Efron's bootstrap method can only be used to estimate the precision measures of estimators when observations are independent. For SPATIAL data that are SPATIALly correlated, the moving block bootstrap method is usually used. But, in this method, the boundary observations have less chance of presence in blocks resampling than the other observations. In this paper, the new separate block bootstrap method is introduced and an algorithm is given for estimating the precision measures of estimators. A simulation study is carried out to compare the efficiency of the separate block bootstrap method with moving block bootstrap. It is shown that, with their method we can estimate the bias of sample mean with no error, and the estimator for variance of sample mean is consistent.

Drought is one of the environmental events and an inseparable part of climatic fluctuations. This phenomenon is one of the main characteristics of the various climates. Awareness of spatiotemporal behavior is effective in land planning. The SPATIAL statistical methods provide the means by which they analyze the SPATIAL patterns of random variables such as precipitation. In this study, using the rainfall data of 84 selected synoptic stations during the period of 30 years (1985 to 2014) in Iran, the SPATIAL analysis of drought has been investigated. Initially, using SPI values (timescales 3, 6, 12, 24 and 48 months), drought and traumatic periods of the area were identified and using the Geostatistic Analyst extension, the drought was zoned by interpolation methods. Moran STATISTICS were used to explain the pattern of drought in Iran. The results of Moran index for drought showed that the values for different years during the statistical period have a positive and close to one, indicating that the SPI drought index data has SPATIAL self-correlation and cluster pattern. Also, the results of Z score and P-value values, clustering of a SPATIAL distribution of drought, were confirmed.

Introduction: Cities, as complex social, geographical, and economic phenomena, also represent a significant SPATIAL phenomenon. In fact, a city is a SPATIAL unit where politics and power are closely intertwined, and cities require policies for their development. The urban living environment necessitates extensive policymaking and development interventions. Data and Method: This research is of an applied development nature, utilizing a descriptive-analytical approach. Data for this study were collected from two sources: library resources and land use maps of Kerman city. These data were analyzed by transferring them to the ArcGIS software environment. To investigate and comprehend the level of dispersion, clustering, and expansion of Kerman city, as well as to understand its physical development pattern, various SPATIAL statistical analysis methods were employed.Results: The findings indicate that the SPATIAL distribution pattern of Kerman city is clustered, with a relatively low level of SPATIAL clustering intensity. The city of Kerman highlights concentration in specific areas within the city, and it is not uniformly distributed across the city.Conclusion: The findings suggest that the development of Kerman city has been predominantly horizontal, with a continuous and dense development pattern in the city center. Results indicate that as we move from the city center towards the periphery, the SPATIAL distribution pattern becomes more scattered. Development has also taken place beyond the old city walls and between the two northern and southern belts. Furthermore, some villages are practically considered urban areas due to their proximity to the city and are within the legal city limits, such as Taherabad in the west and Sarasiyab in the east of the city. These settlements exhibit a semi-urban and semi-rural character both in terms of SPATIAL structure and the employment status of their residents.

Introduction Survey of SPATIAL relationships of environmental data take into accounts as one of themost important goals of SPATIAL STATISTICS for analyzing the SPATIAL patterns and understanding the SPATIAL dependencies. In this context, Exploratory SPATIAL Data Analysis (ESDA) could well to provide methods for distinguishing between SPATIAL randomandnon-random patterns. Using the ESDA for analyzing the SPATIAL autocorrelation of climatic elements is necessary to distinguish the changes and SPATIAL distribution of them. The present research has aimed at explaining the use of ESDA for explaining the SPATIAL patterns of water vapor pressureas one of the most important climatic parameters. Water vapor pressure plays a crucial role in climate system as an important feedback variable associated with the earth’s energy balance and hydrologic cycle. This climatic parameter has an important rolein explaining the climate change or changes in climatic parameters, because of 1) It is the main source of rainfall in all weather systems, 2) It suppliesthe latent heatin this process and controls the heat in thetroposphere, 3) It is the booster of the storm's speed and 4) plays a major role in the dynamics of atmospheric circulation. So, determination and interpretation of the likely reasons of Water vapor pressure changes and its variability are vitally important for human as well as other living-beings.Materials & Methods The study area, with about 360, 200 km2 area, is located in the south and southwest of Iran and approximately between 25° 00' N and 34° 25' N latitudes and between 45° 38' E and 59° 17' E longitudes. Southern and southwestern parts of the study area are located beside of two massive sources of moisture, Persian Gulf and Oman Sea. The main mountain chain in the study area is Zagros that extends from the northwest to the southern part of study area. The Zagros mountain range is responsible for the major portion of rain-producing air masses that enter the region from the western and northwestern sides, with relatively high amounts of rainfall. In this study, water vapor pressure data in pixels (dimension of 9×9 km) in the time interval 1981-2010 were collected by the Iranian Meteorological data website (http: //www.weather.ir).For Interpolation the water vapor pressure Kriging, Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF) were tested and so after the error validation, the optimum method (Ordinary kriging with Gaussian method) was chosen. As regards the aim of this study, analyzing the SPATIAL variability of WV in regional and local scale, the most important geographic features such as elevation, longitude, latitude, slope and aspect were chosen. Topography maps of the study area collected by the Geological Survey of Iran (http: //www.gsi.ir). By mosaicking, georeferencing and editing these maps in Arc GIS 10.2 software, the Digital Elevation Model (DEM) by 10 Km cell size was derived and based on it, the geographic features are prepared. Moran's I, Anselin local Moran's I and LISA as ESDA’s approaches were used to analyze the SPATIAL autocorrelation of water vapor pressure patterns based on climate parameters.Results & Discussion According to the cross validation, it was cleared that the optimum method for interpolation of water vapor pressureis Ordinary kriging with Gaussian method. The results of Moran’s Istatistic showed that the water vapor pressure hasSPATIAL structure and distributed in cluster patternin the South and South West of Iran. The monthly’s surveys showed that the SPATIAL autocorrelation of water vapor pressure in warm months are higher than cold months and therefore have a greater tendency to cluster. The results also showed that the water vapor pressure tending to disperse and non-clusterinspace in the South and SouthWest of Iran. The bivariate Moran' s Istatistic for relation of water vapor pressure and longitude showed the strong and positive SPATIAL autocorrelation and also clustered pattern.Conclusion Survey the monthly SPATIAL autocorrelation of water vapor pressure showed that the water vapor pressure in warm month more dealing with high SPATIAL autocorrelation than cold months and more inclined to clustering. The existence of such situationin the most regions of the study area in the warm season reflects the Consistency and homogeneity in this season in relation to the other seasons. Maybe the main reason of this circumstances are the lack of non-diversification of input pressure systems in this season, climate uniformity and sustainability and effects of local systems. Over time, the water vapor pressure in the south and southwest of Iran has tended to be more dispersed and less clustering in space. The reason for this incident is not fully revealed but it may be attributed to topographic effects, changes in system positioning, land use changes, etc. Investigating the relationship between SPATIAL distribution of water vapor pressure and geographic parameters showed that the relationship between water vapor pressure and latitude, elevation and slope suggested adispersed and heterogeneousSPATIAL distribution between them. The results of the bivariat erelationship between water vapor pressure and aspect suggested a discontinuity and randomness relation.

