Estimation Multivariate data points in spatial statistics with application

Abstract

This paper  deals  to how to estimate points non measured spatial data when the number of its terms (sample spatial) a few, that are not preferred for the estimation process, because we also know that whenever if the data is large, the estimation results of the points non measured to be better and thus the variance estimate less, so the idea of this paper is how to take advantage of the data other secondary (auxiliary), which have a strong correlation with the primary data (basic) to be estimated single points of non-measured, as well as measuring the variance estimate, has been the use of technique Co-kriging in this field to build predictions spatial estimation process, and then we applied this idea to real data in the cultivation of wheat crop in Iraq, where he was be considered the amount of production is the basic data (variable primary) and want to estimate a single points of non measured and the cultivated area (variable secondary) has been programming all calculations language Matlab