جستجوی مقالات مرتبط با کلیدواژه "زمانی" در نشریات گروه "جغرافیا"

Agriculture is one of the most important sectors of the economy, especially in developing countries. Due to growing population, food security has become a major challenge and elimination of hunger is identified as one of the main goals of the millennium development goals. In this regard, considering the strategic nature of cereals and its effective role in ensuring food security in the country, the purpose of this study is to analysis the trend of spatial and temporal changes in cereals (wheat and barley) over a 15year period from 2003 to 2018. In this study, the research method is descriptive-analytical. The data were collected documentary based on the official data published by Ministry of Agriculture Jihad and Statistics Center of Iran. Data analysis was performed using spatial statistics techniques in the ArcMap software environment. The results of the study show that at the national level, changes have been made with a relative reduction in the area under barley cultivation and a significant reduction in the area under wheat cultivation. The results of the research at the provincial level indicate a relative decrease in fluctuations and the range of changes in the area under cereals cultivation among the provinces of the country, in other words, a relative balance has been created in the allocation of land to wheat and barley cultivation among the provinces. The results of spatial analysis of cereal cultivation area also show that the focus of cereal cultivation is mainly in some counties located in the west and northwest of the Iran.

Introduction :

Rapid population growth and limited production resources, especially in the agricultural sector, have made the issue of food security one of the challenges in the world. One of the main goals of sustainable development is to end hunger, achieve food security and develop sustainable agriculture. But predictions indicate that this goal will be difficult to achieve. Thus, world hunger is increasing and the lives of many people are directly and indirectly dependent on agriculture.

Purpose of the research: 

one of the basic approaches to agricultural development and food needs is to increase the yield and productivity of agricultural products for this reason, the present study was conducted with the aim of analysis patterns and spatial and temporal changes in cereal yield (wheat and barley) in Iran during a 35-year period from 1983 to 2018.

This study is an applied research and methodologically descriptive-analytical. Data and information were collected in a library method. Gi statistic or hot spot analysis used to discover the spatial pattern of cereal production (wheat and barley) and to identify its focal points. Arc GIS software was used to analyze the data.

Geographical area of research: 

The study area in this study includes the entire political and geographical area of Iran except open waters. In the present study, three levels of national (Iran), provincial (31 provinces) and counties (429 counties) have been analyzed for spatial and temporal changes in cereal yield.

Findings and Discussion During the study period, Isfahan province had the highest decrease in wheat yield and Khorasan Razavi province had the highest increase in wheat yield. In barley, some provinces such as Mazandaran, Fars, Kerman and Bushehr had the highest decrease in yield and Khorasan Razavi province had the highest increase in barley yield.

The results of research at the national and provincial levels show an increase in cereal yield during the study period. Also, spatial analysis at the county level shows the formation of a cluster pattern with a concentrate on high cereal yields in some plain areas of Iran

The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the highest value is in 44 to 45 degrees of east longitude about 2.8 to 3.3 and the lowest value of albedo is also in 52 to 53 degrees of east longitude, that is, the eastern slopes of the Zagros Mountains, have been recorded at 1 to 1.5 units. In terms of provincial rank, the largest albedo is about 0.25 units in Ilam province and the Fars province is ranked next about 0.24 units. The lowest amount of albedo also in the Gilan provinces and in next Mazandaran province are about 0.19 and 0.18 respectively. In addition, the results of temporal analysis in seasonal scale showed that the highest albedo in Iran in winter was 0.26 and its lowest amount was recorded in spring with 0.23 units. In general, according to the factors used, it can be said that the western and central parts of the country have a highest albedo, and the north and northwest regions of the country have a lowest albedo.

