In Mazandarn province, regional unemployment rates appear to vary widely. In the year 2006, in this province, unemployment rates were 10.85 percent. This disparity is widely in county level. For example, unemployment rate at Joybar and Savadkoh counties were 6.5 and 20.6 percent respectively. Because of regional differences in unemployment rate, geographical analyze is important.In this research, by using SPATIAL econometrics models which is applied in SPATIAL AUTOCORRELATION and Iranian census data in the year 2006, we analyze the geographical distribution of unemployment in the 16 counties of Mazandaran province.On the basis of findings, there was positive SPATIAL AUTOCORRELATION phenomena in Mazandaran labor market at the county level. Counties marked by high unemployment rate, as well as those characterized by low unemployment rate, tended to be SPATIALly clustered, demonstrating the presence of ‘SPATIAL AUTOCORRELATION'.Also, we consider the spillover effect of county's shock on the others.

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.....

IntroductionPrecipitation is a vital component in the hydrological cycle. Its spatio-temporal variations have great environmental and socioeconomic impacts. The SPATIAL variation of rainfall depends upon many factors. Some of these variations are due to synaptic systems and some others formed by local physiographical characteristics of stations such as elevation from sea level, slope, windward and leeward slopes, land cover and land use and etc. If the rainfall is formed by widespread and pervasive synoptic system, it can show a significant SPATIAL similarity and homogeneity in the amount of a given rainfall in all over the region. This is affected by synoptic system. But if the rainfall is dominated by local factors the higher heterogeneity of given amount of the rainfall can be expected.

Poverty is a social, economic, political, cultural and political phenomenon that has long been the greatest of human problems, and human society, in spite of the tremendous changes that has taken place in various fields of science, is now struggling with. The present research is applied in a descriptive and analytical manner and is based on the studies conducted in this field and the analysis of the content of theoretical foundations and the extraction of indicators of poverty reduction on the one hand, and on the other hand, relying on the opinions of the relevant experts Using the Delphi model and the use of deep interviewing tools, the questionnaire, as well as the use of SPSS software in the statistical analyzes and ARC-GIS, GEODA TM and SPATIAL statistics analysis were selected. The analysis shows that 8 villages in the high poverty range, 21 villages in a wide range, 8 in the medium range, 5 in low and 5 in the low range. The results also show that 3 cold clusters (LLL) and 5 hot clusters (HH) were identified in the region, which is the largest hot spot in the southeast of the city near Semnan and is located in villages In this spot, there is a high rural poverty and are adjacent to each other and is the largest cold spot in the northwest of the city and near the city of Tehran and Ray.

Introduction Precipitation is considered as one of the most important climate elements with high temporal-SPATIAL variations. The variable can affect different environmental aspects represented through several different behavioral forms in extreme precipitation. Heavy and extreme rainfalls can occur in the form of flashfloods and usually in draught conditions with considerable negative consequences on human-environment. Therefore, the study of this type of precipitation in the area is an area suitable for agricultural activities. Recognition of such patterns can determine the success in environmental management as well as certainty in resources planning. There is considerable heavy and super heavy precipitation in coastal regions of Caspian Sea, Iran, especially in eastern areas. Thus, understanding the SPATIAL auto-correlation of such a phenomenon can facilitate environmental planning and the reduction of vulnerability to increase adaptability with such a disaster...

Today, the high number of the rural road accidents has shown that accidents at intersections organize high percentage of the total number of accidents. Meanwhile, the geographical information system (GIS) is considered as an appropriate tool for doing SPATIAL analysis and analysis of accidents at intersections. Also, considering that the accident data are massive and non-homogenized, the methods of SPATIAL AUTOCORRELATION and kernel estimation can present connected and more real models than samples of current spots in rural road accidents. The purpose of this research is SPATIAL analysis of rural road accidents based on rural intersection or utilizing SPATIAL AUTOCORRELATION methods and estimation of kernel density. In the first stage, it considers appropriate criteria for SPATIAL analysis of accidents in the old roads of Karaj-Qazvin in the 1388-1392 periods and were weighted by use of fuzzy analytic hierarchy process. Then, to identify accident prone intersections and investigation of their characteristics from AUTOCORRELATION functions of Getis-Ord Gi*, Anselin Local Moran's I and the kernel density estimation was used in order to investigate the SPATIAL AUTOCORRELATION, each of the useable parameters in five successive years used as a Moran's I function. The results have shown that there are 26 accident-prone intersections towards Karaj-Qazvin path and 10 accident-prone intersections for returning, from the total intersections of two way path. Also, for path towards Karaj-Qazvin none of the parameters and for returning path the only parameter of accident type contained SPATIAL AUTOCORRELATION in the five successive years.

