Geographic Data
Zahra Heydari monfared; Seyed Hossein Mirmousavi; Hossein Asakereh; Koohzad Raisipour
Abstract
Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis ...
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Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis is more visible. Due to the fact that most of the rainfall falls in the form of snow in mountainous areas, the management of snow resources in these areas is very important, and knowing the different aspects of variability and geographical patterns governing the phenomenon of snow is a scientific and practical need. It is considered special in water resources and in the agricultural sector. Thus, in the current research, the spatio-temporal patterns governing the annual average of snow density in different decades and the difference of each of the decades compared to the entire time period have been estimated and analyzed using spatial statistics methods.
Materials & Methods: The studied area with an area of about 151,771.91 square kilometers is located between 34°44' to 39°25' north latitude from the equator and 44°3' to 49°52' east longitude from the Greenwich meridian. In order to investigate the spatial autocorrelation changes of the average snow density in northwest Iran during the years 1982-2022 from the data obtained from the database of the European Center for Medium-Range Atmospheric Forecasting ECMWF4/ ERA5 based on daily data, and to identify and understand the spatial patterns of density Barf, based on statistical and graphic models have been used in the geographic information system environment. In the study of temporal-spatial changes of the average snow density of the region in different time periods including 4 decades ((1982-1992), (1992-2002), (2002-2012), (2012-2022)) and the whole period of 41 years (2022) -1982)), general Moran's I and Getis-Ord Gi* statistics were used. Also, in the current research, in order to investigate the effect of changes in Extreme snow precipitation on the amount of snow density in the northwest region, it has been done to determine the snow threshold. In order to estimate snow drift, a threshold was defined. Since the station snowfall amount data has a high dispersion, values above the mean cannot be accurate for defining the threshold of freezing snow. In this way, the 99th percentile index has been used to determine the snow threshold.
Results & Discussion: The aim of the current research is to investigate the spatial autocorrelation changes of the annual mean snow density in the northwest of Iran. For this purpose, the annual snow density data during the statistical period of 1982-2022 was obtained from the ECMWF/EAR5 database with a resolution of 0.25 x 0.25 degrees, and then divided into four ten-year periods. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettys-RDJ) were used at the significance level of 90, 95 and 99%. Also, in order to investigate the effect of extreme precipitation on changes in the level of snow density, the 99th percentile statistical index was used, and based on this index, the freezing threshold of each synoptic station in the region was determined during the last decade (2012-2022) and the interval the entire statistical period (1982-2002) was carried out. The results of the present research showed that in the studied area, snow density has spatial autocorrelation and a strong cluster pattern. With a density threshold less than 0.10 kg/m3, from the first decade to the end of the fourth decade, the area (number of pixels) and the amount of snow density in the northwest have decreased. The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly during the last decade of the study, and this has caused the snow density to increase relatively in the last decade compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has significantly decreased during the last four decades.
Conclusion: The evaluation of the temporal changes of snow density also strengthened the hypothesis of the occurrence of freezing snow precipitation leading to an increase in snow density in the months of cold seasons during the last decade. This point was confirmed by examining the statistical index of the 99th percentile of snowy days of each synoptic station in the region during the last decade (2009-2018) compared to the entire period of station statistics (2000-2018). The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly in the last decade of the study and this has caused the snow density in the last decade to increase relatively compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has decreased significantly during the last four decades. Moran's statistic was used to explain the pattern governing snow density in northwest Iran. The results of Moran's index about the annual average of snow density showed that the values related to different time periods have a positive coefficient and are close to one, which indicates that the snow density data has spatial autocorrelation and has a cluster pattern. Also, the results of standard Z score and P-value confirmed the cluster significance of the spatial distribution of snow density in the northwest. Finally, the analysis of hot spots has been a clear confirmation of the continuation of concentration and clustering of snow density in northwest Iran in space with the increase of the time period, which mountainous areas have the first rank in the formation of hot clusters with a probability of 99%. have given.
