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Showing 4 results for Spatial Distribution
Farhad Azizpour, Vahid Riahi, Somayeh Azizi,
Volume 7, Issue 4 (2-2021)
Volume 7, Issue 4 (2-2021)
Abstract
Abstract
As information about disease and mortality grows, so do appropriate methods for analyzing this type of data that meet different needs. One of these methods is spatial analysis of the disease, which considers its geographical distribution along with other risk factors. The present study is an attempt to depict the spatial pattern of coronary heart disease distribution in rural settlements of Damavand and to explain the factors affecting the spatial distribution of this disease in the study area. Spatial analysis of corona prevalence using spatial statistics analysis methods can extract and analyze the spatial patterns governing the geographical distribution of this disease. For this purpose, the present study seeks to answer the following questions:
As information about disease and mortality grows, so do appropriate methods for analyzing this type of data that meet different needs. One of these methods is spatial analysis of the disease, which considers its geographical distribution along with other risk factors. The present study is an attempt to depict the spatial pattern of coronary heart disease distribution in rural settlements of Damavand and to explain the factors affecting the spatial distribution of this disease in the study area. Spatial analysis of corona prevalence using spatial statistics analysis methods can extract and analyze the spatial patterns governing the geographical distribution of this disease. For this purpose, the present study seeks to answer the following questions:
- What pattern does the spatial distribution of coronary heart disease in the rural area of Damavand city follow?
- What factors have influenced this spatial distribution pattern?
Due to the nature of the subject, the present study is of the combined type and in terms of applied results. The method of data collection is based on documentary-library and survey-field data. Initially, the statistics of the number of patients with coronary artery from the beginning of April 2020 to the end of July 2020 were collected by referring to Damavand health center. Then spatial analysis is applied to them. In order to study the spatial pattern of corona disease distribution and to recognize its non-random structure from various statistical indicators such as mean, percentage, hot spot analysis and also to properly understand the pattern of hot spot clusters by measuring directional geographical distribution (standard ellipse) in GIS software environment. Used. After describing the structure and pattern of dispersions, one should look for the cause and reasons of dispersions. Thus, in field surveys, after determining the number of patients with coronary artery disease, snowball interviews were conducted with 23 residents of Damavand city in order to identify and analyze the factors affecting the spatial distribution pattern of coronary heart disease in this city. After conducting the interviews and collecting the data, in order to analyze them, the underlying theory in the Maxiquida software environment was used. Pearson correlation coefficient was used to determine the relationship between the factors affecting the prevalence of the disease in the study area as independent variables with coronary heart disease as a dependent variable in SPSS environment. Then, Moran's spatial autocorrelation analysis model was used to know the type of distribution pattern of the identified factors.
This part of the findings is divided into two parts according to the questions raised in the research: Spatial distribution pattern of coronary heart disease in rural areas of Damavand city Out of a total of 67 villages, 21 rural points (31.34%) and 1 rural point (1.49%), respectively, have the lowest and highest number of patients with coronary heart disease. Based on the analysis of clusters of hot spots and elliptical curve of geographical distribution, most hot spots are located in the west and northwest of the city and the villages located in these spots with low health centers have almost high population density that are adjacent to each other and They are close to the cities and on the main road. Most of the cold spots are located in the east and southeast of the region.
Factors affecting the distribution pattern of coronary heart disease in rural areas of Damavand city After determining the spatial pattern of corona disease distribution in the rural area of Damavand city, the effective factors in the spatial distribution pattern of this disease should be identified and analyzed. These factors include: Weak official information on coronary heart disease; Weak local community attention to the principles of health exposure to corona risk; Simplifying the risk of coronary heart disease; Short geographical distance between settlements; High level of inter-residential interactions; Weakness in providing health services. Pearson correlation coefficient was used to determine the relationship between the factors affecting the prevalence of the disease in the study area as independent variables with coronary heart disease as a dependent
This part of the findings is divided into two parts according to the questions raised in the research: Spatial distribution pattern of coronary heart disease in rural areas of Damavand city Out of a total of 67 villages, 21 rural points (31.34%) and 1 rural point (1.49%), respectively, have the lowest and highest number of patients with coronary heart disease. Based on the analysis of clusters of hot spots and elliptical curve of geographical distribution, most hot spots are located in the west and northwest of the city and the villages located in these spots with low health centers have almost high population density that are adjacent to each other and They are close to the cities and on the main road. Most of the cold spots are located in the east and southeast of the region.
