Geographic Information System (GIS)
Jalal Samia; Manouchehr Ranjbar Shoobi; Amer Nikpour
Abstract
Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among ...
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Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among them, passing through Kandovan road is fascinating with its beautiful natural landscapes. At the same time, this road is also known as one of the most dangerous roads of Iran due to its mountainous location and the potential occurrence of different types of climatic and geomorphologic hazards. Apart from these dangers, the occurrence of accidents in Kandovan road is one of the main concerns of tourists visiting west parts of Mazandaran province and also the local governments providing relief and rescue services and facilities to injured people. Therefore, it is crucial to identifying the dangerous sections of this road in order to minimize fatalities and socio-economic losses. The purpose of this research is to investigate the spatio-temporal density pattern of road accidents and also to identify accidents clusters along Kandovan road.
Material and methods
To this end, we used road accidents information along Kandovan road, collected by the relief and rescue bases of Red Crescent organization of Mazandaran province in the period of 2016 to 2022. Information like location, date, and the number of death and injuries in the road accidents along this road were used in this research. First, we used GIS, spatial and statistical analyses in order to get insight from road accidents distribution and statistics. In the next step, Kernel Density Estimation – a Geostatitical measure – was used to investigate the general spatial density pattern of road accidents in the period of 2016-2022 and also the spatio-temporal density pattern of road accidents in every year from 2016 to 2022. Furthermore, the hot spot analysis was implemented to the distribution of road accidents in this period in order to find out whether accidents are clustered, dispersed or randomly distributed. Both general spatial pattern and annual spatio-temporal patterns of accidents were investigated using hot spot analysis. With this, accidents clusters reflected as hot spots were identified based on the Getis-Ord Gi*index and the associated Z-score, P-value and Gi-bin statistics. In this context, the number of accident clusters, the length of road in the accident clusters and the percentage of observed accidents in the clusters were computed from 2016 to 2022.
Results and discussion
Results show that 2084 accidents were occurred in the period of 2016-2022 with 9076 injuries and 52 deaths. The most number of accidents was occurred in 2022 following the end of Corona lockdown in 2021. Also, several parts of Kandovan road indicated to contain the highest number of accidents density. Besides, the accident density pattern changes spatially and temporarily with an increasing trend in the number of accidents density from the end year of Corona disease epidemic in 2020. Results from hot spot analysis also identified several accidents clusters along this road in the period of 2016-2022. In this context, road accidents clusters were identified in Zangouleh Bridge, Majlar, Siah bisheh, Knadovan tunnel and Ushen Bridge with average Z-score value of 3.12, average P-value smaller than 0.05 and confidence interval of 90 to 99%. The total length of road in these clusters was more than 14 kilometer which contains around 60 % of the total accidents. The spatio-temporal distribution pattern of accidents clusters and also road lengths in the identified clusters change decreasingly in the period of 2016-2022. The results of this research can be used to investigate the reasons behind the occurrence of road accidents in the high accidents density sections and also in accidents clusters identified along the road. Taking proper preparation and mitigation strategies can be beneficial in proper crisis management of road accidents in order to avoid human causalities and socio-economic losses.
Conclusion
We conclude that kernel density estimation and hot spot analysis are effective geostatistical approaches to investigate the density pattern of road accidents and also to identify accidents clusters. In order to increase the safety of Kandovan road, the factors contributing to accidents occurrence in highly dense accidents sections of road and also in accidents clusters need to be identified, and with implementing proper measures, their effects can be minimized.
Seyyed Yaser Hakimdoust; Alimohammad Pourzeidi; Mohammad Saleh Gerami
Abstract
Introduction
Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal ...
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Introduction
Precipitation is an atmospheric factor, its quantity and distribution vary considerably in different parts of the planet, and is one of the most influential climatic elements that has always been influenced by the climate. Its amount changes in time and place continuously.Knowing the temporal and spatial distribution of rainfall is a useful tool for understanding how non-uniform distribution of water resources and vegetation in each region takes place.Precipitation occurs when the wet weather and the climb factor exist both in the region.In other words, the wet air must rise to a certain height so that it can reach the saturation point due to the subsequent cooling down, and in the next, the cloud produces precipitation.The absence of any of these two factors prevents the occurrence of precipitation.
Rainfall variation is considered as a key factor in the structure and functioning of ecosystems, but its impact on scale and magnitude is much less than its spatial variation.The climatic element, especially precipitation, has significant changes in time periods.Therefore, the recognition of the element of precipitation as one of the two elements of the climate and its changes in different times and places allows the optimal utilization of the natural environment.The amountand spatial distribution of rainfall is a fundamental factor for decision making, design and evaluation of hydrological models as well as water management and planning.Temporal spatial variations have diverse and varied impacts on the management and planning of water resources along a water basin.Climate change is one of the factors affecting the change of water resources.Precipitation, as a highly variable element, has always been a concern for climatologists and waterologistsas a fundamental factor in the blue balance. The extreme variability of rainfall along the time-space has a variety of study approaches.The purpose of this research is to identify the conditions of rainfall in Mazandaran province. Therefore, the location of rainfallin this province was investigated.In this regard, identification of the effective factors of the occurrence of these rainfall in different seasons and their role in the province has been addressed and its results will be available as a scientific and practical solution.