Geographic Data
Sajedeh Baghban; Masoud Minaei
Abstract
Extended AbstractIntroductionMore than 55% of the world's population now lives in cities, while around one billion people worldwide living in informal settlements. The city of Mashhad, as the second metropolis of Iran, has not been deprived of the phenomenon of marginalization and despite 3894 hectares ...
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Extended AbstractIntroductionMore than 55% of the world's population now lives in cities, while around one billion people worldwide living in informal settlements. The city of Mashhad, as the second metropolis of Iran, has not been deprived of the phenomenon of marginalization and despite 3894 hectares of informal settlements is the second city in Iran in terms of the size of such settlements. Informal settlements in the city are less resilient than other parts of the city, mainly due to their distinct social and physical characteristics. Some argue are focusing only on physical factors of resilient, but this is not possible without considering the social factors and social and demographic characteristics of communities. if society is prepared to deal with that crisis, a large volume of disturbances and irregularities after the crisis will be reduced. Since the approach of sustainable urban regeneration in Mashhad has started since 2019 with the establishment of facilitation offices and considering that these offices emphasize the social dimension of regeneration and the participation of local community in this process, therefore, the results of the present study can be effective in this way. Therefore, this study aims to find the answers to the following questions with the aim of spatial analysis of social resilience in the suburbs of the city:How is the social resilience of the suburbs in Mashhad?How is the spatial pattern of social resilience in Mashhad?Research MethodsSince the present study tries to analyze social resilience in the suburbs of Mashhad by using MCDM methods in the framework of urban resilience criteria, it has used descriptive-analytical method in the form of an applied research. For this purpose, based on library studies (articles, books, reports, and various documents), the required information in the field of social equity was collected. Then, by examining the dimensions and frameworks of social resilience, its criteria were determined and operationally defined. In this research, IDRISI software has been used to analyze the research data and evaluate them. Spatial statistics tools in ArcGIS software have been used to analyze the relationship between inefficiency distribution. Inefficiency pattern analysis is also performed by spatial autocorrelation technique. For this purpose, there are different models for measuring spatial autocorrelation statistics, among which the global Moran model and Gi statistic have been used. Spatial modeling of factors affecting inefficiency has been done by geographical weight regression.Discussion and resultsSince there are 3894 hectares of informal settlements in Mashhad and due to the fact that these settlements have been formed over time and without regard to urban planning standards, so they are very sensitive to natural and unnatural hazards and in case of any crisis, returning to pre-accident conditions is important. There are several factors involved in this field, including physical, economic, and social factors. The outcome of all these dimensions will affect the return of these settlements to pre-crisis conditions. Meanwhile, a review of studies on resilience showed that the physical dimension of resilience has been emphasized more than its social dimension. In the current situation of a metropolis such as Mashhad, an important part of the population and area of Mashhad is its suburbs, which includes 66 neighborhoods with a population of nearly one million people and an area of 3894 hectares. If we consider the city as an integrated system, it should be said that other dimensions of resilience, including social resilience, will also affect other sectors, including the physical one.ConclusionAnalysis of WLC, AHP and FUZZY methods, which were used in this study to evaluate the resilience of marginalized neighborhoods, showed that neighborhoods located in the northeast of Mashhad have more resilience than other areas, while the eastern and southeastern areas are less resilient. The social resilience pattern of these neighborhoods was evaluated by using the global Moran method and G general statistics. The results of this study showed that this zoning in the northeast and southeast is not random and has a spatial autocorrelation, so that in the northeast of the cluster of resilient neighborhoods, has led to increased resilience of other neighborhoods and in the southeast, low resilience has affected its reduction in adjacent areas. Warm and relatively warm clusters make up 1631 hectares of suburban areas, which is estimated to be equivalent to 42% of these neighborhoods. In fact, 31 neighborhoods in the suburbs of Mashhad are in hot and relatively hot clusters. The pattern of resilience is not significant elsewhere. Also, modeling the criteria studied in the study showed that the percentage of employed population, percentage of active population and average age have a significant effect on social resilience. According to the results of the leading research, in the process of re-creation that is taking place in the city of Mashhad, there should be a special look at the social dimensions of neighborhoods because the promotion of these dimensions can affect other aspects of resilience. Also, considering the impact of employment on the rate of resilience, it is suggested that in the process of recreating marginalized neighborhoods, special attention must be paid to job creation in these neighborhoods.