Gastric cancer is the most common gastrointestinal cancer and the third leading cause of death in men and the third most common cancer among women in the past two decades in Iran. Analysis the effective related agents can resulted in performance the health system in order to reduce the risk of this disease and help improve the public health. This study due to the difference in various regions, try to discover the spatial trend of Stomach cancer, and to identify critical areas in Metropolitan Tehran, Local Moran autocorrelation index taking into account the different levels of gastric cancer in Tehran. The results showed the High risk of stomach cancer in South and central regions of Tehran that suffering higher levels of pollution in water and air.

Dust storm has been one of the most important challenges of western Asia. This phenomenon has been intensified due to the drought and has many negative effects on people's lives. Since this region located in a dust belt in the world, it is necessary to explore different aspects of this phenomenon is well. Predictive and modeling of this phenomenon can be prevented of jeopardizing the lives of millions of people. So present a Regional Model to assess different aspects of this phenomenon is necessary. Since climate and weather elements are constantly changing, the spatiotemporal model should be used for modeling and visualization. Hence, a model for estimating dust emission has been designed and developed and Geographic Information System (GIS) spatial modeling capabilities and remote sensing (RS) data (wind speed, soil moisture, soil texture and digital elevation model) are used. The model which is called DustEM calculates horizontal dust emission. In this study, modeling is done for 2001 to 2007 and model’s output is evaluated by MODIS AOD and for dictating hot spot area output is clustered in 3 categories contain high, medium and low with threshold 0.3 and 0.6 for AOD. Accuracy index mean for the study period was 73.6% and show high precision of model in detecting hot spot area.

This study seeks to identify and determine the spatial-temporal variations of sediment yield in Qarahsu watershed situated in Ardabil province. To do so, the relations between sediment yield and precipitation were examined in term of temporal-spatial variations in order to provide the estimate model of sediment load in the subwatersheds. Data gathered from six water and rain gauges of the same name over a 22-years period was used. The method followed regression analysis between precipitation and sediment yield by SPSS software and analysis of temporal variations of precipitation and sediment yield by Excel software. Analysis of temporal relations between sediment load and precipitation indicated a higher correlation in intra-annual than in inter-annual scale. In terms of intra-annual variations, except for Hir water gauge, that underwent an increasing trend, other stations had decreasing trends in sediment load. Nonetheless, the increasing trend in annual precipitation of 4 rain gauges was considerable. Results of regression analysis, on one hand, indicated weak correlation between precipitation and sediment load in intra-annual scale, but on the other hand, indicated high correlation in inter-annual scale. Meanwhile, the Fournier index, as seasonal precipitation index, can explain 65% variance of specific sediment yield in the studied watershed. Hence, the index, as indicator of precipitation erosive power, can be effectively used to estimate specific sediment yield in the watershed.

Introduction The climate is such that large system interaction between the four large-scale climatic: hydrosphere, cry sphere, lithosphere and Biosphere. If a change occurs in one of these systems, other systems quickly or slowly make their coordinators. The consequence of this arrangement, the skirt has been instrumental in changing the initiator includes an endless chain links, the device will tie together. Because this is the most important link between climates scientists in the fields of concern in recent years as climate change. Why the climate change caused by global warming and subsequently climate of the planet for future decades, the rapidly growing world system has been disturbed. 2- Methodology In this study, were used the data of mid-level atmospheric pressure, geopotential height pressure from the center's database based on National Centers for Environmental Prediction / National Center for Atmospheric Research(NCEP/NCAR). This data are available for 6 hours GMT time and period 1948 to 2007 on the website Climate Diagnostics Center. National Oceanographic & Atmospheric Administration Government (www.esrl.noaa.gov). The spatial resolution of the data is 2/5 degrees of arc and the area from 0 - 90 degrees north and 0 - 360 degrees meridian and contains 5184 pixels. For comparison, the t - test was used for paired samples and to achieve this goal, the 30-year period was divided into two periods. Diagnosis and detection of positive or negative trend (increase or decrease) during the period of the subtropical high pressure system in the area used the f Kendall's tau trend test and a significance level SPSS software 0/05 and 0/01. 3– Discussion Results of Kendall's tau trend test calculation showed, increases in all months of the area insubtropical high pressure system at significance level of 0/01. August has highest and April has lowest area rate. The system is based on semi-warm and generally higher latitude and has the lowest area. With the passing of spring, and the system is still moving higher latitude, until in July to reach its highest latitude. But with the start of the transition period and beginning of cold period of the year, this contour is pulled toward the low latitudes and most of the area is covered and In February to reach its lowest latitude. 4– Conclusion The results showed that all the months of the study period, with an increasing trend (positive) area are subtropical high pressure system. August has the highest and April has the lowest area rate. The northern limit of the system (contour of 5840 geopotential) in the semi-warm temperate latitudes generally is based as parts of the North, Central Mediterranean, central Europe and North America and is area lowest in July. But with the onset of cold periods, the contour is pulled down latitude and occupies the most area in February.

