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Qualitative Explanation of Cultural and Environmental Factors Reducing Organizational Silence in Social Service Organizations Qualitative Explanation of Cultural and Environmental Factors Reducing Organizational Silence in Social Service Organizations 2 1 INTRODUCTION: The efficiency and development of any organization largely depend on the proper use of human resources. In today's organizations, to reduce organizational silence, employees express their ideas and share their views to increase organizational efficiency. This study was conducted to qualitatively explain the cultural and environmental factors that reduce organizational silence in government organizations. METHODS: This applied study was conducted based on a descriptive-analytical approach and implemented through the field research method.  The samples (n=18) were selected among senior managers of government organizations using purposive sampling and the sample size required amount was based on theoretical saturation criterion. The required data were collected through holding interviews and they were analyzed using the grounded theory method. FINDINGS: The results showed that the four selective codes of "progress", "appropriate cultural background", "lack of proper attribution" and "increasing culture in the field of teamwork" could explain the concept of organizational silence. CONCLUSION: According to the results, the roots of the formation of this destructive and inhibitory organizational phenomenon are lied in the context of social, cultural, and political interactions, identified under the influence of "environmental and cultural factors", and started via social learning. These environmental and cultural factors can be programmed and corrected to guide and control organizational silence and direct the constructive voice of the organization. 2 INTRODUCTION: The efficiency and development of any organization largely depend on the proper use of human resources. In today's organizations, to reduce organizational silence, employees express their ideas and share their views to increase organizational efficiency. This study was conducted to qualitatively explain the cultural and environmental factors that reduce organizational silence in government organizations. METHODS: This applied study was conducted based on a descriptive-analytical approach and implemented through the field research method.  The samples (n=18) were selected among senior managers of government organizations using purposive sampling and the sample size required amount was based on theoretical saturation criterion. The required data were collected through holding interviews and they were analyzed using the grounded theory method. FINDINGS: The results showed that the four selective codes of "progress", "appropriate cultural background", "lack of proper attribution" and "increasing culture in the field of teamwork" could explain the concept of organizational silence. CONCLUSION: According to the results, the roots of the formation of this destructive and inhibitory organizational phenomenon are lied in the context of social, cultural, and political interactions, identified under the influence of "environmental and cultural factors", and started via social learning. These environmental and cultural factors can be programmed and corrected to guide and control organizational silence and direct the constructive voice of the organization. 1 8 2020/12/9 1399/9/19 2021/01/16 1399/10/27 فریدون احمدی Fereydoun Ahmadi Public Administration, Payame Noor University, Iran Iran freyedon@yahoo.com مزدک جمشیدی Mazdak Jamshidi PhD student, Public Management, Human Resources, Payame Noor University, Iran Iran mazdak.jamshidi@gmail.com سید علی اکبر احمدی Seyed Ali Akbar Ahmadi Public Administration, Payame Noor University, Iran Iran a.ahmadi4867@gmail.com زهرا فروتنی Zahra Forootani Public Administration, Payame Noor University, Iran Iran foroutani.dr@gmail.com Organizational Silence Cultural and Environmental Factors Grounded Theory Government Organizations Organizational Silence Cultural and Environmental Factors Grounded Theory Government Organizations 1. Deniz N, Noyan A, Ertosun ÖG. The relationship between employee silence and organizational commitment in a private healthcare company. Proc Soc Behavioral Sci 2013; 99: 691-700.##2. Hasan I, Hoi CK, Wu Q, Zhang H. Is social capital associated with corporate innovation? Evidence from publicly listed firms in the US. J Corporate Finance 2020; 62: 101623.##3. Zhang Q, Pan J, Jiang Y, Feng T. The impact of green supplier integration on firm performance: The mediating role of social capital accumulation. J Purchasing Supply Manag 2020; 26(2): 100579.##4. Takhsha M, Barahimi N, Adelpanah A, Salehzadeh R. The effect of workplace ostracism on knowledge sharing: the mediating role of organization-based self-esteem and organizational silence. J Workplace Learn 2020; 32(6): 417-35. ##5. Oduyoye FO. Debate on the role of organizational silence behaviors and employee efficiency. Global J Manag Busin Res 2020; 20(6): 1-9.##6. Delery JE, Roumpi D. Strategic human resource management, human capital, and competitive advantage: is the field going in circles? Hum Resour Manag J 2017; 27(1): 1-21.##7. Nankervis A, Baird M, Coffey J, Shields J. Human resource management. Canberra: Cengage Australia; 2019.##8. Tang G, Chen Y, Jiang Y, Paille P, Jia J. Green human resource management practices: scale development and validity. Asia Pac J Hum Resour 2018; 56(1): 31-55.##9. Lincoln YS, Guba EG. But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Dir Prog Eval 1986; 30: 73-84.##10. Danaee Fard H, Panahi B. An analysis of employee’s attitudes in public organizations: Explanation of organizational silence climate and silence behavior. Transform Manag J 2010; 2(3): 1-19 [In Persian]. ##11. Damghanian H, Rouzban F. Explaining employee silence in connection with the direct manager based on mixed method. Organ Behav Stud Quart 2015; 4(3): 194-75 [In Persian].##12. Farjam S, Almodarresi SM, Pirvali E, Saberi H, Malekpour S. The mediator effect of occupational burnout on the relationship between organizational cynicism and organizational silence (Case of study: employees of Farokhshahr social security organization hospital). Rev Publicando 2018; 5(15): 1136-59.##13. Beyran Nejad A, Davari E, Afkhami M. Organizational silence as a current challenge in human resource management: exploring the factors and consequences. Organ Behav Stud Quart 2017; 6(1): 147-76 [In Persian].##1. Deniz N, Noyan A, Ertosun ÖG. The relationship between employee silence and organizational commitment in a private healthcare company. Proc Soc Behavioral Sci 2013; 99: 691-700.##2. Hasan I, Hoi CK, Wu Q, Zhang H. Is social capital associated with corporate innovation? Evidence from publicly listed firms in the US. J Corporate Finance 2020; 62: 101623.##3. Zhang Q, Pan J, Jiang Y, Feng T. The impact of green supplier integration on firm performance: The mediating role of social capital accumulation. J Purchasing Supply Manag 2020; 26(2): 100579.##4. Takhsha M, Barahimi N, Adelpanah A, Salehzadeh R. The effect of workplace ostracism on knowledge sharing: the mediating role of organization-based self-esteem and organizational silence. J Workplace Learn 2020; 32(6): 417-35. ##5. Oduyoye FO. Debate on the role of organizational silence behaviors and employee efficiency. Global J Manag Busin Res 2020; 20(6): 1-9.##6. Delery JE, Roumpi D. Strategic human resource management, human capital, and competitive advantage: is the field going in circles? Hum Resour Manag J 2017; 27(1): 1-21.##7. Nankervis A, Baird M, Coffey J, Shields J. Human resource management. Canberra: Cengage Australia; 2019.##8. Tang G, Chen Y, Jiang Y, Paille P, Jia J. Green human resource management practices: scale development and validity. Asia Pac J Hum Resour 2018; 56(1): 31-55.##9. Lincoln YS, Guba EG. But is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Dir Prog Eval 1986; 30: 73-84.##10. Danaee Fard H, Panahi B. An analysis of employee’s attitudes in public organizations: Explanation of organizational silence climate and silence behavior. Transform Manag J 2010; 2(3): 1-19 [In Persian]. ##11. Damghanian H, Rouzban F. Explaining employee silence in connection with the direct manager based on mixed method. Organ Behav Stud Quart 2015; 4(3): 194-75 [In Persian].##12. Farjam S, Almodarresi SM, Pirvali E, Saberi H, Malekpour S. The mediator effect of occupational burnout on the relationship between organizational cynicism and organizational silence (Case of study: employees of Farokhshahr social security organization hospital). Rev Publicando 2018; 5(15): 1136-59.##13. Beyran Nejad A, Davari E, Afkhami M. Organizational silence as a current challenge in human resource management: exploring the factors and consequences. Organ Behav Stud Quart 2017; 6(1): 147-76 [In Persian]. ##