Factors affecting the distribution pattern of coronary heart disease in rural areas of Damavand city After determining the spatial pattern of corona disease distribution in the rural area of Damavand city, the effective factors in the spatial distribution pattern of this disease should be identified and analyzed. These factors include: Weak official information on coronary heart disease; Weak local community attention to the principles of health exposure to corona risk; Simplifying the risk of coronary heart disease; Short geographical distance between settlements; High level of inter-residential interactions; Weakness in providing health services. Pearson correlation coefficient was used to determine the relationship between the factors affecting the prevalence of the disease in the study area as independent variables with coronary heart disease as a dependent
Hamideh Roshani, Raoof Mostafazadeh, Abazar Esmali-Ouri, Mohsen Zabihi,
Volume 7, Issue 4 (2-2021)
Volume 7, Issue 4 (2-2021)
Abstract
Introduction and objective:
Temporal and spatial variability of rainfall is one of the determining factors for water resources management, agricultural production, drought risk, flood control and understanding the effect of climate change. The impact of spatiotemporal patterns of precipitation on flood/drought hazard and available water resources is an undeniable issue in water resources management. Precipitation concentration (PCI) and Seasonality (SI) indices are the important indicators to determine the distribution of precipitation in a region which can lead to identify and manage before occurring natural hazards including flood and drought and hydro-meteorological storms. Several methods available to study the spatial and temporal distribution of rainfall. Indicators of rainfall concentration and seasonality are among the methods of studying rainfall dispersion that depend on the distribution of rainfall patterns at different time scales. Accordingly, the study and understanding of temporal and spatial changes in rainfall can lead to sound management policies in the field of water and soil resources by planners and decision makers.
Methodology:
The precipitation concentration index is presented as a powerful indicator for determining the temporal distribution of precipitation to show the distribution of precipitation and rain erosion. The increase in the value of this indicator indicates a low dispersion and a higher concentration of rainfall, which is closely related to the intensity of rainfall. Seasonality index as one of the key factors in detecting seasonal variation in the variables of natural ecosystems, measures the time distribution of hydrological components at different times of the year and uses each hydrological variable to classify different hydrologic variable regimes. In this regard, the present research aimed to investigate the spatial and temporal distribution and trend analysis of PCI and SI for 41 rain gauge stations of Golestan province (38-year study period) in annual, seasonal and dry and wet time scales. The Mann-Kendall test was used to determine the trend of time changes in PCI and SI indices during the study period in all selected rain gauge stations in Golestan province. Mann-Kendall test is one of the non-parametric tests to determine the trend in hydroclimate time series. The advantages of this method include its suitability for use in time series without a specific statistical distribution, as well as the effectiveness of this method in data with extreme values in time series. In order to determine the spatial pattern of PCI and SI indices in different time scales (annual, seasonal, and dry and wet periods), the method of inverse distance weighting was employed in GIS environment. In this method, a weight has been assigned to each point that decreases with increasing distance from the known value point. On the other hand, the effectiveness of the known point in estimating the unknown point and calculating the mean also decreases. In this regard, the best results are obtained when the behavior of the mathematical function is similar to the behavior of the observed phenomenon. The study area in terms of extent, topographic diversity, type of land use has a high heterogeneity that affects the characteristics and temporal and spatial occurrence of dry and wet periods. The average annual rainfall varies from about 150 to 750 mm over the study area.
Results:
According to the results, the average of PCI for annual, spring, summer, autumn, winter, dry and wet periods in the research area were obtained 13.15, 11.96, 13.15, 10.72, 9.96, 14.72, and 1072, respectively. Also, Chat station with 0.79 (seasonal distribution with dry and wet seasons) and Shastkalateh station with 0.47 (mainly seasonal distribution with short dry season) had the maximum and minimum of SI in the Golestan province, respectively. In addition, 27 and 14 of studied stations had the increasing (Significant and no-significant) and decreasing (Significant and no-significant) trend for PCI and SI.
Conclusions:
Non-compliance of precipitation in Golestan province with a single temporal and spatial pattern is another achievement of the present study. The results of the current research can be used as a roadmap for water resources planning and policy making in the study area. It is noteworthy that the PCI and SI indices do not emphasize the cumulative values of precipitation and address the pattern of rainfall distribution, which can be a better criterion for assessing changes in precipitation patterns at different time scales. In this regard, determining the priority of areas for protection and management of water and soil resources, and spatial pattern of agricultural crops. The trend of changes in PCI and SI indicators and its relationship with important climatic components can be considered in assessing the changes in pattern of precipitation and climatic variables.
