The occurrence of skin cancer is related to many parameters which are spatially distributed. GIS can be used for analysing such spatial relationships. On the other hand, the occurrence of skin cancer is a probabilistic phenomenon. In other words, even in presence of all causing factors, skin cancer does not necessarily occur. In this research, a geostatistical model is developed to simulate the spatial distribution of skin cancer. First, the data framework is designed and proper data are gathered. Then, the data are processed in GIS and prepared to enter the model. Finally, by creation of the geostatistical model, the relations between the disease and its factors are studied. As a result, the effecting factors and their importance degrees are extracted. The developed model can be used both as simulator tool and as a decision support tool. In the other words, by changing the values of one or more parameters, the probability of occurring changes in the occurrence of disease can be studied. This can help the managers in evaluating the result of any possible course of action, when challenging the disease.

Background and Purpose: Development in sciences are influenced by the wys of comfort in living today. But, the development has provided high problems such as environmental pollution. Heavy metals are the most important elements that contaminated environment and are poisonous for human as well. These elements such as Pb, Zn, Cr, Cd, Co and Cu are produced by industrial activities. Method: This paper attempts to apply geochemical methods such as statistical and geostatistical method for evaluation of distribution heavy metals in soils. Thermal power plant of Shazand in the close of Mohajaran of Arak city is the center of elemental pollution accompanied with refinery and petrochemical in this area. Findings: This study is determined that among the elements such as Pb, Zn, Cr, Cd, Co and Cu, only Cd is contaminants element. Discussion and Conclusion: The amount of Cd is high in fuel raw of Iran and Shazand power plant uses this fuel. Therefore, Cd has increased in the extruded smok and has transferred by wind and has deposited in the soil.

Resource estimation and determining the grade distribution is one of the most important stages in planning and designing the open-pit and underground mines. In this work, a new mythology is used for resource estimation of the Angouran underground mine based on the optimized integration of the indicator kriging (IK), simple kriging (SK), and inverse distance weighted (IDW) methods. For this purpose, waste blocks are first removed from the block model using the IK method. Then the amount of mineral resource is estimated using the SK and IDW methods. Indeed, variograms are developed to estimate the grade of zinc minerals in the three used methods. Variograms analysis in three directions prove that the studied resource is anisotropic. Also the validation results confirm that the correlation coefficients between the measured and estimated zinc values by the SK and IDW methods equal to 0.76 and 0.75, respectively. Knowing this satisfactory result, a 3D model of the resource is prepared using the IK method, in which the ore and waste sections of the Angouran underground mine are separated definitely. According to the above methodology, the calculated resource of the Angouran underground mine using the SK method is achieved 1373962.5 tons with an average grade of 30.11%, whereas the estimated amount of this resource is attained 1349325 tons with an average grade of 31.88% using the IDW approach. The verification results show that the suggested methodology based on the optimized integration of the IK, SK, and IDW methods can be successfully applied for resource modeling and grade estimating of the Angouran underground mine.

Understanding the spatio-temporal distribution of precipitation is an important factor for planning and implementation of soil and water conservation, flood control and drought mitigation projects. Precipitation zoning maps and their variability play an important role in land management activities. The objective of the current research was to evaluate the capability of geo-statistical methods for estimating annual precipitation in the province of Fars. Data of 92 gauging stations with a common period of 20 years were collected and different approaches including ordinary kriging (OK), simple kriging (SK), and deterministic methods such as inverse distance weights (IDW), radial basis function (RBF), local polynomial interpolation (LPI) and global polynomial interpolation (GPI) were assessed based on R, MAE and RMSE. The results showed that the ordinary kriging had higher R and lower RMSE and MAE. Therefore, this method was implemented in the preparation of the isotheral map of the province of Fars. The results indicate advantages of the kriging method over those of the deterministic techniques. Moreover, the results revealed that 45.465% of the province has the annual precipitation range of 200-300 m while only 0.09 % of the province has the highest precipitation range of 900-1000 mm.