Khuzestan Province is adjacent with some great deserts that the dust of these deserts are continually entering this province. Understanding the SPATIAL pattern of these dust is of great help when planning to mitigate its effects. Therefore, the aim of this study was to determine the best inter-discipline of the dust and then to analyze the SPATIAL STATISTICS of the dust in Khuzestan province during the period 1995-2016. The results showed that the method of reverse weighing with a coefficient of 0. 75 is the best model for the microscope zonation. Analysis of the SPATIAL STATISTICS of the dust phenomenon in Khuzestan province shows that we have witnessed clustering of days with dust in the province over the years. The clustering maps of SPATIAL autocorrelation of the dust using the Moran STATISTICS show that in general, the northwest and western regions of province during the studied period have high clusters and the southern, eastern, southeastern and the southwest have low clusters in the province. In 1995, the highest Moran coefficient was (Moran Index = 1/008797) and in 2008 the lowest Moran coefficient was (Moran Index = 0/996831), indicating the highest and lowest number of cluster dust days in 1995 and 2008. The results of hot and cold spots in Khuzestan province during the statistical period show that mainly the north and northwest regions contain hot spots, which means more frequent days of dust in these areas of the province during the studied time period.

Urban planning has gone from the past to the present day to a greater extent based on physical factors, to the extent that the basis of urban planning plans and urban plans influenced urban development and physical spaces. It's The city is the product of complex economic and social relations and its SPATIAL heterogeneity reflects the processes of widespread socio-economic-cultural-social life in the context of nature in history. Understanding the pattern of SPATIAL development in the major cities of the regions and countries is essential for the formulation of appropriate policies and the achievement of sustainable development. Achieving this also requires the use of appropriate and advanced methods and tools. This article was written with the aim of studying the SPATIAL growth and development of Gorgan city using SPATIAL analysis models and SPATIAL correlation techniques using GIS. Accordingly, in order to analyze the SPATIAL development of Gorgan, a research hypothesis was presented and analyzed through descriptive-analytical method. Accordingly, various SPATIAL STATISTICS and SPATIAL correlation techniques, including cluster analysis, Moran and Gray coefficients, General G, hot spots analysis, have been used. The results show that Gorgan has experienced a cluster-dominated randomized growth pattern with the formation of two hot spots in the southern and eastern part of the city and a cold lake in the northern part.

‎Modeling and efficient estimation of the trend function is of great importance in the estimation of variogram and prediction of SPATIAL data. In this article, the support vector regression method is used to model the trend function. Then the data is de-trended and the estimation of variogram and prediction is done. On a real data set, the prediction results obtained from the proposed method have been compared with Spline and kriging prediction methods through cross-validation. The criterion for choosing the appropriate method for prediction is to minimize the root mean square of the error. The prediction results for several positions with known values were left out of the data set (for some reason) and were obtained for new positions. The results show the high accuracy of prediction (for all positions and elimination positions) with the proposed method compared to kriging and spline.

The aim of this work is to understand the relation between time and place that an earthquake takes place. In order to answer this question, the Modified Level Crossing (MLC) technique has been implemented. By studying two earthquakes, one in Iran and one in California we came to the conclusion that there is a relation between time and place of an earthquake occurrence. As a matter of fact, this relation is quite decisive. By performing MLC analysis and comparing the two regions, we can state that geographical effects play an effective role due to geophysical differences between Iran and California. Indeed, by comparing the readings of Iran and California, one could come to understand the geophysical differences between the two domains.The so-called level crossing analysis has been used to investigate the SPATIAL and temporal fluctuations of earthquake form time series. In this paper, we calculated the average frequency of up-crossing for original and shuffled data of Iran and California earthquakes in SPATIAL and temporal series.This analysis showed a significant difference between the original data and shuffled data. By introducing the relative change of the total number of up-crossings fororiginal data with respect to the so-called shuffled data, R, and calculate the Hurst exponent, Iran and California earthquakes are compared.

Drought is the most important natural disaster, due to its widespread and comprehensive short and long term consequences. Several meteorological drought indices have been offered to determine the features. These indices are generally calculated based on one or more climatic elements.....