Temporal and spatial variability of rainfall is one of the determining factors for water resources management, agricultural production, drought risk, flood control and understanding the effect of climate change. The impact of spatiotemporal patterns of precipitation on flood/drought hazard and available water resources is an undeniable issue in water resources management. Precipitation concentration (PCI) and Seasonality (SI) indices are the important indicators to determine the distribution of precipitation in a region which can lead to identify and manage before occurring natural hazards including flood and drought and hydro-meteorological storms. Several methods available to study the spatial and temporal distribution of rainfall. Indicators of rainfall concentration and seasonality are among the methods of studying rainfall dispersion that depend on the distribution of rainfall patterns at different time scales. Accordingly, the study and understanding of temporal and spatial changes in rainfall can lead to sound management policies in the field of water and soil resources by planners and decision makers.
Methodology:
The precipitation concentration index is presented as a powerful indicator for determining the temporal distribution of precipitation to show the distribution of precipitation and rain erosion. The increase in the value of this indicator indicates a low dispersion and a higher concentration of rainfall, which is closely related to the intensity of rainfall. Seasonality index as one of the key factors in detecting seasonal variation in the variables of natural ecosystems, measures the time distribution of hydrological components at different times of the year and uses each hydrological variable to classify different hydrologic variable regimes. In this regard, the present research aimed to investigate the spatial and temporal distribution and trend analysis of PCI and SI for 41 rain gauge stations of Golestan province (38-year study period) in annual, seasonal and dry and wet time scales. The Mann-Kendall test was used to determine the trend of time changes in PCI and SI indices during the study period in all selected rain gauge stations in Golestan province. Mann-Kendall test is one of the non-parametric tests to determine the trend in hydroclimate time series. The advantages of this method include its suitability for use in time series without a specific statistical distribution, as well as the effectiveness of this method in data with extreme values in time series. In order to determine the spatial pattern of PCI and SI indices in different time scales (annual, seasonal, and dry and wet periods), the method of inverse distance weighting was employed in GIS environment. In this method, a weight has been assigned to each point that decreases with increasing distance from the known value point. On the other hand, the effectiveness of the known point in estimating the unknown point and calculating the mean also decreases. In this regard, the best results are obtained when the behavior of the mathematical function is similar to the behavior of the observed phenomenon. The study area in terms of extent, topographic diversity, type of land use has a high heterogeneity that affects the characteristics and temporal and spatial occurrence of dry and wet periods. The average annual rainfall varies from about 150 to 750 mm over the study area.
Results:
According to the results, the average of PCI for annual, spring, summer, autumn, winter, dry and wet periods in the research area were obtained 13.15, 11.96, 13.15, 10.72, 9.96, 14.72, and 1072, respectively. Also, Chat station with 0.79 (seasonal distribution with dry and wet seasons) and Shastkalateh station with 0.47 (mainly seasonal distribution with short dry season) had the maximum and minimum of SI in the Golestan province, respectively. In addition, 27 and 14 of studied stations had the increasing (Significant and no-significant) and decreasing (Significant and no-significant) trend for PCI and SI.
Conclusions:
Non-compliance of precipitation in Golestan province with a single temporal and spatial pattern is another achievement of the present study. The results of the current research can be used as a roadmap for water resources planning and policy making in the study area. It is noteworthy that the PCI and SI indices do not emphasize the cumulative values of precipitation and address the pattern of rainfall distribution, which can be a better criterion for assessing changes in precipitation patterns at different time scales. In this regard, determining the priority of areas for protection and management of water and soil resources, and spatial pattern of agricultural crops. The trend of changes in PCI and SI indicators and its relationship with important climatic components can be considered in assessing the changes in pattern of precipitation and climatic variables.
Dr Hassan Lashkari, Dr Zainab Mohammadi,
Volume 9, Issue 1 (5-2022)
Volume 9, Issue 1 (5-2022)
Abstract
Synoptic analysis of the changes trend of the share of systems due to the Sudan low
In the cold period of the Persian Gulf coast during 1976-2017
Introduction
In the Ethiopian-Sudan range forms the low pressure system without front in the cold and transition seasons that is affecting the climate of the adjacent regions by crossing the Red sea. Based on the evidence in the context of Iran, studying Sudan low was first begun by Olfat in 1968. Olfat refers to low pressures which are formed in northeastern Africa and the Red Sea and then pass Saudi Arabia and the Persian Gulf, enter Iran, and finally, cause rainfall. The most comprehensive research specifically examining Sudan low, was the work carried out by the Lashkari in 1996. While he studying the floods that occurred in southwestern of Iran, he was identified Sudan low by the most important cause of such flooding and he explained how they are formed, and how these low-pressure systems were deployed on the southwest of Iran.