Various plannings in agriculture, hydrology, architecture and energy largely depend on the correct estimation of the amount of solar radiation. To estimate the solar radiation, most of the previous studies have used Angstrom model, having the values of sunshine hours as it's input, or regression equations through considering the climatic factors and elements affecting the radiation. In this study, optimized "Bird and Hulstrom" model is used to estimate incoming solar radiation in Kermanshah Province by using the daily data from synoptic weather stations of the province between the years 1990- 2009. For this purpose, all parameters involved in radiation, including length of day, humidity, air mass, air pressure, precipitable water, sky albedo, atmospheric absorption and absorption by ozone were used and the resulting spatial-temporal variations of radiation were analyzed. The results showed that there is little difference between the radiation values measured by pyranometer and values estimated by the model. This indicates the ability of the model to estimate radiation. The least amount of incoming solar radiation in the region occurs in January and December, which varies from 9 to 13 MJ/m2/day in different locations of the province. The amount of radiation absorbed by ozone is reduced in the summer due to the reduction of air mass, especially in the highlands; while the amount of direct radiation and thereby total radiation is increased in this season. The highest amount of incoming solar radiation in the region occurs in June, which varies from 32 to 42 MJ/m2/day in different locations. In autumn and winter, radiation differences in the region are reduced because of the role of diffuse radiation.

Wayfinding is one of the important tasks of individuals in an urban traffic network. Guider geospatial information systems are the common services which helps people to navigate different unfamiliar routes with altered goals. The key challenge of these systems is the introduction of wayfinding information in a context aware manner. However، space، time and identity are the primary contexts and the other contexts are modeled based on these three elements. This article concentrated on modeling spatio-temporal relevancy of moving user based on his/her identity. The introduced model is able to cover all of the spatial relationships (topologic، metric and directional) on temporal dimension also to consider the characteristics of user and related contexts. In this way the proposed model utilizes Allen’s Multi- Interval Algebra (MIA) along with Dynamic Voronoi continuous Range Query (VCRQ) and introduces an innovative approach based on coarser calculus principals and customization methods. The model (MI 25) has been implemented as a software runnable on mobile systems and PDAs. The assessment of the implemented model is done regarding to three parameters including accuracy، time performance and users satisfaction. Tehran Districts 3، 6 and 11 are selected as case study area. In order to test the accuracy parameters، the designed software is run in three different routes with two different velocities and three time durations in 100 iterations. Then the accuracy of the detection of the related contexts is tested by means of a binomial distribution with one-sided 95% confidence level، precision and recall factors. The results of implementation and evolution based on accuracy، time performance and user satisfaction demonstrated the efficiency of the proposed model in an urban context-aware wayfinding systems.

Daily precipitation data from Aphrodite''s resources with a spatial resolution of 0. 25*0. 25 degree for 57 years have been used to detect the trend of precipitation in Afghanistan. First of all، monthly، seasonally، and yearly maps and regional mean time series calculated. According to the annual map، mean annual precipitation of the country is about 256 mm. There are two precipitation maxima in Afghanistan، one in eastern part and the other one is north- eastern of the country. Throughout the country precipitation maxima occurs in winter. The trend of time series with validity levels of %95 and %99 were examined by the use of Mann Kendall nonparametric. Trend analysis of annual time series shows no trend.