Designing an Entrepreneurial Supply Chain Model during Disasters in Iran Designing an Entrepreneurial Supply Chain Model during Disasters in Iran 2 1 INTRODUCTION: Relief organizations, especially the Red Crescent, lack any specific entrepreneurial strategy and program for production, identification, and distribution of relief supplies. These organizations mainly focus on the preparation and distribution of supplies in times of crisis. In this regard, the present study aimed to design an entrepreneurial supply chain model with an emphasis on technology in 2020 in Iran. METHODS: The present study was conducted based on a qualitative and quantitative design. In the first phase, some indicators were obtained by observing the current situation and interviewing 30 experts. Following that, the final model was achieved by considering all indicators and categorizing the topics. In the Delphi process, experts' interviews and theoretical consensus suggested some hypotheses. In other words, in the second phase, structural equation modeling was used to finalize the model. In the next stage, the final questionnaire was provided to 186 Red Crescent employees. FINDINGS: After the analysis and extraction of the criteria from the interviews, components of the model were retrieved, and two questionnaires were designed. The first questionnaire was about supply chain management encompassing four main components of customer integrity, supplier integrity, internal integrity, and innovative orientation. The second questionnaire was related to technology, including seven components: personal characteristics, attitudinal factors, educational factors, technical factors, economic factors, environmental factors, as well as human and managerial factors. Considering the KMO value (˃0.7) and the significant value of the Bartlett Sphericity test, it can be concluded that the data are suitable for factor analysis. The model fit values all exceeded 0.9, indicating that the model has a “good fit. The path coefficients were significant for seven relationships at the level of 0.05. CONCLUSION: As evidenced by the obtained results, the supply chain in disasters requires experts' comprehensive approach and innovative perspectives. The tendency of countries to take innovative measures in disasters requires macro-policies at the national and regional levels. Therefore, all dimensions and aspects of the entrepreneurial supply chain in disasters must be considered in order to attain the final goal which is effective and efficient disaster management.   2 INTRODUCTION: Relief organizations, especially the Red Crescent, lack any specific entrepreneurial strategy and program for production, identification, and distribution of relief supplies. These organizations mainly focus on the preparation and distribution of supplies in times of crisis. In this regard, the present study aimed to design an entrepreneurial supply chain model with an emphasis on technology in 2020 in Iran. METHODS: The present study was conducted based on a qualitative and quantitative design. In the first phase, some indicators were obtained by observing the current situation and interviewing 30 experts. Following that, the final model was achieved by considering all indicators and categorizing the topics. In the Delphi process, experts' interviews and theoretical consensus suggested some hypotheses. In other words, in the second phase, structural equation modeling was used to finalize the model. In the next stage, the final questionnaire was provided to 186 Red Crescent employees. FINDINGS: After the analysis and extraction of the criteria from the interviews, components of the model were retrieved, and two questionnaires were designed. The first questionnaire was about supply chain management encompassing four main components of customer integrity, supplier integrity, internal integrity, and innovative orientation. The second questionnaire was related to technology, including seven components: personal characteristics, attitudinal factors, educational factors, technical factors, economic factors, environmental factors, as well as human and managerial factors. Considering the KMO value (˃0.7) and the significant value of the Bartlett Sphericity test, it can be concluded that the data are suitable for factor analysis. The model fit values all exceeded 0.9, indicating that the model has a “good fit. The path coefficients were significant for seven relationships at the level of 0.05. CONCLUSION: As evidenced by the obtained results, the supply chain in disasters requires experts' comprehensive approach and innovative perspectives. The tendency of countries to take innovative measures in disasters requires macro-policies at the national and regional levels. Therefore, all dimensions and aspects of the entrepreneurial supply chain in disasters must be considered in order to attain the final goal which is effective and efficient disaster management.   9 15 2020/12/92020/11/2 1399/8/12 2021/01/162021/01/3 1399/10/14 مجتبی اکبری Mojtaba Akbari Department of Entrepreneurship, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran Iran mojiakbari55@gmail.com حسین دیده خانی Hossein Didehkhani Assistant Professor, Department of Industries, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran Iran didehkhani@gmail.com سامره شجاعی Samereh Shojaei Department of Management, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran Iran احمد مهرابیان Ahmad Mehrabian Department of Industries, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran Iran Disasters Entrepreneurship Supply Chain Technology Disasters Entrepreneurship Supply Chain Technology References##1. Naderi N, Mohammadi J. Locating temporary housing after the earthquake, using GIS and AHPTechniques (A case study: 15 districts of Isfahan City). J Soc Issues Human 2015; 3(12): 71-5. (In Persian).##2. Asgari A. In the search of management and planning principals. International Congress of Crisis Management in Disasters, Tehran, Iran; 2007 (In Persian).##3. Yu M, Yang C, Li Y. Big data in natural disaster management: a review. Geosciences 2018; 8(5): 165.##4. Seeger MW, Ulmer RR, Novak JM, Sellnow T. Post-crisis discourse and organizational change, failure and renewal. J Organ Change Manag 2005; 18(1): 78-95.##5. Kusi-Sarpong S, Gupta H, Sarkis J. A supply chain sustainability innovation framework and evaluation methodology. Int J Prod Res 2019; 57(7): 1990-2008.##6. Liao SH, Hu DC, Ding LW. Assessing the influence of supply chain collaboration value innovation, supply chain capability and competitive advantage in Taiwan's networking communication industry. Int J Prod Econ 2017; 191: 143-53.##7. Seman NA, Govindan K, Mardani A, Zakuan N, Saman MZ, Hooker RE, et al. The mediating effect of green innovation on the relationship between green supply chain management and environmental performance. J Cleaner Product 2019; 229: 115-27.##8. Hahn GJ. Industry 4.0: a supply chain innovation perspective. Int J Product Res 2020; 58(5): 1425-41.##9. Heaslip G, Kovács G, Haavisto I. Innovations in humanitarian supply chains: the case of cash transfer programmes. Product Plan Control 2018; 29(14): 1175-90.##10. Wang M, Asian S, Wood LC, Wang B. Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era. Modern Supply Chain Res Appl 2020; 2(2): 83-98.##11. Braun V, Clarke V. Using thematic analysis in psychology. Qualit Res Psychol 2006; 3(2): 77-101.##12. Schwandt TA, Lincoln YS, Guba EG. Judging interpretations: but is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Direct Evaluat 2007; 2007(114): 11-25.##13. Altay N, Gunasekaran A, Dubey R, Childe SJ. Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view. Product Plan Control 2018; 29(14): 1158-74.##14. Nagurney A, Masoumi AH, Yu M. An integrated disaster relief supply chain network model with time targets and demand uncertainty. Regional Sci Matters 2015; 7(15): 287-318.##15. Candan G, Yazgan HR. A novel approach for inventory problem in the pharmaceutical supply chain. DARU J Pharm Sci 2016; 24(1): 1-16.##16. Mehralian G, Gatari AR, Morakabati M, Vatanpour H. Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies. Iran J Pharm Res 2012; 11(1): 209-19 (In Persian).##17. Kumar S, Havey T. Before and after disaster strikes: a relief supply chain decision support framework. Int J Product Econ 2013; 145(2): 613-29.##References##1. Naderi N, Mohammadi J. Locating temporary housing after the earthquake, using GIS and AHPTechniques (A case study: 15 districts of Isfahan City). J Soc Issues Human 2015; 3(12): 71-5. (In Persian).##2. Asgari A. In the search of management and planning principals. International Congress of Crisis Management in Disasters, Tehran, Iran; 2007 (In Persian).##3. Yu M, Yang C, Li Y. Big data in natural disaster management: a review. Geosciences 2018; 8(5): 165.##4. Seeger MW, Ulmer RR, Novak JM, Sellnow T. Post-crisis discourse and organizational change, failure and renewal. J Organ Change Manag 2005; 18(1): 78-95.##5. Kusi-Sarpong S, Gupta H, Sarkis J. A supply chain sustainability innovation framework and evaluation methodology. Int J Prod Res 2019; 57(7): 1990-2008.##6. Liao SH, Hu DC, Ding LW. Assessing the influence of supply chain collaboration value innovation, supply chain capability and competitive advantage in Taiwan's networking communication industry. Int J Prod Econ 2017; 191: 143-53.##7. Seman NA, Govindan K, Mardani A, Zakuan N, Saman MZ, Hooker RE, et al. The mediating effect of green innovation on the relationship between green supply chain management and environmental performance. J Cleaner Product 2019; 229: 115-27.##8. Hahn GJ. Industry 4.0: a supply chain innovation perspective. Int J Product Res 2020; 58(5): 1425-41.##9. Heaslip G, Kovács G, Haavisto I. Innovations in humanitarian supply chains: the case of cash transfer programmes. Product Plan Control 2018; 29(14): 1175-90.##10. Wang M, Asian S, Wood LC, Wang B. Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era. Modern Supply Chain Res Appl 2020; 2(2): 83-98.##11. Braun V, Clarke V. Using thematic analysis in psychology. Qualit Res Psychol 2006; 3(2): 77-101.##12. Schwandt TA, Lincoln YS, Guba EG. Judging interpretations: but is it rigorous? Trustworthiness and authenticity in naturalistic evaluation. New Direct Evaluat 2007; 2007(114): 11-25.##13. Altay N, Gunasekaran A, Dubey R, Childe SJ. Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view. Product Plan Control 2018; 29(14): 1158-74.##14. Nagurney A, Masoumi AH, Yu M. An integrated disaster relief supply chain network model with time targets and demand uncertainty. Regional Sci Matters 2015; 7(15): 287-318.##15. Candan G, Yazgan HR. A novel approach for inventory problem in the pharmaceutical supply chain. DARU J Pharm Sci 2016; 24(1): 1-16.##16. Mehralian G, Gatari AR, Morakabati M, Vatanpour H. Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies. Iran J Pharm Res 2012; 11(1): 209-19 (In Persian).##17. Kumar S, Havey T. Before and after disaster strikes: a relief supply chain decision support framework. Int J Product Econ 2013; 145(2): 613-29. ##

