Statistical map analysis of the mean and the gini coefficient of healthcare expenses in Iran

In many societies fairness and equality are the most significant concepts among the governments and peoples. In the health systems, the fast increase in expenses takes expert’s attention to measure and control the inequality. The aim of this paper is to investigate the inequality of household’s health expense’s in the Iran and making its statistical map. The health expenses data for doing this research have been gotten from the statistical center of Iran (SCI). The process of preparing data and estimating the statistical indexes have been done using the S-PLUS software. Spatial analysis and predictions have been done by geoR. For making statistical maps we have used Arcgis9.2 software. We conclude that the mean of costs and its gini coefficient increases, from east to the west of Iran. The Gini coefficient of household’s health expenses differs from 0.64 to 0.84, which show the existence of high inequality in this type of expenses. For eastern and north-western areas the least Gini coefficient had been predicted. In Sistan and Baluchestan and Hormozgan provinces the most inequality are predicted. Governments should try by financial aids decrease the inequality and receive fairness in health systems

Comparison of Kriging and artificial neural network models for the prediction of spatial data

The prediction of a spatial variable is of particular importance when analyzing spatial data. The main objective of this study is to evaluate and compare the performance of several prediction-based methods in spatial prediction through a simulation study. The studied methods include ordinary Kriging (OK), along with several neural network methods including Multi-Layer Perceptron network (MLP), Ensemble Neural Networks (ENN), and Radial Basis Function (RBF) network. We simulated several spatial datasets with three different scenarios due to changes in data stationarity and isotropy. The performance of methods was evaluated using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Concordance Correlation Coefficient (CCC) indexes. Although the results of the simulation study revealed that the performance of the neural network in spatial prediction is weaker than the Kriging method, but it can still be a good competitor for Kriging

Probability of SDS Days Prediction in Iran’s Eastern Region Using Spatio-Temporal Indicator Kriging model

One of the most important environmental challenges in the Middle East and Iran in recent years is the increasing SDS phenomenon. In order to forecast the probability of SDS days, wind speed and Horizontal view data in the eastern regions of Iran was investigated using Kriging model of Spatial-Temporal indicator,and R software, in which indicators one and zero were considered for a SDS and for a day without SDS, respectively. Then the SP Data array (Spatial Temporal Data) was constructed with a combination of the matrix and vector in the STFDF class (Spatial Temporal Function Data Frame), and STF class (Spatial Temporal Function). After fitting all the separable and non-separable models, the sum metric variogram with the least average of sum of squares was selected as the best model for fitting data. The output of the model showed that the data enjoy a spatial-temporal dependence to 5 days, so from the last day of the statistical period we can forecast the probability of occurrence of the SDS day for the next 5 days. On the first forecast able day, i.e. 2017/04/01, the critical points of Sarakhs and Fariman stations in Razavi Khorasan province with a probability of 16 and 20 percent, respectively, Zabol, Zahak, Mirjawa, Nosrat Abad, Zahedan and Khash stations in Sistan and Baluchestan provice with 17, 13.13, 19.24 and 17 percent, respectively, and finally Abarkuh, Bafgh and Behabad stations in Yazd province with 20, 16 and 35 percent, respectively, enjoyed the highest probability of occurrence of SDS days. Keywords:Spatial-Temporal Variogram, Predict, SDS Days, Eastern Rregions of Iran, Kriging Indicator, Sand and Dust Storm(SDS), Region, Indicator Kriging Method, SDS