We review recent developments of sequential data assimilation techniques used in oceanography to integrate spatio-temporal observations into numerical models describing physical and ecological dynamics. Theoretical aspects from the simple case of linear dynamics to the general case of nonlinear dynamics are described from a geostatistical point-of-view. Current methods derived from the Kalman f...

Detailed knowledge of forest structure is an important component in research focusing on forest biodiversity monitoring, carbon budgeting studies, fire modelling and forest inventory estimation. In forest inventory mapping, high spatial resolution multispectral imagery are becoming a valuable and often a critical tool to effective forest resource management planning. In this research, we descri...

Integrated transport, land-use and air quality monitoring are prioritized especially in metropolitan areas. The complexity is straight forward, since data, indicators, variables, methods and approaches vary and isolated. The Spatial Information Sciences (SIS) provides mature solutions for data and policy integration, since the nature of problem is specific to geo-spatial distribution. The inter...

Transforming complex multivariate geological data to be multivariate Gaussian is an important and challenging problem in geostatistics. A variety of transforms are available to accomplish this goal, but may struggle with data sets of high dimensional and sample sizes. Projection Pursuit Density Estimation (PPDE) is a well-established non-parametric method for estimating the joint PDF of multiva...

Spatial data, either areal or geostatistical (point-referenced), are becoming increasingly utilized in the study of many scientific fields due to the accessibility of data monitoring systems and associated datasets. When both types of data are available for the same underlying spatial process, computationally efficient and statistically sound methods are needed for their joint analysis. Markov ...

The IsoMAP uses the multiple linear regression and geostatistical methods to analyze isotope data. Suppose the response variable Y (s) and the vector of independent variable x(s) are observed at a finite number of sites s1, · · · , sn. The multiple linear regression method assumes Y (s) and Y (s′) are independent but the geostatistical method assume they are not. In the following, we will first...

Background: Cardiac output (CO) is the total volume of blood pumped by the heart per minute and is a function of heart rate and stroke volume. CO is one of the most important parameters for monitoring cardiac function, estimating global oxygen delivery and understanding the causes of high blood pressure. Hence, measuring CO has always been a matter of interest to researchers and clinicians. Sev...

This study estimated hepatitis A risk areas in a region of Duque de Caxias, Rio de Janeiro State, Brazil. A cross-sectional study consisting of a hepatitis A serological survey and a household survey were conducted in 19 census tracts. Of these, 11 tracts were selected and 1,298 children from one to ten years of age were included in the study. Geostatistical techniques allowed modeling the spat...