جستجوی مقالات مرتبط با کلیدواژه « خودهمبستگی مکانی » در نشریات گروه « جغرافیا »

Among the types of airborne particles, particles with a diameter of less than 10 microns have many adverse effects on human health. Meteorological parameters and the movement of a large volume of vehicles are considered the most important modulating factors in the distribution and concentration of atmospheric pollutants. In this study, in order to predict the concentration of PM-10 pollutant during a long-term interval in Tehran city, GA-ANFIS hybrid model was used. Wind speed, wind direction, temperature, relative humidity and traffic volume were considered as inputs and pollutant concentration PM_10 as the output of the model. The results of the calculation of the performance indicators showed that the combined GA-ANFIS model provides a better framework than the ANFIS model in predicting the pollutant concentration PM_10. In order to evaluate the spatio-temporal patterns of PM_10 pollutant concentration and to identify hot and cold spots in Tehran city, local Moran's statistic and Ard-J Gettys statistic were calculated. The results showed that there is a high level of clustering of PM_10 pollutant in Tehran (with 95% confidence level). The clusters of PM_10 have divided the city into two northern and southern parts so that most of the cold spots in the northern half and the hot spots in the south have spread to the center of the city

Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is ofparticular importance. Improper pattern of water consumption in different areas of a city can be one ofthe cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order toidentify consumption patterns in different areas of the city. The purpose of this study is to investigatethe spatial pattern of water consumption in Qom city using spatial autocorrelation techniques. For thisreason, the consumption of 117 neighborhoods of Qom city during 2017 was collected and theaverage household water consumption for each neighborhood was calculated. Moran index was usedto identify the type of consumption pattern and local Moran index and hot spot technique were usedfor spatial distribution of the consumption pattern. The results of spatial autocorrelation showed thatthe largest cluster pattern of water consumption in Qom city occurred in summer with the value ofMoran index (I = 0.24). Also, the highest significance of the index (z = 7.02) was observed in thisseason. In both local and hot spot analysis, it was observed that high consumption has a high clusterpattern compared to low consumption. Spatially, high consumption clusters were observed in thecentral and western neighborhoods of the city and low consumption clusters were observed in thesouthern, eastern and northern neighborhoods of the city. Temporally, high consumption clusters wereobserved in central and western neighborhoods in summer and winter, respectively and lowconsumption clusters were observed in cold seasons.

Due to deep, complex and everlasting interaction between precipitation and climatic elements-factors, there are changes and varieties in both time and space dimensions of precipitation. So that climate experts and related scientists take their attentions to this phenomenon. An approach to do this kind of investigations is to describe spatial variations based on spatial statistics. The major spatial non-stationary of Iran precipitation is due to variation in situation, elevation and topography characters (slope and its direction) in this country. Circumstances of every one of these characters could determine the precipitation spatial patterns. Accordingly understanding spatial distribution of precipitation and its mechanism are important aspect in climatological researches. One of the common statistical models in which it is possible to determine the relation between variables as well as reconstruct, estimating and forecasting data is multivariate regression model. These sorts of models are useful for time series analyses as well as spatial modeling. One of the regression models that could be used in spatial analyses is called Geographically Weighted Regression (GWR). In current study it will be attempted to introduce this approach and using General Regression (GR) to justify spatial variation of precipitation in Iran based on 1436 stations in Iran.Research Methodology In this research Esfezary data base have been used. This daily data based contain 15998 days and 7187 pixels (15*15 KM) of precipitation over Iran. Accordingly the data matrix is created in 15998* 7187 and S-mode dimension. This matrix data base is estimated by using 1436 stations and Kriging method. To achieve independent variables, digital elevation map by 15*15 KM resolution has been created. So that, spatial (including longitude and latitude) and topographic (including slope magnitude and aspect) characters have been derived. Accordingly a data base has been created that contain spatial characters, topographic features and precipitation amounts.

Quality water characteristics for drinking purpose are affected by numerous environmental and human conditions. Distribute of this characteristic in various parts, in according to exploitation and management of the environment and in order to geological and geomorphic structures are different. Factors including waste water infiltration, urban runoff, large livestock, industrial waste water, solid waste place, far and near to the sea are factors that directly and indirectly affect water quality. Theses affect especially if these due to increasing of some sensitive parameters such as nitrate, it is necessary to evaluate systematically. The main purpose of this paper is evaluating the quality of groundwater resource using GIS and geo statistics in Gorgan city. Water quality maps in GIS are created using interpolation methods. In fact, interpolation is estimation of the unknown continuous variable based on the discrete known samples. There are several methods for interpolation of spatial data. all these models are based on the logic of the effects of spatial autocorrelation functions. According to calculation of variables correlation ratio, structural analysis is possible. In general, interpolation methods are divided into two deterministic and non-deterministic methods and local and global. Common interpolation methods are inverse distance weighting (IDW), polynomial, and geo statistic Kriging. Krigging is an estimation of the unknown samples based on weighted moving average. This model notice to land structure, with variogram. The best of unbiased line that include minimum of error variance is used for evaluation of model spatial precision. In this paper the values of some factors such as Mean Bias Error (MBE), Mean Absolute Error (MAE) and Root Mean Square Standardized were evaluated.