Thresholds of Environmental Physical Resilience of Tehran Metropolis Earthquake; Flood; Tehran; Urban Perspective; Resilience 2 1 Introduction The structure of the urban platform as well as its physical features, depending on the inherent conditions and behavior of environmental thresholds in relation to changes.  The purpose of this study is to determine the geomorphological landscapes of Tehran by two phenomena of earthquake and flood that have the highest risk in different periods of Tehran. Therefore, by focusing on the inherent characteristics and evolutionary trend of Tehran city landscape, It is divided into three landscapes; North, Central, and South. Methodology The relationship between the landscape of Tehran based on the form and geomorphological processes and the study of earthquake and flood hazards was obtained in four steps, which include: Data collection, data processing, calculation of indicators and analysis of findings. According to the characteristics of topography, physiography, geology, the results of field studies and satellite images, aerial photographs and also paleogeomorphological research in Tehran, the study area aims to determine the resilience thresholds of the city to three northern urban landscapes, The center and south were divided. Results and discussion Based on the zoning map of Tehran based on the earthquake phenomenon in the three landscapes of north, center and south, the highest distribution of non-resilience is in the northern and southern regions of the city. Northeastern, southwestern, and semi-western regions have the highest urban resilience to earthquake. Based on the zoning of Tehran based on the flood phenomenon in the three landscapes of north, center and south, the highest distribution of non-resilience is in the northern areas of the city. Northeast, southwest, and west have the highest urban resilience to floods. Conclusion According to the present study, in general, the city of Tehran in order to increase its resilience to earthquake and flood hazards should be studied not in one landscape but in different landscapes 2 INTRODUCTION: The structure of the urban platform of Tehran and its physical characteristics depends on the inherent conditions and environmental thresholds in relation to changes. This study aimed to determine the natural landscapes of Tehran by two phenomena of earthquake and flood that posed the highest risk in different periods of this city. Therefore, the natural perspective of Tehran is divided into three perspectives of north, central, and south regarding the inherent features and evolutionary process. METHODS: The relationship between the perspective of Tehran based on the form and geomorphological processes and the evaluation of earthquake and flood hazards have been observed in four stages, which included data collection, data processing, calculation of indicators, and analysis of findings. The studied area was divided into three northern, central, and south urban landscapes to determine the resistance thresholds of the city according to the characteristics of topography, physiography, geology, the results of field studies and satellite images, aerial photographs, as well as paleogeomorphological research in Tehran. FINDINGS: According to the zoning map of Tehran based on the earthquake phenomenon in three perspectives of north, center, and south, the highest distribution of non-resistance is observed in the northern and southern areas of the city. Northeast, southwest, and semi-western regions have the highest urban resilience to earthquakes. Moreover, regarding the zoning of Tehran based on the flood phenomenon in the three perspectives of north, center, and south, the highest distribution of non-resilience has been observed in the northern regions of the city. Northeast, southwest, and west of Tehran have the highest urban flood resilience. CONCLUSION: Based on the results of the present study, in order to increase resilience against the risks of earthquakes and floods, the city of Tehran should be studied not in just one perspective but in different perspectives. 16 30 2020/12/92020/11/22020/11/17 1399/8/27 2021/01/162021/01/32021/03/14 1399/12/24 اسماعیل عبدلی Ismail Abdoli Geomorphology, Faculty of Earth Sciences, Shahid Beheshti University of Tehran, Tehran, Iran Iran es.abdoli@yahoo.com منیژه قهرودی تالی Manijeh Ghahroudi Tali Geomorphology, Faculty of Earth Sciences, Shahid Beheshti University of Tehran, Tehran, Iran Iran m-ghahroudi@sbu.ac.ir جمیله توکلی نیا Jamileh TavakoliNia Geography and Urban Planning, Faculty of Earth Sciences, Shahid Beheshti University of Tehran, Tehran, Iran Iran j_tavakolinia@sbu.ac.ir Tehran urban landscape earthquake flood resilience Earthquake Flood Tehran Urban Perspective Resilience 1. Dadashpoor H, Adeli Z. Measuring the amount of regional resilience in Qazvin urban region. 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Investigation of geomorphological limitations and its effect on physical growth of the city using GIS and AHP (Case study: Givi city). Quantit Geomorphol Res 2015; 4: 1-16[In Persian].##19. Maghsoudi M, Nayyeri H, Amani K. Hydrodynamics and stability of the Gheshlagh river and its effect on urban development in Sanandaj. Quantit Geomorphol Res 2018; 5(2): 66-81[In Persian].##20. Parvin M. Evaluation of the interaction between geomorphologic and urban development conditions (Case study: Kermanshah city). Quantit Geomorphol Res 2019; 7(3): 231-44 [In Persian].##21. Aslani F, Amini-Hosseini K, Fallahi A. Evaluation of physical resilience of Karaj City, Iran, against earthquake. Sci J Rescue Reli 2019; 11: 63-71.##22. Rahimi F, Sadeghi Niaraki A, Qodousi, M. Modeling physical-social resilience in district 1 of Tehran. Quart Sci J Rescue Reli 2020; 12)1(: 46-56 [In Persian].##23. Mohammadkhani M, Karkehabadi Z, Arghan A. Resilience assessment of Semnan, Iran, in the face of an earthquake. 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Assessment of urban resilience against earthquake by using promethee model, case study: district 1 of Tehran Municipality. J Urban Ecol Res 2019; 10(20): 127-40.‌##36. Hosseini A, Fatahiyan SA, Malakan J. Spatial analysis of safe areas based of earthquake risk using multi-criteria decision making and fuzzy logic (The case study on district 20 of Tehran). J Environ Sci Technol 2020; 22(1): 151-66.##37. ‌Pourahmad A, Ziyari K, Sadeghi A. Spatial analysis of physical resilience components of urban attrited/beaten tissues against earthquakes (Case study: district 10 of Tehran Municipality).‌ J Spatial Plan 2018; 8(1): 110-30.‌##38. Derafshi K. The flood risk changes effective factors in Tehran Metropolis. J Spatial Analysis Environ Hazarts 2020; 7(3): 125-46.##39. Moghadas M, Asadzadeh A, Vafeidis A, Fekete A, Kötter T. A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. Int J Disast Risk Reduct 2019; 35: 101069.‌##40. Masnavi MR, Gharai F, Hajibandeh M. Exploring urban resilience thinking for its application in urban planning: a review of literature. Int J Environ Sci Technol 2019; 16(1): 567-82.‌##41. Cariolet J M, Vuillet M, Diab Y. Mapping urban resilience to disasters–A review. Sustainable Cities Soc 2019; 51: 101746.‌##1. Dadashpoor H, Adeli Z. Measuring the amount of regional resilience in Qazvin urban region. J Emerg Manag 2016; 4(2): 73-84 [In Persian].##2. Chen C, Xu L, Zhao D, Xu T, Lei P. A new model for describing the urban resilience considering adaptability, resistance and recovery. Saf Sci 2020; 128: 104756. ##3. Grafakos S, Gianoli A, Tsatsou A. Towards the development of an integrated sustainability and resilience benefits assessment framework of urban green growth interventions. Sustainability 2016; 8(5): 461.‌##4. Khorshiddoust AM, Rezaeimoghaddam M, Ahmadi M, Khaleghi S. The role of river geomorphic processes in environmental hazards of Songhor in Kermanshah province. Geographic Space 2011; 35: 209-34 [In Persian].##5. Uday P, Marais KB. Resilience-based system importance measures for system-of-systems. Proc Comput Sci 2014; 28: 257-64.‌##6. Lundberg J, Johansson BJ. Systemic resilience model. Reliabil Engin Syst Saf 2015; 141: 22-32.‌##7. Dessavre DG, Ramirez-Marquez JE, Barker K. Multidimensional approach to complex system resilience analysis. Reliabil Eng Syst Saf 2016; 149: 34-43.‌##8. Cutter SL. The landscape of disaster resilience indicators in the USA. Natl Hazards 2016; 80(2): 741-58.##9. Cutter SL, Burton CG, Emrich CT. Disaster resilience indicators for benchmarking baseline conditions. J Homeland Security Emerg Manag 2010; 7(1): 51.