In this research Uremia was chosen as a case study. Trying to capabilities of GIS, sprung on the robberies of house and The resulting output in the form of a map of the crime-ridden areas, should be introduced.the method used is Statistical-graphical. The results show that the seasonal pattern of home robberies is concentrated in the spring and summer. Weekly pattern is concentrated on the weekends (Thursday and Friday).

Despite academic advances in spatial analysis within archaeology, primary uses of computer based GIS in archaeology were initiated by Cultural Resource Management (CRM) needs in the world. In conjunction with the development of environmental GIS data within several organizations, other federal agencies saw its potential for the management and spatial representation of archaeological data. Therefore, in other ways, developing methods of analysis and visual representation of data, previously impossible, were started. New methods of exploratory data analysis became possible and the efficiency within GIS environments for manipulation and analysis of spatial data contributed to exploratory and experimental use of data. Developed in catchment analysis of the 1970 in archaeology was a byproduct of settlement pattern and cultural ecological studies in which archaeologists became interested in the types and spatial distribution of resources exploited by a given settlement. The concept is based on the assumption that resource use around a settlement is distance dependant and that sites will be located so that to maximize exploitation of resource. Typically, the area associated with the settlement was calculated through Euclidian boundary techniques. Yet Euclidian distance measures do not take into account aspects of the topography in consideration of distance. Today, more sophisticated models utilize cost distance to establish distance in the development of catchment areas. Other improvements in spatial analysis can also be seen in settlement pattern analysis. With GIS, archaeologists are now able to explore and analyze multiple variables across a given landscape such as distance between archaeological site locations and particular types of architecture and/or artifacts. These variables can be used to discuss issues related to, for instance, mobility and socio-political development through time. At more micro-scales, GIS has been used to manage and analyze the distribution of artifact types at a given site. This type of analysis is useful for understanding the behavioral patterns at a given site and identifying specific activity areas within a site through time. This study has tried to analyze the spatial-temporal patterns of Songhor’s archaeological sites in GIS. To conduct the research, the techniques applied for gathering data will be pervasive surface survey. Accordingly, all archaeological and historical evidence will be identified and then recorded, in details. The collected data, including 286 sites and monuments from Neolithic to late Islamic period, was reported in 2 volumes. Dating of the sites has been carried out based on sample recognition and comparing the studies of collected surface data. The material gathered were divided into six general groups, which are Neolithic, Chalcolithic, Bronze, Iron, Historic and late Islamic period. According to current research, from the total 286 sites, two of them belong to Neolithic periods, 32 of them belong to Chalcolithic period, 25 of them belong to Bronze period, 46 of them belong to Iron Age and 147 of sites present the culture of the Achaemanid, Parthian and Sassanian era. Finally, 214 sites and monuments have shown the traces of the Islamic period which some of them show only a particular time of Islamic era and mostly present the monuments and architecture of this period. Then these archeological sites are chosen as our materials and statistical population. For achieving the research goals, we have used and analyzed geographical information, using Arc GIS 10 Software. By establishing a data bank, as Geodatabase, for the studywe carried out an analysis on spatial distribution of the sites. Thus, we focused on natural factors like height, slope, Landform, climate, flowing waters, rivers, flora (pasture), and rainfall to understand the role and efficacy of each factor in appearance of the sites. Our study made it clear that the ancient settlement patterns of Songhor was highly affected by natural factors such as flora, water sources, rainfall, and height. In addition, according to this research it has been specified that each of the natural factors have played different roles in the distribution of the ancient sites and there is no same precept for all. Thus, it is necessary that the role of each natural factor to be studied separately. The analysis shows that the area under study is affected by Zagros mountain ranges, height, distance to the river and dense pasture cover. This area has a cool climate and height and these factors have created special and different conditions in forming human settlements relative to other areas in central Zagros. Our study made it clear that the ancient settlement patterns of Songhor were highly affected by natural factors such as height and distance to the river.