Materials and Methods
In this study, for the purpose of identifying the rainfall in the province of Mazandaran, five years of rainfall from 2006 to 2010 have been used from a total of 12 synoptic stations.Using extracted data from precipitation graphs, rainfall of more than 10 mm was extracted in the studied area.Then the data were categorized into four parts: spring, summer, autumn, and winter of the year. To create the database, they entered the SPSS and ARC GIS10 software.In the spatial analysis of the data, the semi-modification of these models has been used, which was calculated using ARC GIS10 software.The methods used in the zoning of Kriging and IDW models for fitting include: IDW with three potentials of 1,2,3, and the Kriging method with spherical, circular, exponential, Gaussian, and spherical models, which is performed with conventional Kriging technique.Also, for statistical comparison of models, root mean square error of RMSE, MAE, RMSE and their correlation coefficient were used.Then, optimal mapping based on multivariate regression was fitted based on the simulation method and the recursive method of six variables in rainfall generation including latitude and longitude, number of rainfall days, elevation, relative humidity and dew point temperature. The effects of these factors on rainfall in the province will be evaluated in different seasons and annually.
ResultsandDiscussion
The results of the spring survey show that there were 5 stations out of 12 stations without rainfall.These stations are located in the plain and in the mountain range of the region.The analysis showed that the correlation coefficient between variables is R^2= 967, which indicates a strong relationship between the set of independent variables and the dependent variable.85.8% of rainfall in the spring season in Mazandaran province depend on these variables. In the summer, only 2 stations in the province did not experience rainfall ranges, both of which were at high altitudes and include the station Alasht and Kyasar.Variables show a very strong relationship in the summer with a correlation coefficientof R^2=0.995 which is 0.9. 9%of rainfall in Mazandaran province depends on these six variables.The fall season is one of the high seasons in the province of Mazandaran. Only one station (Siahbisheh) has been registered from 12 storm rainfall stations.Estimates show that the six variables analyzed in this chapter with a correlation coefficient of R^2 = 0.983 represent a strong correlation.The results of the winter season show that all stations in Mazandaran province have rainfall, although it includes fewer days than theautumn season.All stations experience at least one day at Alasht Station for up to 7 days in Ramsar.The results of the analysis show that in winter, the correlation coefficient is R^2 = 0.996.
Conclusion
For zoning of the study area, the IDW method with three potentials of 1, 2, 3 and the Kriging method have been used with spherical, circular, exponential and Gaussian models. The evaluation and determination of the best model and verification of the produced maps was carried out. Also, for statistical comparison of the models, the root mean square errors of RMS, MAE, RMSE and their correlation coefficient were used, which, the best model for zoning was the IDW model with two potentials of 1,3 and ordinary circular kriging. Optimal mapping was done by multivariate regression based on the model of synchronous and retrograde method, and six variables that have the greatest effect on rainfall, including latitude and longitude, rainfall days, elevation, relative humidity and dew point temperature were studied.The results show that the correlation values of these six variables are 0.97 in spring, 0.99 in summer, 0.98 in autumn, 0.99 in winter and 0.99 in annual rainfall which indicates a strong relationship between these six variables in the rainfall ofMazandaran province.
Mohammad Reza Zand-e Moghaddam; Sepideh Habibi Kutanaei
Volume 22, Issue 88 , January 2014, , Pages 62-68
Abstract
Nowadays, tourism industry is among the largest and most varied industries of the world to the extent that many countries consider it as their main source of foreign exchange earnings and job creation, establishing social justice, cultural growth, increasing welfare level and a field for the growth of ...
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Nowadays, tourism industry is among the largest and most varied industries of the world to the extent that many countries consider it as their main source of foreign exchange earnings and job creation, establishing social justice, cultural growth, increasing welfare level and a field for the growth of private sector and a means for the development of their infrastructure. The present article seeks to find an answer to this research question: “what are the reasons for this country not benefiting from various advantages of tourism?” The research method is applied-descriptive which is performed in the form of a case study. While collecting information about the tourism development of the provinces and categorizing them, its potentials were ranked using TOPSIS. Results indicate that for most of the cases the province’s potentials are not in a good situation in terms of development index.
Mahmood Davoodi; Naser Bay; Omid Ebrahimi
Volume 22, Issue 88 , January 2014, , Pages 100-105
Abstract
Traditional methods of climatic classification are very diverse. Despite traditional and comparative importance, these methods have weaknesses which impair their comprehensive performance. Natural potentials as the background of human activities form the basis and foundation of many environmental programs ...
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Traditional methods of climatic classification are very diverse. Despite traditional and comparative importance, these methods have weaknesses which impair their comprehensive performance. Natural potentials as the background of human activities form the basis and foundation of many environmental programs and land use plans. Sustainable development needs careful planning based on resource constraints and abundances, and local development potentials are determined by its climate. Due to the significant topographic diversity and geographic expansion of Iran, providing a logical classification based on this country's natural realities is quite difficult. Due to topographic diversity of Mazandaran province, its climatic classification is not easily executable. The present article seeks to determine the climate of Mazandaran province according to Litinsky model. We tried to use different methods for climatic classification of the province, yet finally we focused on Litinsky model and explained it. Litinsky model use three fundamental elements of temperature, precipitation and Berry coefficient. Then, it takes advantage of auxiliary indicators including adaptation, continuity of dry season and solar radiation condition to provide a comprehensive classification. To do so, data obtained from 10 synoptic and climatologic stations in Mazandaran during 1984-2005 statistical period was used in SPSS environment. Finally, climate of Mazandaran province stations were determined and proposed in table 4.