##10. Bastaminia A, Rezaie MR, Saraie MH. Explaining and analyzing the concept of resiliency and its indicators and frameworks in natural disasters. Disast Prev Manag Knowl 2016; 6(1): 32-46 [In Persian].##11. Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E, et al. A place-based model for understanding community resilience to natural disasters. Global Environ Change 2008; 18(4): 598-606.##12. Sharifi A, Yamagata Y. Resilient urban planning major principles and criteria. Energy Proc 2014; 61: 1491-5.‌##13. Amini Hosseini K, Hosseini M, Jafari MK, Hosseinioon S. Recognition of vulnerable urban fabrics in earthquake zones: a case study of the Tehran metropolitan area. J Seismol Earthquake Eng 2020; 10(4): 175-87 [In Persian].##14. Rezaie MR, Rafieian M, Hosseini SM. Measurement and evaluation of physical resilience of urban communities against earthquake (Case study: Tehran neighborhoods). Hum Geography Res 2015; 47(4): 609-23 [In Persian].##15. Hosseini KA, Hosseinioon S. A survey on the parameters affecting the vulnerability of urban fabrics to earthquake in Tehran. 15th World Conference on Earthquake Engineering (15WCEE), Lisbon, Portugal; 2012. P. 24-8.##16. Eshgi A, Nazmfar H, Jafari A. Assessing the physical resilience of a city against possible earthquakes (Case Study: region one of Tehran). Phys Sac Plan 2018; 4(4): 11-26 [In Persian].##17. Ghahroudi Tali M, Pourmousavi SM, Khosravi S. Study of potential seismicity damage by multi attribute decision making (Case study: district 1 of Tehran). Quant Geomorphol Res 2013; 1(3): 57-68 [In Persian].##18. Setayeshi Nasaz H, Rostai SH, Omranidorbash M, Zarepishe M. Investigation of geomorphological limitations and its effect on physical growth of the city using GIS and AHP (Case study: Givi city). Quantit Geomorphol Res 2015; 4: 1-16[In Persian].##19. Maghsoudi M, Nayyeri H, Amani K. Hydrodynamics and stability of the Gheshlagh river and its effect on urban development in Sanandaj. Quantit Geomorphol Res 2018; 5(2): 66-81[In Persian].##20. Parvin M. Evaluation of the interaction between geomorphologic and urban development conditions (Case study: Kermanshah city). Quantit Geomorphol Res 2019; 7(3): 231-44 [In Persian].##21. Aslani F, Amini-Hosseini K, Fallahi A. Evaluation of physical resilience of Karaj City, Iran, against earthquake. Sci J Rescue Reli 2019; 11: 63-71.##22. Rahimi F, Sadeghi Niaraki A, Qodousi, M. Modeling physical-social resilience in district 1 of Tehran. Quart Sci J Rescue Reli 2020; 12)1(: 46-56 [In Persian].##23. Mohammadkhani M, Karkehabadi Z, Arghan A. Resilience assessment of Semnan, Iran, in the face of an earthquake. Quart Sci J Rescue Reli 2020; 12)3(: 217-26 [In Persian].##24. Ramesht MH. Chaos theory in geomorphology. Geography Dev Iran J 2003; 1(1): 13-37 [In Persian]. ##25. Ahern J. Urban landscape sustainability and resilience: the promise and challenges of integrating ecology with urban planning and design. Landscape Ecol 2013; 28(6): 1203-12.‌ ##26. Zare S, Hosseini F. The prevalence of urban areas vulnerability to seismic risk (a case study of region one, Tehran). Iran Univ Sci Technol 2017; 27(2): 153-60.##27. Hyogo framework for action 2005-2015: building the resilience of nations and communities to disasters. World Conference on Disaster Reduction, Hyogo, Japan; 2005.##28. Pearson L, Pelling M. The UN Sendai framework for disaster risk reduction 2015–2030: Negotiation process and prospects for science and practice. J Extreme Events 2015; 2(01): 1571001.##29. Tehran Master Plan. Ministry of roads & urban development Islamic Republic of Iran. Tehran: Tehran Municipality; 2006 [In Persian].##30. The third 5-year plan. Tehran: Urban Renewal Organization of Tehran; 2019 [In Persian].##31. Ahmadabadi A, Saberi M. An application of quantitative geomorphometric indicators in identifying ‘[Susceptible Zones by Using SVM Model (Case study: Khorramabad-Pol Zal Freeway). Quanti Geomorphol Res 2016; 3: 197-213. ##32. Diao C. An approach to theory and methods of urban geomorphology. Chin Geographical Sci 1995; 6(1): 88-95.‌##33. Arc Map 10.3. ArcGIS Desktop. Available at: URL:https://desktop.arcgis.com/en/arcmap/10.3/main/ get-started/whats-new-in-arcgis-1031.htm; 2020. ##34. Pashapoor H, Pourakrami M. Measuring physical dimensions of urban resilience in the face of the natural disasters (Earthquake) (Case study: Tehran's 12th District). J Stud Hum Settlements Plan 2018; 12(4): 985-1001.‌##35. Eshghei A, Nazmfar H. Assessment of urban resilience against earthquake by using promethee model, case study: district 1 of Tehran Municipality. J Urban Ecol Res 2019; 10(20): 127-40.‌##36. Hosseini A, Fatahiyan SA, Malakan J. Spatial analysis of safe areas based of earthquake risk using multi-criteria decision making and fuzzy logic (The case study on district 20 of Tehran). J Environ Sci Technol 2020; 22(1): 151-66.##37. ‌Pourahmad A, Ziyari K, Sadeghi A. Spatial analysis of physical resilience components of urban attrited/beaten tissues against earthquakes (Case study: district 10 of Tehran Municipality).‌ J Spatial Plan 2018; 8(1): 110-30.‌##38. Derafshi K. The flood risk changes effective factors in Tehran Metropolis. J Spatial Analysis Environ Hazarts 2020; 7(3): 125-46.##39. Moghadas M, Asadzadeh A, Vafeidis A, Fekete A, Kötter T. A multi-criteria approach for assessing urban flood resilience in Tehran, Iran. Int J Disast Risk Reduct 2019; 35: 101069.‌##40. Masnavi MR, Gharai F, Hajibandeh M. Exploring urban resilience thinking for its application in urban planning: a review of literature. Int J Environ Sci Technol 2019; 16(1): 567-82.‌##41. Cariolet J M, Vuillet M, Diab Y. Mapping urban resilience to disasters–A review. Sustainable Cities Soc 2019; 51: 101746.‌ ##

Designing a Model of Organizational Citizenship Behavior from a Social Perspective in the Iranian Red Crescent Society Designing a Model of Organizational Citizenship Behavior from a Social Perspective in the Iranian Red Crescent Society 2 1 INTRODUCTION: A good organizational citizen is a thought and idea that includes various behaviors of employees such as accepting and assuming additional duties and responsibilities, following organizational rules and procedures, maintaining and developing a positive attitude, being patient, and tolerating dissatisfaction and problems in the workplace. The increase in the level of organizational citizenship behavior (OCB) in the organization makes the organization an attractive environment for work. In other words, the desired level of OCBs affects the improvement of the performance of employees and, in general, the organization. The Iranian Red Crescent Society is one of the human-centered organizations, and observing the indicators of citizenship behavior is one of the effective factors in the success of its performance. Therefore, this study aimed to investigate the OCB from a social perspective in the Iranian Red Crescent Society. METHODS: The present applied study was conducted based on an exploratory qualitative approach. The statistical population of this study consisted of all managers and employees of the Iranian Red Crescent Society. The required data were collected using in-depth semi-structured interviews, which reached saturation after holding 14 interviews. To analyze the data, content analysis, meta-synthesis, and fuzzy Delphi methods using grounded theory were applied. The MAXQDA software (version 10) was used in the theory analysis process. FINDINGS: In this study, the results were classified into 78 concepts, 14 sub-criteria, and 2 main criteria, including the dimensions of citizenship behavior and the consequences of citizenship behavior. The most important dimensions of OCB consisted of the categories of helpful behaviors, individual creativity, organizational obedience, organizational loyalty, chivalry, civic virtue, and personal growth. CONCLUSION: The results of data analysis showed that the categories of increasing performance productivity and effectiveness, promoting positive relationships among employees, boosting efficiency in resource allocation, reducing maintenance costs, creating the necessary flexibility for innovation, improving customer service, using rare resources effectively were the most important consequences of observing OCB from a social perspective in the IRCS. 2 INTRODUCTION: A good organizational citizen is a thought and idea that includes various behaviors of employees such as accepting and assuming additional duties and responsibilities, following organizational rules and procedures, maintaining and developing a positive attitude, being patient, and tolerating dissatisfaction and problems in the workplace. The increase in the level of organizational citizenship behavior (OCB) in the organization makes the organization an attractive environment for work. In other words, the desired level of OCBs affects the improvement of the performance of employees and, in general, the organization. The Iranian Red Crescent Society is one of the human-centered organizations, and observing the indicators of citizenship behavior is one of the effective factors in the success of its performance. Therefore, this study aimed to investigate the OCB from a social perspective in the Iranian Red Crescent Society. METHODS: The present applied study was conducted based on an exploratory qualitative approach. The statistical population of this study consisted of all managers and employees of the Iranian Red Crescent Society. The required data were collected using in-depth semi-structured interviews, which reached saturation after holding 14 interviews. To analyze the data, content