Remotely sensed data are able to monitor some characteristics of the environment and also their spatial structure. The latter one is the first and main step in field data interpolations. Therefore, spatial analysis of some field data is possible by employing of related satellite data bands. In this study, as an example, Landsat ETM+ thermal band (band 6) was acquired to determine the spatial structure of surface temperature distribution. For the purpose of evaluation and selection of the best interpolation model, the band 6 data was crossed with a regular sampling grid and therefore, a dataset was constructed. Using geo-statistical analysis, empirical semi-variogram was calculated and various mathematical models were fitted to its points (e.g. gaussian, exponential, circular and spherical). Afterwards, the fitted models were applied to generate different temperature distribution maps using kriging interpolation approach. Finally, the optimum model which could better predict temperature changes and distribution was recognized. Result of the study shows that the exponential model would be better to predict and estimate surface temperature in un-sampled locations in the area of studied. So, the model can be used for interpolation of field temperature data with a high confidence level. The represented method can be developed for all the other environmental factors which could better characterized by remotely sensed data, like minimum and maximum temperature, evapotranspiration and so on.

This study aims to describe and model the spatial variations of precipitation in the mountainous areas of middle Zagros using geo statistical techniques. To this aim, a dataset was obtained from 249 precipitation recording stations of Meteorological organization (climatologic, synoptic, and rain gauge) and Ministry of Energy of Iran in a 10-year period (1995-2004). Among these stations, 219 stations were used for modeling the spatial variability of precipitation, and the remaining 30 stations were used to evaluate the proposed method. To check the normality of data and detect the trend, Kolmogorov- Smirnov test and Trend Analysis of ESRI Arc GIS 9.3 is utilized, respectively. An evaluation of simple, ordinary, and general kriging with and without auxiliary variables of altitude, latitude, slope, and distance from ridges show that the ordinary kriging with latitude and distance from the ridge auxiliary variables are more precise. To illustrate the spatial variability of precipitation in the area of study, profiles of precipitation and altitudes perpendicular to ridges were plotted in the 50 Km intervals. These profiles make evidence that despite the relative correlation between precipitation and topography, the points with maximum precipitation does not correspond to the highest topographic points. Moreover, leeward and windward sides present different behaviors with respect to precipitation. Finally, it is found that topographies act as barriers in the way of air masses, thus air masses in clash with mountain release large amounts of their humidity.

Drought is a natural phenomenon that periodically, every region and every climate, occurs in different sectors of human life can be affected by many negative effects. So far, the drought has many definitions. The lack of a comprehensive definition of drought and the different means, from a different perspective, the drought has hampered. For this purpose the quantitative expression of drought This phenomenon is used. This research proposes four characteristics of drought intensity, duration, and extent of occurrence of drought has been studied. To determine the extent of drought and its effects on different parts of the study area, indicators PNPI, MPNPI, DI and RAI in different parts of the draft Zoning calculated using ARC GIS software have been conducted. To achieve this goal, the statistics for 32 meteorological stations and synoptic polls that are doing good distribution in the region, 30-year period (from 1981 to 2010) were collected. Spatial distribution of drought indices using ground ordinary kriging (OK) and the spherical model, exponential, linear and Gaussian and inverse distance method (IDW) Batvan 1 to 5 were calculated, and the area classified drought done were. Then the method ground by using various criteria to evaluate and compare the RMSE and MAE. The results show that the ordinary kriging (OK) Gaussian model is more appropriate than the inverse distance method (IDW) and has a higher accuracy. It is recommended that this method be used for mapping regional drought.

This paper investigates the effectiveness of Kriging and Inverse Desistance Weighting (IDW) methods and Hotspot analysis in evaluation of groundwater quality in Kerman Plain, Iran. In order to conduct this study the information regarding the concentration level of electrical conductivity (EC), total dissolved salts (TDS) and sodium adsorption ratio (SAR) of 60 exploiting wells in the study area were obtained. The positional accuracy and correctness of this information were confirmed. Then, interpolation maps were produced for EC, TDS and SAR parameters using the raw data and the output of hotspot analysis (Z-Score) with the application of IDW and Kriging methods. This result demonstrates preferability of Kriging in regard to IDW using the output of Hotspot analysis (Z-Score), as the experts don’ t interfere with the range and number and produced maps are based on geostatistical analysis. Furthermore, the resulted maps indicated sharp boundaries of very high and very low concentration of each parameter and the calculated area for EC (1411, 1063 km2), TDS (1874, 1470 km2) and SAR (321, 396 km2) are very close to reality. Accordingly, applying the output of hotspot analysis in combination with other interpolation methods can assist researchers in determining the extent of very high and very low concentration of a parameter and in calculating the area of the affected region.