Materials and methods
The study period with long-term variations was considered from 9.5 to 11 years based on solar cycles. Precipitation data for 13 synoptic stations are considered above 5 mm in south and southwestern Iran. With three criteria were determined for the days of rainfall caused by each type of atmospheric system. The visual analysis of high and low altitude cores and geopotential height at 1000 hPa pressure level (El-Fandy, 1950a; Lashkari, 1996; 2002) were considered based on the aim of the study. Accordingly, the approximate locations of activity centers, as well as the range of the formation and displacement of the Sudan system were initially identified based on the location of the formation of low and high-pressure cores. Then, the rainy days due to the Sudan system in January were separated from the precipitation of the other atmospheric system.
Results and discussion
According to the selected criteria in the forty-year statistical period, 507 precipitation systems were identified with different continuities that led to precipitation in the northern coast of the Persian Gulf. The pattern of independent Sudan low rainfall was responsible for 77% of the precipitation in the Persian Gulf. Decade frequency share of Sudan low was lower in the first decade (16%) compared to the next three decades. This system of rainfall was more activated during the second and third decades compared to the first decade. However, rainfall changes were not evident in the mid-decade. Independent Sudan low precipitation provide 25% and 27% of the cold season precipitation of the Persian Gulf during the second and third decades respectively. In accordance with the 24th solar cycle, at the end of the study period, the Sudan low was more effective on the Gulf coast than ever before. During this decade, 125 cases of Sudan low rainfall was recorded for the Persian Gulf. Thus, the frequency of Sudan low during the fourth decade was about 31%, which was higher than in the rest of the decade. Overall, the Sudan low rainfall was repeated 151 times for 2 days rainfall, during the statistical period studied. This Precipitation has increased over the last decades compared to other periods.
Conclusion
The severe variability of rainfall along the timing and location of the permanent Persian Gulf coasts can have a significant impact on the economic and agricultural behavior of the Gulf population in the three provinces of Ahwaz, Bushehr and Hormozgan.The purpose of this study was to evaluate the precipitation changes due to Sudan low in the Persian Gulf coastal region during the cold period. The results of this study showed that the role of integration patterns in influencing the precipitation of the Persian Gulf coast has decreased with the strengthening and further activation of the Sudan low system during the last two decades. That way, about 77percent of the region's rainfall is provided by independent Sudan low. At the end of the course (in accordance with 24th solar cycle activity) the Sudan low system was more active than before. Although the Sudan low activity was different at each station during the period studied, but in the historical passage incremental and decade's positive behavior of Sudan low was common to all stations. Evaluation of changes in rainfall duration shows that the pattern of precipitation with 2days duration is more frequent than the patterns of one to several days.
Keywords: Sudan low- Solar cycle- Persian Gulf.
Nabi Mohamadi, Behrouz Sari Saraf, Hashen Rostamzadeh,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Nowadays, due to global warming, drought and the occurrence of cold periods and heat stress, the study of climatic variables is very important. Therefore, in this research, the long-term forecast of temperature changes in northwest Iran in the base period (1985-2014) and three periods of the near future (2021-2050), the medium future (2051-2080) and the distant future (2100- 2081) was paid. For this purpose, 2 extreme temperature indices including Warm spells duration index (WSDI) and cold spells duration index (CSDI) and Maan-Kendall trend test were used to check the changes. To predict the changes of the profiles in the future period after evaluating 7 general circulation models (GCMs) from the sixth report model series (CMIP6) from two optimal models under three socio-economic forcing scenarios including SSP1-2.6, SSP3-7.0 and SSP5-8.5 was used. The spatial distribution of the trend of changes in the Warm spells duration index (WSDI) in the base period showed that its maximum core is located in the south and southwest of the region, and its amount decreases by moving towards the north and northeast. Spatial changes of the Cold spells duration index (CSDI) are characterized by its maximum cores in the western regions and around Lake Urmia and minimum cores in the central and northern regions of the study area. According to the results, the average Warm spells duration index (WSDI) and of the Cold spells duration index (CSDI) are equal to 5.53 and 3.80 days per year, respectively, and the maximum and minimum Warm spells duration index (WSDI) are 1.8 and 2.7 days, respectively Piranshahr and Parsabad stations and the maximum and minimum and the Cold spells duration index (CSDI) are also 5.7 and 1.32 days corresponding to Zarineh and Marivan stations. Examining the trend of changes also showed that in most stations, the WSDI index has an increasing trend, and this trend has become significant in some stations, but the CSDI index has a decreasing trend and is not significant in any of the stations. The evaluation of different models with different error measurement indices also showed that MRI-ESM2-0 and MPI-ESM1-2-L models have the best performance in simulating temperature extreme in the studied area. The distribution of changes in the future period also showed that the WSDI will increase in most stations and based on all three scenarios, especially the SSP5-8.5 scenario, but the CSDI trend will decrease in most stations and based on the SSP3-7.0 and SSP5-8.5 scenarios will be significant.
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