analysis, meta-synthesis, and fuzzy Delphi methods using grounded theory were applied. The MAXQDA software (version 10) was used in the theory analysis process. FINDINGS: In this study, the results were classified into 78 concepts, 14 sub-criteria, and 2 main criteria, including the dimensions of citizenship behavior and the consequences of citizenship behavior. The most important dimensions of OCB consisted of the categories of helpful behaviors, individual creativity, organizational obedience, organizational loyalty, chivalry, civic virtue, and personal growth. CONCLUSION: The results of data analysis showed that the categories of increasing performance productivity and effectiveness, promoting positive relationships among employees, boosting efficiency in resource allocation, reducing maintenance costs, creating the necessary flexibility for innovation, improving customer service, using rare resources effectively were the most important consequences of observing OCB from a social perspective in the IRCS. 31 41 2020/12/92020/11/22020/11/172021/01/3 1399/10/14 2021/01/162021/01/32021/03/142021/03/30 1400/1/10 مهدی وکیلیان Mehdi Vakilian Sayyah Organizational Behavior, Department of Public Administration, Faculty of Literature and Humanities, Islamic Azad University, Chalous Branch, Mazandaran, Iran Iran vakilianm953@yahoo.com حسنعلی آقاجانی Hassan Ali Aghajani Department of Public Management, Mazandaran University, Mazandaran, Iran Iran aghajani@umz.ac.ir قربانعلی آقااحمدی Ghorban Ali Agha Ahmadi Department of Public Administration, Faculty of Literature and Humanities, Islamic Azad University, Chalous Branch, Mazandaran, Iran Iran ahmadyali33@yahoo.com Maryam Rahmaty Maryam Rahmaty Department of Public Administration, Faculty of Literature and Humanities, Islamic Azad University, Chalous Branch, Mazandaran, Iran Iran Citizenship Behavior Content Analysis Fuzzy Delphi Technique Organization Red Crescent Citizenship Behavior Content Analysis Fuzzy Delphi Technique Organization Red Crescent 1. Owor, JJ. (2016). Human Resource Management Practices, Employee Engagement and Organi-zational Citizenship Behaviors in Selected Firms in Uganda. African Journal of Business Management 2016; 10(1): 1-12.##2. Park GR, Moon GW, Hyun SE. An impact of self-leadership on innovative behavior in sports educators and understanding of advanced research. The SIJ Transactions on Industrial, Financial, & Business Management 2014; 2(3): 117-22. ##3. Eslami S .The relationship between transfor-mational leadership and job motivation of employees in the branches of Tejarat Bank in Isfahan [Master Thesis]. 2016.##4. Qiu X, Yan X, Lv Y. The effect of psychological capital and knowledge sharing on innovation performance for professional technical employees. Journal of Service Science and Management. 2015; 8 (04): 545.##5. Payam M, Bashlideh K, Hashemi S, Naami Abdolzahra. Study of the effect of spiritual intelligence and appreciation on organizational citizenship behavior and anti-production behavior; the mediating role of organizational commitment and job motivation in the staff of Shahid Chamran University of Ahvaz. Knowledge and Research in Applied Psychology. 20(2): 1-12. [In Persian].##6. Aghajani M, Mahdad A. The Impact of Transformational Leadership on Organizational Citizenship Behavior and Innovative Behaviors in Isfahan Azad University Staff: The Mediating Role of Job Attention. Knowledge and Research in Applied Psychology 2018. 20(1): 35-46. [In Persian].##7. Hosseini SS, Senobar N. The Effect of High Performance Human Resource Activities on Commitment and Organizational Citizenship Behavior of Public Employees. Economic Sociology and Development 2019; 8(1): 103-129. [In Persian].##8. Salman S, Mahmood A, Aftab F, and Mahmood A. Impact of Safety Health Environment on Employee Retention in Pharmaceutical Industry: Mediating Role of Job Satisfaction and Motivation. IBT Journal of Business Studies (JBS) 2016.12(1): 185-197. ##9. Sekhar C, Patwardhan M, Vyas V. A study of HR flexibility and firm performance: a perspective from IT industry. Global Journal of Flexible Systems Management 2016. 17(1):57-75. ##10. Swaminathan S, Jawahar PD. Job satisfaction as a predictor of organizational citizenship behavior: An empirical study. Global journal of Business Research 2013. 7(1): 71-80. ##11. Úbeda-García M, Claver-Cortés E, Marco-Lajara B, Zaragoza-Sáez P. Human resource flexibility and performance in the hotel industry. Personnel Review 2017. 46(4).##1. Owor, JJ. (2016). Human Resource Management Practices, Employee Engagement and Organi-zational Citizenship Behaviors in Selected Firms in Uganda. African Journal of Business Management 2016; 10(1): 1-12.##2. Park GR, Moon GW, Hyun SE. An impact of self-leadership on innovative behavior in sports educators and understanding of advanced research. The SIJ Transactions on Industrial, Financial, & Business Management 2014; 2(3): 117-22. ##3. Eslami S .The relationship between transfor-mational leadership and job motivation of employees in the branches of Tejarat Bank in Isfahan [Master Thesis]. 2016.##4. Qiu X, Yan X, Lv Y. The effect of psychological capital and knowledge sharing on innovation performance for professional technical employees. Journal of Service Science and Management. 2015; 8 (04): 545.##5. Payam M, Bashlideh K, Hashemi S, Naami Abdolzahra. Study of the effect of spiritual intelligence and appreciation on organizational citizenship behavior and anti-production behavior; the mediating role of organizational commitment and job motivation in the staff of Shahid Chamran University of Ahvaz. Knowledge and Research in Applied Psychology. 20(2): 1-12. [In Persian].##6. Aghajani M, Mahdad A. The Impact of Transformational Leadership on Organizational Citizenship Behavior and Innovative Behaviors in Isfahan Azad University Staff: The Mediating Role of Job Attention. Knowledge and Research in Applied Psychology 2018. 20(1): 35-46. [In Persian].##7. Hosseini SS, Senobar N. The Effect of High Performance Human Resource Activities on Commitment and Organizational Citizenship Behavior of Public Employees. Economic Sociology and Development 2019; 8(1): 103-129. [In Persian].##8. Salman S, Mahmood A, Aftab F, and Mahmood A. Impact of Safety Health Environment on Employee Retention in Pharmaceutical Industry: Mediating Role of Job Satisfaction and Motivation. IBT Journal of Business Studies (JBS) 2016.12(1): 185-197. ##9. Sekhar C, Patwardhan M, Vyas V. A study of HR flexibility and firm performance: a perspective from IT industry. Global Journal of Flexible Systems Management 2016. 17(1):57-75. ##10. Swaminathan S, Jawahar PD. Job satisfaction as a predictor of organizational citizenship behavior: An empirical study. Global journal of Business Research 2013. 7(1): 71-80. ##11. Úbeda-García M, Claver-Cortés E, Marco-Lajara B, Zaragoza-Sáez P. Human resource flexibility and performance in the hotel industry. Personnel Review 2017. 46(4). ##

Investigation of Traffic Accident Hotspots in Yazd City using GIS Investigation of Traffic Accident Hotspots in Yazd City using GIS 2 1 INTRODUCTION: The importance of safety and prevention of traffic accidents have highlighted the necessity of research and investigation in this field. The present study aimed to identify and eliminate the risk factors underlying the occurrence of inner-city traffic accidents. In so doing, scientific and effective solutions can be provided at the lowest cost to implement the slogan of "prevention is better than cure". METHODS: In this study, the accident rate-severity index method was used to identify traffic accident hotspots. The spatial units in which this index (I) was more than twice the average of the total spatial units were identified as Hot Zones FINDINGS: In the present study, the study area (6,600 meters) was divided into 66 100-meter spatial units. Using the accident rate-severity method, 6 (600 m) and 19 (1900 m) spatial units were identified as Hot Zone (9.1%) and Yellow Zone (28.8%), respectively. Finally, 41 Cold Zone spatial units (62.1%) were identified (4,100 m). CONCLUSION: As evidenced by the obtained results, the following measures can be effective in the reduction of road accidents: retrospection of traffic signs in terms of number, size, location, height, installation of speed bumps in the Hot Zone and Yellow Zone spatial units, timely pruning of trees along the road-construction of underpasses and overpasses in some important points of the route (accident hotspots and Yellow Zone). Furthermore, the development of a comprehensive and long-term educational program to improve traffic safety culture in kindergartens and schools can be effective in reducing road accidents. 2 INTRODUCTION: The importance of safety and prevention of traffic accidents have highlighted the necessity of research and investigation in this field. The present study aimed to identify and eliminate the risk factors underlying the occurrence of inner-city traffic accidents. In so doing, scientific and effective solutions can be provided at the lowest cost to implement the slogan of "prevention is better than cure". METHODS: In this study, the accident rate-severity index method was used to identify traffic accident hotspots. The spatial units in which this index (I) was more than twice the average of the total spatial units were identified as Hot Zones FINDINGS: In the present study, the study area (6,600 meters) was divided into 66 100-meter spatial units. Using the accident rate-severity method, 6 (600 m) and 19 (1900 m) spatial units were identified as Hot Zone (9.1%) and Yellow Zone (28.8%), respectively. Finally, 41 Cold Zone spatial units (62.1%) were identified (4,100 m). CONCLUSION: As evidenced by the obtained results, the following measures can be effective in the reduction of road accidents: retrospection of traffic signs in terms of number, size, location, height, installation of speed bumps in the Hot Zone and Yellow Zone spatial units, timely pruning of trees along the road-construction of underpasses and overpasses in some important points of the route (accident hotspots and Yellow Zone). Furthermore, the development of a comprehensive and long-term educational program to improve traffic safety culture in kindergartens and schools can be effective in reducing road accidents. 