In this study, Landsat TM images were used to analyze the spatio-temporal changes of urban heat island of Tehran. The time period of images (13 images) is from 1986 to 2010. First, the mono-window algorithm was utilized to retrieve land surface temperature (LST) from thermal band of Landsat TM images. Then, the global and local SPATIAL AUTOCORRELATIONs are used to quantitatively explore the SPATIAL and temporal evolution of LST in Tehran. Results of global SPATIAL AUTOCORRELATION showed that LST field in Tehran has changed to cluster pattern during the study period. In addition, the rate of global SPATIAL AUTOCORRELATION increased from 1986 to 2010. According to the results of the global SPATIAL AUTOCORRELATION two important remarks were obtained: (1) new thermal clusters were created during the study period. (2) The SPATIAL extent of the old thermal clusters have been increased. To clarify the nature of the clusters (hot or cool cluster) and to explore the SPATIAL variations of these clusters, the local SPATIAL AUTOCORRELATION were used. These results confirmed the strengthening and extension of the hot clusters and weakening of the cool clusters. Exploration and Comparison of the SPATIAL distribution of the high-high and low-low points during the study period showed that between 1986 and 2010 due to the degradation of half of the vegetation in the west and southwest of the Tehran (Districts of 18, 19, 21 and 22) cool clusters have been disappeared or fragmentized and new thermal clusters formed. As a result of these changes, urban heat island that was dominant in Mehrabad Airport (District 9) and portions of Districts 21 and 22 in 1986 expanded toward the west and southwest. Also, small thermal cluster have been emerged in the south and west of District 19. Moreover, because of the degradation of orchards in the northern part of Tehran (especially Shemiranat) cool clusters of these Districts have been weakened.

The assessment of urban heat islands is considered as a key variable in the studies of environmental sciences because modeling the interactions of the land surface flux can best respond to many urban problems of modern societies. This study aimed to detect heat islands over Arak city and their clustering was done. For this purpose, the satellite images of Landsat 8 (OLI and TIRS) related to August for three consecutive years of 2013, 2014 and 2015 were taken from the United States Geological Survey (USGS) Site. For the extraction of Urban Heat Islands (UHI) values of Land Surface Temperatures (LST), Moran' s AUTOCORRELATION functions and hot spot analyses by MATLAB and Arc GIS capabilities were used. After land surface temperature (LST) calculation, hot and cold clusters of heat islands over Arak were extracted using the hot spot analysis index. To evaluate the factors affecting the formation and clustering of heat islands in Arak NDVI and NDBI indices were used. The results showed that there is a high correlation between the two parameters, vegetation and urban built areas with land surface temperature so that the vegetation index has moderated and urban built areas has exacerbated the heat islands over Arak city. Comparative assessment of urban heat islands led to the detection of two types of heat islands over Arak: Focal heat islands and the linear heat islands. Moran's SPATIAL AUTOCORRELATION analysis revealed that the land surface temperature has SPATIAL structure in Arak; in other words, land surface temperature is distributed in clusters in Arak. Finally, analysis of hot spots is a clear confirmation on focus and clustering of heat islands over Arak by increasing the time period.

Temperature is one of the important elements of weather, resulting in part from solar energy absorbed by the effects of the Earth's surface and is converted to thermal energy. Maximum temperatures in the hot period of the year, especially in the hours after the city, should be given more attention. Analyzing the maximum temperature can also be normal evaluation mechanism to evaluate changes and track climate change. This study aimed to identify the spatio-temporal patterns and determine the maximum monthly patches of country, so the main focus of the maximum temperature will be identified...