Urbanisation changes the land cover types in urban areas and results in different climatic conditions، which has been termed as “Urban climate”. Urban climates are different from those of less built-up areas in terms of air temperature، humidity، wind speed and direction، and amount of precipitation. The previous researches show that urban areas are warmer than surrounding rural areas creating the «urban heat island» (UHI) (Rose and Devadas; 2009، 2). The buildings، concrete، asphalt and industrial activity of urban areas causes UHI. Replacing natural land cover with pavements، buildings and other infrastructures takes away the natural cooling effects. Also، tall buildings and narrow streets can heat the air trapped between them and reduce airflow. In addition، heat from vehicles، factories and air conditioners adds heat to the surroundings، further exacerbating the heat island effect. UHI can impact local weather and climate، altering local wind patterns، spurring the development of clouds and fog، increasing the number of lightning events، and influencing the rates of precipitation. Furthermore، the poor air quality that results from the increased energy usage for cooling purposes in heat-island city can cause discomfort for humans and affect health، aggravating asthma and promoting other respiratory illnesses (Liu and Zhang، 2011:158). Thermal remote sensing data play an important role in the study of UHI. First، UHI is studied using NOAA AVHRR thermal data (Balling and Brazell، 1988:1289-1293; Gallo et al.، 1993:899-908; Gallo and Owen، 1988:159-172; Streutker، 2002:282-289). Later، thermal infrared data TM، ETM+ and ASTER are used to study UHI more precisely (Weng، 2001:1999-2014; Weng et al.، 2006:1275-1286; Amiri et al.، 2009:2606; Flahatlar et al.، 2011). Meanwhile، in some studies، The spatial and temporal variations of UHI are detected by using multi- temporal thermal images of landasat TM and ETM (Mo et al.، 2011:4616-4622). Several researchers (Akbari، 1379; jangi، 1383; shakiba et al.، 1388:56-69; namdari، 1388; malekpour and taleai، 1389:89-102، and 1390:29-42) have studied the UHI in Tehran. But none of them have presented a comprehensive picture of its spatial and temporal variations. Thus، the main objective of this study is to study the spatial and temporal variations of UHI in Tehran urban area. Study Area: The city of Tehran as capital of Islamic republic of Iran is located between latitudes of 35°33’10“N to 35°50’12“N and longitudes of 51°05’17“E to 51°37’36“E with an average elevation of 1600 m، is surrounded by high mountains in the north and north east. Tehran experiences warm summers and cold winters. Tehran is a major center of culture، industry، commerce and transportation. The biggest city of the country، Tehran exhibits rapid population growth and urban expansion in the form of encroachment to the limited agricultural areas (malekpour et al.، 2010). Due to its complicated expansion and urban structure and function the temperature variations are very important for its life and existence. Thus this study has tried to analyse the spatial and temporal variations of its temperature. In this paper، multi- temporal Landsat TM images (13 images) are used for the analysis of UHI in Tehran. LST is extracted using the mono-window algorithm presented in Qin et al (2001). Then، using appropriate statistical methods، spatial and temporal variations of LST were analysed. Based on thermal radiance transfer equation، the mono-window algorithm only requires three parameters of emissivity، transmittance and effective mean atmospheric temperature - to retrieve LST from Landsat TM 6. The spatial model to retrieve LST from Landsat TM using ERDAS spatial model tool is designed. In this model، only two measured parameters are required; near-surface air temperature and water vapour content of the atmosphere at the time of satellite pass، which can be easily obtained from synoptic weather stations of Tehran. These two parameters are then converted to air transmittance and effective mean atmospheric temperature. The third required parameter is the emissivity، which can be calculated from the normalised difference vegetation index (NDVI). The remaining useful data can all be obtained from the Meta file in the satellite image data. It is impossible to compare thermal data from different years directly using absolute LST. Therefore، a normalisation technique (Xu et al.، 2009) has been used to compare the computed LST of different years. The LST images were rescaled to the same level between 0 and 1 using the maximum and minimum values for those images. Then temporal variations were explored. The intensity of UHI from 1986 to 2010 was evaluated by the urban heat island ratio index (URI) (Mu et al.، 2011). The URI was proposed to quantitatively compare the UHI differences in different years based on the ratio of UHI area to whole city area. The temporal changes of LST during the study period showed that the distribution pattern of LST has changed. The area of moderate and very cool temperature of the city has been decreased but the area of very high temperatures (very hot areas) has been increased significantly. It is completely clear that spatial extent of UHI along with the spatial development of Tehran have been increased during this period of 25 years (1986 to 2010) and the extent of UHI has increased from 83 square kilometres (13 percent) to 99 square kilometres (16 percent). The temporal change of the intensity of UHI also showed that URI has increased. The URI has increased from 0. 11 in 1986 to 0. 15 in 2010. According to the spatial distribution of LST، UHI has maintained its principal center (Mehrabad airport) and The new hot clusters have emerged in the west (District 21 and west of district 22) and south- west (portion of District 18 and 19) of the Mehrabad airport. Our study showed that UHI of Tehran has extended toward the areas with an intensive decrease in vegetation and the extensive development of industries، working places and warehouses (the west and south-west of Tehran). In this study، the mono-window algorithm is applied to retrieve LST in Tehran using the Landsat TM data. Then، using appropriate statistical methods، spatial and temporal variations of LST are analysed. Consideration of spatial-temporal variation of LST during the study period showed that the distribution of the UHI in Tehran has changed. Center of the UHI that had already been located on Mehrabad airport has extended toward the west and southwest of Tehran. The metropolitan of Tehran has witnessed a very fast urban sprawl during the study years from 1986 to 2010. The urban expansion was accompanied by increased UHI intensity of 0. 11 in 1986 to 0. 15 in 2010 over an almost 25-year period and resulted in the development of the severe UHI in the region and thus the degradation of the liveability of the city.

