Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two f...

Cation exchange capacity (CEC) is one of the most important soil attributes which control some basic properties of soil such as acidity, water and nutrient retaining capacity. However, the measurement of cation exchange capacity in large areas is time consuming and requires high expenses. One way to save time and expenses is to use simple soil covariates and geostatistical methods in mapping CE...

As basic data, the reliability of precipitation data makes a significant impact on many results of environmental applications. In order to obtain spatially distributed precipitation data, measured points are interpolated. There are many spatial interpolation schemes, but none of them can perform best in all cases. So criteria of precision evaluation are established. This study aims to find an o...

Precipitation is important factor affecting vegetation and controlling key ecological processes. In order to quantify spatial patterns of precipitation in Chongqing tobacco planting region, China, under ArcGIS platform, three multivariate geostatistical methods including cokriging, small grid and regression kriging, coupled with auxiliary topographic factors extracted from a 1:100000 DEM were a...

1. Abstract This paper presents a methodology that combines logistic regression with kriging for incorporating exhaustive secondary information into the mapping of the risk of occurrence of unexploded ordnance (UXO). Logistic regression, which is appropriate for binary data (indicators) analysis, is used to derive the trend component in simple kriging with varying local means (SKlm). The techni...

Environmental data is nearly always multivariate and often spatial–temporal. Thus to interpolate the data in space or to predict in space–time it is necessary to use a multivariate spatial–temporal method. Cokriging is easily extended to spatial–temporal data if there are valid space–time variograms or covariance functions. Various authors have proposed such models. In this paper, a generalized...

Accounting for secondary exhaustive variables (such as elevation) in modelling the spatial distribution of precipitation can improve their estimate accuracy. However, elevation and data are associated with different support sizes it is necessary to define methods combine such data. The paper was aimed compare block ordinary cokriging kriging an external drift estimating annual using covariate. ...

Population information is typically available for analysis in aggregate socioeconomic reporting zones, such as census blocks in the United States and enumeration districts in the United Kingdom. However, such data mask underlying individual population distributions and may be incompatible with other information sources (e.g. school districts, transportation analysis zones, metropolitan statisti...