Yunes Khosravi; Hassan Lashkari; Aliakbar Matkan; Hossein Asakareh
Abstract
Introduction
Survey of spatial relationships of environmental data is considered as one of themost important goalsof spatial statistics for analyzing the spatial patterns and understanding the spatial dependencies. In this context, the Exploratory Spatial Data Analysis (ESDA) could well provide methods ...
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Introduction
Survey of spatial relationships of environmental data is considered as one of themost important goalsof spatial statistics for analyzing the spatial patterns and understanding the spatial dependencies. In this context, the Exploratory Spatial Data Analysis (ESDA) could well provide methods for distinguishing betweenspatialrandomandnon-random patterns. Using the ESDA for analyzing the spatial autocorrelation of climatic elements is necessary to distinguish their changes and spatial distribution. The present research is aimed at explaining the use of ESDA to describethe spatial patterns ofwater 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: 1) It is the main sourceof rainfall in allweathersystems, 2) It suppliesthe latent heatin this process and controls the heat inthetroposphere, 3) It is the booster of the storm's speed and 4) It 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 studied area, with about 360,200 km2 area, is located in the South and the 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 studied area are located beside two massive sources of moisture, i.e. Persian Gulf and Oman Sea. The main mountain chain in the studied area is Zagros that extends from the northwest to the Southern part of the studied area. The Zagros mountainrange 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) inthe time interval of 1981-2010 were collected by the Iranian Meteorological data website (http://www.weather.ir).To interpolate the water vapor pressure, Kriging Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF) were tested and so after theerror validation, the optimum method (Ordinary Kriging with Gaussian method) was chosen. Considering the aim of this study, analyzing the spatial variability of WV in regional and local scale, the most important geographical features such as elevation, longitude, latitude, slope and other aspects were chosen. Topographical maps of the studied area were collected by the Geological Survey of Iran (http://www.gsi.ir). The Digital Elevation Model (DEM)with a 10 Km cell size was derived by mosaicking, geo-referencing, and editing these maps in Arc GIS 10.2 software, and the geographical features were prepared based on it. Moran's I, local Moran'sAnselin, and LISA were used asESDA’s approaches to analyze the spatial autocorrelation of water vapor pressure patterns based on climate parameters.
Results & Discussion
According to the cross validation, it was cleared thatthe 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 is distributed in cluster patternin the South and the Southwest of Iran. The monthly surveys showed thatthe spatial autocorrelation of water vapor pressure in warm months is higher than the cold months and therefore hasa greater tendency to cluster. The results alsoshowedthat the water vapor pressure is 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 thestrong and positive spatial autocorrelation and also clustered pattern.
Conclusion
The monthly surveys showed that the spatial autocorrelation of water vapor pressure in warm months is higher than the cold months and is more tending towards clustering. The existence of such situation in most regions of the studied area in the warm seasons reflects the consistency and homogeneity in this seasons in relation to other seasons. The main reason for these circumstances may be the lack of non-diversification of input pressure systems in these seasons, climate uniformity and sustainability and effects of local systems. Over the 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 topographical effects, changes in system positioning, land use changes, etc.Investigating the relationship between spatial distribution of water vapor pressure and geographical parameters showed that the relationship betweenwater vapor pressureand latitude,elevation and slope suggested adispersed and heterogeneousspatial distribution between them. The results of the bivariaterelationship betweenwater vapor pressureand other aspects suggested a discontinuous and random relation.
