interpolados a partir de pontos batimétricos. A batimetria fluvial é geralmente conduzida através de seções transversais, o que pode acarretar em uma malha amostral esparsa. Métodos híbridos de krigagem, como krigagem por regressão (KR) e co-krigagem (CK), empregam a correlação com preditores auxiliares, além da auto-correlação entre variáveis, na predição da variável resposta. Neste estudo, sugere-se que a distância ortogonal de um ponto até a linha de centro do talvegue de um rio pode ser usada como covariável para KR e CK. Considerando-se que a variabilidade da cota do leito do rio é abrupta transversalmente a direção do fluxo, espera-se que quanto maior a distância euclidiana de um ponto até o talvegue, maior será sua elevação. O objetivo deste estudo foi avaliar o uso da covariável proposta em métodos híbridos de krigagem para a predição espacial da topografia do leito de rios. Para tanto, foi realizada uma validação externa, em que seções transversais foram usadas para interpolação e dados levantados entre as seções consistiram na amostra de teste. Os resultados da KR e CK foram comparados aos da krigagem ordinária. A KR apresentou a menor REQM. No mapa resultante da KR, o talvegue foi preservado nas lacunas não amostradas entre as seções, enquanto os demais métodos subestimaram a profundidade do talvegue nestes espaços. Assim, conclui-se que a KR pode melhorar a predição espacial de dados batimétricos fluviais.
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El uso de técnicas geoestadísticas permite conocer la variabilidad de las propiedades de los suelos y facilita la interpretación, la predicción y la toma de decisiones. Con el fin de establecer el impacto del manejo que se le ha dado a la granja Tunguavita, se realizó un transecto, en el cual, se tomaron 85 puntos de muestreo, que se encontraban en un zona de bosque, dedicada a pastos y a explotación equina. Se determinaron las relaciones de masa-volumen, las humedades en profundidad y las resistencias a la penetración a dos profundidades. La aireación fue la relación de masa- volumen que presentó el mayor coeficiente de variación, lo que implica un manejo diferente a lo largo del transecto evaluado. La densidad real y la porosidad tienen una baja dispersión en el espacio. Las densidades aparente y real, la porosidad, la aireación y la relación de vacíos tuvieron un rango superior a 1000m. Los muestreos para la resistencia a la penetración y las humedades medidas con TDR, se deberían hacer cada 600m. La resistencia a la penetración mostró ser inversamente proporcional al contenido de humedad en el suelo. Las variables medidas se correlacionan en dos grupos: el primero, asocia a las densidades con las humedades medidas a diferentes profundidades y, el segundo grupo, relaciona la resistencia a la penetración a 15cm con la medida a 30cm. La resistencia a la penetración es un buen indicador de la compactación del suelo y del manejo agropecuario que se da en la granja Tunguavita.
The use ofgeostatistical techniques allows to find the variability of soil properties and also facilitates interpretation, prediction and decision making. Therefore, in order to establish the impact of management that has been given to the farm Tunguavita, a transect was conducted in which 85 sample points were taken, located found in a wooded area, an area devoted to pasture, and an area of equine exploitation. Volume mass ratios, humidities in depth and resistance to penetration at two depths was determined. Aeration was the mass-volume ratio that had the highest variation coefficient, which implies the different management practices along the evaluated transect. The particle density and porosity have a low dispersion in space. Bulk and particle densities, porosity, aeration and voids ratio presented a range higher than 1000m. Therefore, sampling for penetration resistance and moisture with TDR measurements should be done each 600m. Resistance to penetration showed to be inversely proportional to the moisture content in the soil. The measured variables are correlated in two groups, the first one associated densities with the humidities, measured at different depths, and the second group related to penetration resistance at 15cm with measurement at 30cm. The penetration resistance is a good indicator of soil compaction and the agricultural management that occurs in the Tunguavita farm.
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Objective:To explore the spatial distribution and clustering in birth defects from 2010 to 2013 in Shaanxi Province.Methods:Spatial distribution was used to describe the birth defects,while ordinary Kriging method was used to predict the status of birth defects in Shaanxi province.The spatial characteristics for the birth defects at the county/district level were analyzed by spatial autocorrelation.Results:The overall incidence of birth defects was 219.196/10 000;Birth defect did not appear to be a random distribution but show a significant spatial aggregation.Spatial interpolation predicted the geographic distribution for occurrence of birth defects in Shaanxi Province.Local autocorrelation analysis showed nine hot spot areas for birth defects,such as Qian County,Liquan County,Yongshou County,Bin County,Fufeng County,Jingyang County,Chunhua County,Wugong County and Xingping City,and seven cold spot areas including Jia County,Yuyang District,Mizhi County,Suide County,Wubu County,Qingjian County and Zizhou District.Conclusion:There are spatial clustering in birth defects from 2010 to 2013 in Shaanxi Province.Spatial interpolation and spatial autocorrelation can be used to predict the spatial features of birthdefects in the whole province and provide evidence for the further intervention.