Accessing the long-term rainfall statistics in most of regions is restrained due to the lack of rainfall stations and drawbacks in recording data. Developing the artificial neural networks leads to reconstruct the missing information in dispersed meteorological stations even within the years in which the recording stations were absent. In this research the average monthly rainfall data including 36600 collected records for 305 meteorological stations were employed to reconstruct the missing monthly average rainfall values. The selected study area is a vast part in north west of Iran; including the provinces Ardebil and West East Azerbaijan and the models have been run for a decade between 1995 and 2004. The network structure has been based on two hidden layers and trained using the back propagation error algorithm. The longitude، latitude، elevation، slope، month number (1-12) and the average monthly rainfall values for 1 to 26 nearest neighbors for each station have been used as the model input variables. The results showed that using 5 nearest neighboring stations gives the highest precision and the obtained correlation coefficient during 1995-2004 is 0. 86 for the training data. The corresponding value of reconstructed data for West and East Azerbaijan and Ardebil provinces were 0. 82، 0. 78 and 0. 70 during 1985-1994 respectively. The reason of such differences in the mentioned provinces arises from the fact that the number of stations taking part in training process decreases from West Azerbaijan to Ardebil. The interpolated maps have then been created using different geostatistics methods based on the output predicted estimates resulted from neural networks. Consequently، this method allows investigating the spatiotemporal distribution of rainfall along with creating the monthly interpolated maps for years 1985 to 2004 in the study area. For validating the interpolation methods، the cross validation has been employed and the Mean Biased Error and Mean Absolute Error and Root Mean Square Error have been compared.