42 48 2020/12/92020/11/22020/11/172021/01/32020/11/16 1399/8/26 2021/01/162021/01/32021/03/142021/03/302021/01/31 1399/11/12 علیرضا توکلی مهر Alireza Tavakoli Mehr Occupational Health Engineering, School of Health, Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Iran Iran tavakolimehr@yahoo.com مجتبی سلیمی Mojtaba Salimi Department of Geography and Urban Planning, University of Tehran, Tehran, Iran Iran halvanigh@yahoo.com Gholam Hossein حلوانی Gholam Hossein Halvani Department of Occupational Health Engineering, School of Health, Shahid Sadoughi University of Medical Sciences and Health Services, Yazd, Iran Iran Mahmoudreza Peyravi Mahmoudreza Peyravi Department of Health in Disasters and Emergencies, School of Management and Medical Information, Shiraz University of Medical Sciences, Fars, Iran Iran حکمت مرادی Hekmatollah Moradi Department of Health in Disasters and Emergencies, School of Management and Medical Information, Shiraz University of Medical Sciences, Fars, Iran Iran morad2063@yahoo.com میلاد احمدی مرزاله Milad Ahmadi Marzaleh Department of Health in Disasters and Emergencies, School of Management and Medical Information, Shiraz University of Medical Sciences, Fars, Iran Iran miladahmadimarzaleh@yahoo.com Rate-Severity Index Hot Zones Spatial Unit Rate-Severity Index Hot Zones Spatial Unit 1. Shafabakhsh GA, Famili A, Bahadori MS. GIS-based spatial analysis of urban traffic accidents: case study in Mashhad, Iran. J Traffic Transport Eng 2017; 4(3): 290-9 (In Persian).##2. Durduran SS. A decision making system to automatic recognize of traffic accidents on the basis of a GIS platform. Expert Syst Appl 2010; 37(12): 7729-36.##3. Iyanda AE. Geographic analysis of road accident severity index in Nigeria. Int J Inj Control Saf Prom 2019; 26(1): 72-81.##4. Khani F, Samsam Shariat S, Atashpour S. Study of the relationship between personality traits with occupational accidents and quality of sleep among road drivers in Isfahan city (Year 1390). Strategic Res Soc Problems Iran Univ Isfahan 2013; 1(4): 75-88 (In Persian).##5. Loo BP. The identification of hazardous road locations: a comparison of the blacksite and hot zone methodologies in Hong Kong. Int J Sustainable Transport 2009; 3(3): 187-202.##6. Goswami D, Morshed MM, Hasan MS. Implication of GIS technology in accident research in Bangladesh. J Bangladesh Instit Plan 2015; 8: 159-66.##7. Zeller KA, Wattles DW, DeStefano S. Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA. Environ Manag 2018; 62(3): 518-28.##8. Rahmani M. Zoning of road accident-prone with determine the black spots by using GIS (case study: malayer-hamedan road). Environ Based Territorial Plan 2016; 9(34): 155-75 (In Persian).##9. Kavousi A, Moradi A, Rahmani K, Zeini S, Ameri P. Geographical distribution of at fault drivers involved in fatal traffic collisions in Tehran, Iran. Epidemiol Health 2020; 42: e2020002 (In Persian).##10. Meeker J, Perry A, Dolan C, Emary C, Golden K, Abla C, et al. Development of a competency framework for the nutrition in emergencies sector. Public Health Nutr 2014; 17(3): 689-99.##11. AFRC Engineering. Criteria for determining the incident points of the city. Tehran: Transportation and Traffic Department of Tehran Municipality; 2012.##12. Hoshyar H, Sharifi B. Spatial analysis of intra-city accidents (Case study: Uromia city). J Geographical Eng Territory 2017; 1(1): 90-101 (In Persian).##1. Shafabakhsh GA, Famili A, Bahadori MS. GIS-based spatial analysis of urban traffic accidents: case study in Mashhad, Iran. J Traffic Transport Eng 2017; 4(3): 290-9 (In Persian).##2. Durduran SS. A decision making system to automatic recognize of traffic accidents on the basis of a GIS platform. Expert Syst Appl 2010; 37(12): 7729-36.##3. Iyanda AE. Geographic analysis of road accident severity index in Nigeria. Int J Inj Control Saf Prom 2019; 26(1): 72-81.##4. Khani F, Samsam Shariat S, Atashpour S. Study of the relationship between personality traits with occupational accidents and quality of sleep among road drivers in Isfahan city (Year 1390). Strategic Res Soc Problems Iran Univ Isfahan 2013; 1(4): 75-88 (In Persian).##5. Loo BP. The identification of hazardous road locations: a comparison of the blacksite and hot zone methodologies in Hong Kong. Int J Sustainable Transport 2009; 3(3): 187-202.##6. Goswami D, Morshed MM, Hasan MS. Implication of GIS technology in accident research in Bangladesh. J Bangladesh Instit Plan 2015; 8: 159-66.##7. Zeller KA, Wattles DW, DeStefano S. Incorporating road crossing data into vehicle collision risk models for moose (Alces americanus) in Massachusetts, USA. Environ Manag 2018; 62(3): 518-28.##8. Rahmani M. Zoning of road accident-prone with determine the black spots by using GIS (case study: malayer-hamedan road). Environ Based Territorial Plan 2016; 9(34): 155-75 (In Persian).##9. Kavousi A, Moradi A, Rahmani K, Zeini S, Ameri P. Geographical distribution of at fault drivers involved in fatal traffic collisions in Tehran, Iran. Epidemiol Health 2020; 42: e2020002 (In Persian).##10. Meeker J, Perry A, Dolan C, Emary C, Golden K, Abla C, et al. Development of a competency framework for the nutrition in emergencies sector. Public Health Nutr 2014; 17(3): 689-99.##11. AFRC Engineering. Criteria for determining the incident points of the city. Tehran: Transportation and Traffic Department of Tehran Municipality; 2012.##12. Hoshyar H, Sharifi B. Spatial analysis of intra-city accidents (Case study: Uromia city). J Geographical Eng Territory 2017; 1(1): 90-101 (In Persian). ##

Climate Change Effects Management with the Approach of the Uncertainty of Atmosphere-Ocean General Circulation Models in Hamedan Province, Iran Climate Change Effects Management with the Approach of the Uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran 2 1 INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively. 2 INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively. 49 60 2020/12/92020/11/22020/11/172021/01/32020/11/162020/10/3 1399/7/12 2021/01/162021/01/32021/03/142021/03/302021/01/312021/02/7 1399/11/19 مجید احمدی Majid Ahmadi Agricultural Meteorology, Kish International Campus, University of Tehran, Tehran, Iran Iran ahmadimajid21@yahoo.com قاسم عزیزی Ghasem Azizi Professor, Climatology, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Iran ghazizi@ut.ac.ir سعید بازگیر Saeed Bazgir Agricultural Meteorology, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran Iran sbazgeer@ut.ac.ir محمد همتی Mohammad Hemmati Department of Physical Geography, Islamic Azad University, Imam Khomeini Memorial Branch, Shahr-e-Rey, Tehran, Iran Iran moh_hemmati2051@yahoo.com Crisis management Climate change Hamedan province AOGCMs Uncertainty Crisis management Climate change Hamedan province AOGCMs Uncertainty 1. Prudhomme C, Davies H. Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate. Clim Change 2009; 93(1): 197-222. ##2. Wilby RL, Harris I. A framework for assessing uncertainties in climate change impacts: low‐flow scenarios for the River Thames, UK. Water Resour Res 2006; 42(2): 1-10.##3. Wang H, Xiao W, Wang Y, Zhao Y, Lu F, Yang M, et al. Assessment of the impact of climate change on hydropower potential in the Nanliujiang river basin of China. Energy 2019; 167: 950-9. ##4. Türkeş M, Sümer UM, Demi̇r İ. Re‐evaluation of trends and changes in mean, maximum and minimum temperatures of Turkey for the period 1929–1999. Int J Climatol 2002; 22(8): 947-77.##5. Ashraf B, Alizadeh A, Mousavi Baygi M, Bannayan Aval M. Verification of temperature and precipitation simulated data by individual and ensemble performance of five AOGCM models for North East of Iran. Water Soil 2014; 28(2): 253-66 [In Persian]. ##6. Jahanbakhsh Asl S, Khorshiddoust A, Alinejad MH, Pourasghar F. Impact of climate change on precipitation and temperature by taking the uncertainty of models and climate scenarios (case study: Shahr Chay basin in Urmia). Hydro geomorphology 2016; 2(7): 107-22. [In Persian]. ##7. Ghandi A, Seyedian SM. Uncertainty analysis of rainfall projections (case study: Bojnourd and Mashhad synoptic gauge station). J Water Soil Conservat 2017; 24(1): 189-204. [In Persian]. ##8. Khazaei MR, Khazaei H. Scenarios in climate change impact assessment on monthly stream-flow of Karun Basin. J Environ Sci Technol 2018; 20(1): 29-40. [In Persian]. ##9. Taban H, Zohrabi N, Nikbakht SA. Uncertainty assessment of GCM models for estimating rainfall and runoff of Dez Ulya basin under climate change. J Earth Space Phys 2018; 44: 89-102. [In Persian]. ##10. Wilby RL, Whitehead PG, Wade AJ, Butterfield D, Davis RJ, Watts G. Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK. J Hydrol 2006; 330(1-2): 204-20. ##11. Cameron D. An application of the UKCIP02 climate change scenarios to flood estimation by continuous simulation for a gauged catchment in the northeast of Scotland, UK (with uncertainty). J Hydrol 2006; 328(1-2): 212-26.