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Objective To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources.Methods The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected.Downscaling interpolation was conducted by using area-to-area Poisson Kriging method.The interpolation results were visualized by using geographic information system (GIS).The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township.Results In the downscaling interpolation,the range of the fitting semi-variance equation was 20.38 km.Within the range,the incidence had correlation with each other.The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y=1.053 1x,R2=0.99.The incidences at different scale were consistent.Conclusions The incidence of HFMD had spatial autocorrelation within 20.38 km.When HFMD occurs in one place,it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km.Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.
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Objective To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources.Methods The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected.Downscaling interpolation was conducted by using area-to-area Poisson Kriging method.The interpolation results were visualized by using geographic information system (GIS).The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township.Results In the downscaling interpolation,the range of the fitting semi-variance equation was 20.38 km.Within the range,the incidence had correlation with each other.The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y=1.053 1x,R2=0.99.The incidences at different scale were consistent.Conclusions The incidence of HFMD had spatial autocorrelation within 20.38 km.When HFMD occurs in one place,it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km.Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.
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The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.
A distribuição espacial da biomassa na Amazônia é heterogênea, variando temporalmente e espacialmente em relação aos diferentes tipos de formações vegetais abrangidas por este bioma. Estimativas de biomassa nesta região variam significativamente dependendo da abordagem aplicada e do conjunto de dados utilizados para sua modelagem. Assim, este estudo teve como objetivo avaliar três diferentes técnicas geoestatísticas na estimativa da distribuição espacial da biomassa acima do solo (BAS). As técnicas escolhidas foram: 1) regressão por mínimos quadrados ordinários (OLS), 2) regressão geograficamente ponderada (RGP) e, 3) regressão geograficamente ponderada - krigagem (RGP-K). Estas técnicas foram aplicadas sobre um mesmo conjunto de dados de campo, utilizando as mesmas variáveis ambientais decorrentes de dados cartográficos e de sensoriamento remoto de alta resolução espacial (RapidEye). Este trabalho foi desenvolvido na floresta amazônica da província de Sucumbíos no Equador. Os resultados deste estudo mostraram que a RGP-K, sendo uma técnica híbrida, forneceu estimativas estatisticamente satisfatórias com menor erro de predição em comparação com as outras duas técnicas. Além disso, observou-se que 75% da BAS foi explicada pela combinação de dados de sensoriamento remoto e variáveis ambientais, sendo os tipos de formações vegetais a variável de maior importância para estimar BAS. Cabe ressaltar que, embora o uso de imagens de alta resolução espacial melhora significativamente a estimativa da distribuição espacial da BAS, o processamento desta informação requer alta demanda computacional.
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Biomass , Soil Characteristics , Amazonian Ecosystem , Regression Analysis , Remote Sensing TechnologyABSTRACT
Objective To understand the distribution of the severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang province,and predict the incidence and the probability of SFTS outbreak.Methods Based on the cases of SFTS from 2011-2015,software ArcGIS 10.0 was used to analyze the spatial distribution,Moran's I and Getis-Ord Gi were used to analyze the spatial autocorrelation.The incidence trend was explored by trend surface analysis,and the prediction was made by Kriging interpolation.Results The incidence of SFTS increased and the distribution expanded in Zhejiang from 2011 to 2015,the seasonal and the demographic characteristics of SFTS were similar to the previous research;there were regional clustering of the cases (P＜0.001);a downward trend was observed from northeast to southwest in terms of incidence of SFTS;the second-order disjunctive Kriging interpolation based on circular model and the indicator Kriging interpolation based on exponential model had higher prediction accuracy,the probabilities of outbreak in Anji,Daishan and Tiantai were high,the prediction deviation of inland was less than that of edge area.Conclusion The prediction of SFTS by Kriging interpolation had high accuracy,the incidence of SFTS was higher and the distribution of SFTS was larger than the results of surveillance,the risk areas for epidemic were Anji,Daishan,Ninghai,Tiantai,Sanmen and Linhai.
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Objective To understand the distribution of the severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang province,and predict the incidence and the probability of SFTS outbreak.Methods Based on the cases of SFTS from 2011-2015,software ArcGIS 10.0 was used to analyze the spatial distribution,Moran's I and Getis-Ord Gi were used to analyze the spatial autocorrelation.The incidence trend was explored by trend surface analysis,and the prediction was made by Kriging interpolation.Results The incidence of SFTS increased and the distribution expanded in Zhejiang from 2011 to 2015,the seasonal and the demographic characteristics of SFTS were similar to the previous research;there were regional clustering of the cases (P＜0.001);a downward trend was observed from northeast to southwest in terms of incidence of SFTS;the second-order disjunctive Kriging interpolation based on circular model and the indicator Kriging interpolation based on exponential model had higher prediction accuracy,the probabilities of outbreak in Anji,Daishan and Tiantai were high,the prediction deviation of inland was less than that of edge area.Conclusion The prediction of SFTS by Kriging interpolation had high accuracy,the incidence of SFTS was higher and the distribution of SFTS was larger than the results of surveillance,the risk areas for epidemic were Anji,Daishan,Ninghai,Tiantai,Sanmen and Linhai.
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OBJECTIVES: Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 mum in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. METHODS: We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed. RESULTS: Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 mug/m3 (standard deviation=2.40 and 9.51 mug/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models. CONCLUSIONS: In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics.