Showing 2 results for کریگیدن
Nasrin Mahdianfard, Mohsen Mohammadzadeh,
Volume 17, Issue 40 (9-2015)
Abstract
Linking between geographic information systems and decision making approach own the invention and development of spatial data melding methods. Data melding methods combine the data, to achieve a better result and their aim is, to detect the information available in the data set in order to enhance the ability of interpreting data and increase the accuracy of the data analysis. In this paper, Bayesian melding method has been studied for combination of measurements, outputs of deterministic models and kriging methods. By spatial Bayesian melding and kriging an attempted is made to spatial prediction of ozone data in Tehran and results are validated and compared using the mean square error criterion.
Volume 18, Issue 51 (5-2005)
Abstract
Sometimes observations in statistical analysis are not independent and typically are correlated with their spatial locations. In spatial statistics there are some methods to interpolate this kind of data. In this paper some of interpolators for spatial data will be studied. Then they will be evaluated and compared by means of mean square errors criteria. Finally it will be shown that kriging acts better than other methods such as spline, inverse distance, for interpolation of spatial data.