##12. Graham LP, Hagemann S, Jaun S, Beniston M. On interpreting hydrological change from regional climate models. Clim Change 2007; 81(1): 97-122.##13. Fowler HJ, Ekström M. Multi‐model ensemble estimates of climate change impacts on UK seasonal precipitation extremes. Int J Climatol 2009; 29(3): 385-416. ##14. Chilkoti V, Bolisetti T, Balachandar R. Climate change impact assessment on hydropower generation using multi-model climate ensemble. Renewable Energy 2017; 109: 510-7. ##15. Naz BS, Kao SC, Ashfaq M, Gao H, Rastogi D, Gangrade S. Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States. J Hydrol 2018; 556: ##359-70. ##16. Wang GQ, Zhang JY, Xu YP, Bao ZX, Yang XY. Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios. Water Sci Eng 2017; 10(2): 87-96. ##17. Thompson JR, Laizé CL, Green AJ, Acreman MC, Kingston DG. Climate change uncertainty in environmental flows for the Mekong River. Hydrol Sci J 2014; 59(3-4): 935-54. ##18. Xu K, Xu B, Ju J, Wu C, Dai H, Hu BX. Projection and uncertainty of precipitation extremes in the CMIP5 multimodel ensembles over nine major basins in China. Atmos Res 2019; 226: 122-37. ##19. Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati ME, Barati MA, et al. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agr Water Manag 2011; 100(1): 1-8. ##20. Krause P, Boyle DP, Bäse F. Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 2005; 5: 89-97. ##21. Toor GS, Harmel RD, Haggard BE, Schmidt G. Evaluation of regression methodology with low‐frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas. J Environ Qual 2008; 37(5): 1847-54. ##22. Solomon S. The physical science basis: contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Intergovernmental Panel on Climate Change (IPCC), Climate Change; 2007.##23. Hashemi-Ana SK, Khosravi M, Tavousi T, Nazaripour H. Validation of AOGCMs capabilities for simulation length of dry spells under the climate change and uncertainty in Iran. Sci Res Quart Geographical Data 2017; 26(103): 43-58. [In Persian]. ##24. Gholampour Shemami Y, Majnoun Hosseini N, Bazrafshan J, Sharifzadeh F, Kanouni H. Assessing precipitation and reference potential evapotrans-piration in the current climate and under CORDEX climate change projections in major drylands region of Kurdistan province. Iran J Soil Water Res 2020; 50(10): 2583-94. [In Persian].##1. Prudhomme C, Davies H. Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate. Clim Change 2009; 93(1): 197-222. ##2. Wilby RL, Harris I. A framework for assessing uncertainties in climate change impacts: low‐flow scenarios for the River Thames, UK. Water Resour Res 2006; 42(2): 1-10.##3. Wang H, Xiao W, Wang Y, Zhao Y, Lu F, Yang M, et al. Assessment of the impact of climate change on hydropower potential in the Nanliujiang river basin of China. Energy 2019; 167: 950-9. ##4. Türkeş M, Sümer UM, Demi̇r İ. Re‐evaluation of trends and changes in mean, maximum and minimum temperatures of Turkey for the period 1929–1999. Int J Climatol 2002; 22(8): 947-77.##5. Ashraf B, Alizadeh A, Mousavi Baygi M, Bannayan Aval M. Verification of temperature and precipitation simulated data by individual and ensemble performance of five AOGCM models for North East of Iran. Water Soil 2014; 28(2): 253-66 [In Persian]. ##6. Jahanbakhsh Asl S, Khorshiddoust A, Alinejad MH, Pourasghar F. Impact of climate change on precipitation and temperature by taking the uncertainty of models and climate scenarios (case study: Shahr Chay basin in Urmia). Hydro geomorphology 2016; 2(7): 107-22. [In Persian]. ##7. Ghandi A, Seyedian SM. Uncertainty analysis of rainfall projections (case study: Bojnourd and Mashhad synoptic gauge station). J Water Soil Conservat 2017; 24(1): 189-204. [In Persian]. ##8. Khazaei MR, Khazaei H. Scenarios in climate change impact assessment on monthly stream-flow of Karun Basin. J Environ Sci Technol 2018; 20(1): 29-40. [In Persian]. ##9. Taban H, Zohrabi N, Nikbakht SA. Uncertainty assessment of GCM models for estimating rainfall and runoff of Dez Ulya basin under climate change. J Earth Space Phys 2018; 44: 89-102. [In Persian]. ##10. Wilby RL, Whitehead PG, Wade AJ, Butterfield D, Davis RJ, Watts G. Integrated modelling of climate change impacts on water resources and quality in a lowland catchment: River Kennet, UK. J Hydrol 2006; 330(1-2): 204-20. ##11. Cameron D. An application of the UKCIP02 climate change scenarios to flood estimation by continuous simulation for a gauged catchment in the northeast of Scotland, UK (with uncertainty). J Hydrol 2006; 328(1-2): 212-26.##12. Graham LP, Hagemann S, Jaun S, Beniston M. On interpreting hydrological change from regional climate models. Clim Change 2007; 81(1): 97-122.##13. Fowler HJ, Ekström M. Multi‐model ensemble estimates of climate change impacts on UK seasonal precipitation extremes. Int J Climatol 2009; 29(3): 385-416. ##14. Chilkoti V, Bolisetti T, Balachandar R. Climate change impact assessment on hydropower generation using multi-model climate ensemble. Renewable Energy 2017; 109: 510-7. ##15. Naz BS, Kao SC, Ashfaq M, Gao H, Rastogi D, Gangrade S. Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States. J Hydrol 2018; 556: ##359-70. ##16. Wang GQ, Zhang JY, Xu YP, Bao ZX, Yang XY. Estimation of future water resources of Xiangjiang River Basin with VIC model under multiple climate scenarios. Water Sci Eng 2017; 10(2): 87-96. ##17. Thompson JR, Laizé CL, Green AJ, Acreman MC, Kingston DG. Climate change uncertainty in environmental flows for the Mekong River. Hydrol Sci J 2014; 59(3-4): 935-54. ##18. Xu K, Xu B, Ju J, Wu C, Dai H, Hu BX. Projection and uncertainty of precipitation extremes in the CMIP5 multimodel ensembles over nine major basins in China. Atmos Res 2019; 226: 122-37. ##19. Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati ME, Barati MA, et al. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agr Water Manag 2011; 100(1): 1-8. ##20. Krause P, Boyle DP, Bäse F. Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 2005; 5: 89-97. ##21. Toor GS, Harmel RD, Haggard BE, Schmidt G. Evaluation of regression methodology with low‐frequency water quality sampling to estimate constituent loads for ephemeral watersheds in Texas. J Environ Qual 2008; 37(5): 1847-54. ##22. Solomon S. The physical science basis: contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge: Intergovernmental Panel on Climate Change (IPCC), Climate Change; 2007.##23. Hashemi-Ana SK, Khosravi M, Tavousi T, Nazaripour H. Validation of AOGCMs capabilities for simulation length of dry spells under the climate change and uncertainty in Iran. Sci Res Quart Geographical Data 2017; 26(103): 43-58. [In Persian]. ##24. Gholampour Shemami Y, Majnoun Hosseini N, Bazrafshan J, Sharifzadeh F, Kanouni H. Assessing precipitation and reference potential evapotrans-piration in the current climate and under CORDEX climate change projections in major drylands region of Kurdistan province. Iran J Soil Water Res 2020; 50(10): 2583-94. [In Persian]. ##

Spatial Analysis of Pre-Hospital Emergency Bases in Disasters in Tehran Province, Iran Spatial Analysis of Pre-Hospital Emergency Bases in Disasters in Tehran Province, Iran 2 1 INTRODUCTION: Tehran is always exposed to various dangers due to its high population density. A geographic information system (GIS) can be very useful for reducing the financial and human burden caused by accidents, disasters, and diseases. METHODS: This field study was performed using a practical and descriptive research method. The pre-hospital emergency bases in the east of Tehran province and all the emergency bases covered by Shahid Beheshti University of Medical Sciences in disasters were evaluated as a case study. The population of these cities amounted to 1,149,485 people and included cities in the east of Tehran province, Iran, including Damavand, Firoozkooh, Pakdasht, Pishva, Qarchak, and Varamin. FINDINGS: Rational maps were created and analyzed in the areas where emergency bases were located using ArcGIS software, as well as analysis of regions, distances, point density, and a combination of these factors. Regarding the standards and indicators, it was determined that the Disaster and Emergency Medical Management Center needs to have130 emergency medical technicians, 23 ambulances, and one ambulance bus to equip the exiting emergency bases according to the standard pre-hospital regulations. Other requirements in this regard include the replacement of worn-out ambulances with new ones, construction of two emergency bases in Qarchak, Tehran, Iran, and three emergency bases in Pakdasht, Tehran, Iran, as well as the transfer of bases in the proximity of faults and flood-prone areas to safe places. CONCLUSION: Based on the obtained results, a comprehensive pre-hospital database was designed for the use of managers and officials in the occurrence of accidents, which might be used as a pilot work that can be expanded to other areas of Tehran, Iran, and other provinces in managing disasters and accidents. 2 INTRODUCTION: Tehran is always exposed to various dangers due to its high population density. A geographic information system (GIS) can be very useful for reducing the financial and human burden caused by accidents, disasters, and diseases. METHODS: This field study was performed using a practical and descriptive research method. The pre-hospital emergency bases in the east of Tehran province and all the emergency bases covered by Shahid Beheshti University of Medical Sciences in disasters were evaluated as a case study. The population of these cities amounted to 1,149,485 people and included cities in the east of Tehran province, Iran, including Damavand, Firoozkooh, Pakdasht, Pishva, Qarchak, and Varamin. FINDINGS: Rational maps were created and analyzed in the areas where emergency bases were located using ArcGIS software, as well as analysis of regions, distances, point density, and a combination of these factors. Regarding the standards and indicators, it was determined that the Disaster and Emergency Medical Management Center needs to have130 emergency medical technicians, 23 ambulances, and one ambulance bus to equip the exiting emergency bases according to the standard pre-hospital regulations. Other requirements in this regard include the replacement of worn-out ambulances with new ones, construction of two emergency bases in Qarchak, Tehran, Iran, and three emergency bases in Pakdasht, Tehran, Iran, as well as the transfer of bases in the proximity of faults and flood-prone areas to safe places. CONCLUSION: Based on the obtained results, a comprehensive pre-hospital database was designed for the use of managers and officials in the occurrence of accidents, which might be used as a pilot work that can be expanded to other areas of Tehran, Iran, and other provinces in managing disasters and accidents. 61 76 2020/12/92020/11/22020/11/172021/01/32020/11/162020/10/32021/01/24 1399/11/5 2021/01/162021/01/32021/03/142021/03/302021/01/312021/02/72021/02/3 1399/11/15 زهرا ملامحمدعلیان مهریزی Zahra Mollamohammad Alian Mehrizi Applied Science Higher Education Institute Red Crescent Society of the Islamic Republic of Iran, Tehran, Iran Iran zmehrizi@yahoo.com سید احمد میرشجاعی Seyed Ahmad Mirshojaee Master of DMIS, Tehran, Iran Iran zmehrizi@yahoo.com Geographic Information System Management of Accident Pre-hospital Emergency Spatial Analysis Geographic Information System Management of Accident Pre-hospital Emergency Spatial Analysis 1. Karamizadeh Z, Zarifsanayei N, Faghihi AA, Mohammadi H, Habibi M. The study of effectiveness of blended learning approach for medical training courses. Iran Red Crescent Med J 2012; 14(1): 41. ##2. Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med 2002; 9(4): 288-95. ##3. Moghaddasi H, Rabiei R, Mastaneh Z, Movahedinia S, Shayan ZP, BastaniTehrani M, et al. Pre-hospital emergency information system in America and England: a review. Payesh 2014; 13(4): 383-91 [In Persian].##4. Talebi B, Faryabi F. Application of GIS in road accident management. Tehran: Instruction of Intercity Rescue Bases; 2014 [In Persian]. ##5. Zebardast E, Mohammadi A. Site selection for post-earthquake relief centers using GIS and analytic hierarchy process (AHP). Honar-Ha-Ye-Ziba 2005; 21: 5-16. [In Persian]. ##6. Chen AY, Yu TY, Chuang WL, Lai JS, Yeh CH, Ma HM, et al. Ambulance service area considering the disturbance of disasters on transportation infrastructure. Design Construct Maintain Bridges 2014; 44: 94-101. ##7. Fatih SA. A GIS based new navigation approach for reducing emergency Vehicle's response time. Selcuk Univ J Eng Sci Technol 2017; 5(1): 47-60. ##8. Schmid V. Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. Eur J Operat Res 2012; 219(3): 611-21. ##9. Ghasemi Moghar J. General results of the general population and housing census 2016. Tehran: Plan and Budget Organization of Iran; 2017. [In Persian]. ##10.##1. Karamizadeh Z, Zarifsanayei N, Faghihi AA, Mohammadi H, Habibi M. The study of effectiveness of blended learning approach for medical training courses. Iran Red Crescent Med J 2012; 14(1): 41. ##2. Blackwell TH, Kaufman JS. Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med 2002; 9(4): 288-95. ##3. Moghaddasi H, Rabiei R, Mastaneh Z, Movahedinia S, Shayan ZP, BastaniTehrani M, et al. Pre-hospital emergency information system in America and England: a review. Payesh 2014; 13(4): 383-91 [In Persian].##4. Talebi B, Faryabi F. Application of GIS in road accident management. Tehran: Instruction of Intercity Rescue Bases; 2014 [In Persian]. ##5. Zebardast E, Mohammadi A. Site selection for post-earthquake relief centers using GIS and analytic hierarchy process (AHP). Honar-Ha-Ye-Ziba 2005; 21: 5-16. [In Persian]. ##6. Chen AY, Yu TY, Chuang WL, Lai JS, Yeh CH, Ma HM, et al. Ambulance service area considering the disturbance of disasters on transportation infrastructure. Design Construct Maintain Bridges 2014; 44: 94-101. ##7. Fatih SA. A GIS based new navigation approach for reducing emergency Vehicle's response time. Selcuk Univ J Eng Sci Technol 2017; 5(1): 47-60. ##8. Schmid V. Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. Eur J Operat Res 2012; 219(3): 611-21. ##9. Ghasemi Moghar J. General results of the general population and housing census 2016. Tehran: Plan and Budget Organization of Iran; 2017. [In Persian]. ##10. ##

Role of Reducing the Vulnerability of Urban Texture in the Capacity of Relief and Rescue Operation after a Possible Earthquake in the District 5 of Isfahan, Iran Role of Reducing the Vulnerability of Urban Texture in the Capacity of Relief and Rescue Operation after a Possible Earthquake in the District 5 of Isfahan, Iran 2 1 INTRODUCTION: Despite the progress of urbanization, earthquake as one of the most important natural hazards threatens most cities in the world. Accordingly, managing and reducing the vulnerability of cities to this disaster, as well as the planned relief and rescue operation, are of particular importance. The city of Isfahan, Iran, is one of the areas which requires proper attention and planning due to its high population density and vulnerability. In this regard, this study was conducted to identify the physical texture vulnerability of the District 5 of Isfahan to earthquakes and its impact during rescue and relief operations. METHODS: To conduct the research, the indices of access to green space, building density, population density, distance from the fault, distance from relief centers, access to roads and arteries, and width of roads were selected due to their frequency in studies conducted on the vulnerability of cities and scores given by specialists. Finally, the critical areas of the region were determined by weighting each of the indices using the analytic hierarchy process method in Expert Choice software (version 11) and examining the vulnerability of the region in the Geographic Information System. FINDINGS: It was revealed that 68% of the area had a suitable density of green space, and 73% and 88% of the region had low building and population densities, respectively. Moreover, 76% of the area had good access to relief centers and the whole area had proper passages. Finally, it was found that no faults passed through this area, and the impact of adjacent faults caused this area to be in a moderate situation in terms of vulnerability. CONCLUSION: The critical areas were determined by overlaying each of the vulnerability layers of the city and applying their degree of importance. The results showed that 6% and 18% of the areas were in critical and highly vulnerable conditions, respectively. Therefore, rescue and relief operations would be performed with an acceptable capacity after such disasters as earthquakes. 2 INTRODUCTION: Despite the progress of urbanization, earthquake as one of the most important natural hazards threatens most cities in the world. Accordingly, managing and reducing the vulnerability of cities to this disaster, as well as the planned relief and rescue operation, are of particular importance. The city of Isfahan, Iran, is one of the areas which requires proper attention and planning due to its high population density and vulnerability. In this regard, this study was conducted to identify the physical texture vulnerability of the District 5 of Isfahan to earthquakes and its impact during rescue and relief operations. METHODS: To conduct the research, the indices of access to green space, building density, population density, distance from the fault, distance from relief centers, access to roads and arteries, and width of roads were selected due to their frequency in studies conducted on the vulnerability of cities and scores given by specialists. Finally, the critical areas of the region were determined by weighting each of the indices using the analytic hierarchy process method in Expert Choice software (version 11) and examining the vulnerability of the region in the Geographic Information System. FINDINGS: It was revealed that 68% of the area had a suitable density of green space, and 73% and 88% of the region had low building and population densities, respectively. Moreover, 76% of the area had good access to relief centers and the whole area had proper passages. Finally, it was found that no faults passed through this area, and the impact of adjacent faults caused this area to be in a moderate situation in terms of vulnerability. CONCLUSION: The critical areas were determined by overlaying each of the vulnerability layers of the city and applying their degree of importance. The results showed that 6% and 18% of the areas were in critical and highly vulnerable conditions, respectively. Therefore, rescue and relief operations would be performed with an acceptable capacity after such disasters as earthquakes. 77 85 2020/12/92020/11/22020/11/172021/01/32020/11/162020/10/32021/01/242020/10/8 1399/7/17 2021/01/162021/01/32021/03/142021/03/302021/01/312021/02/72021/02/32021/03/3 1399/12/13 سید آرش حسینی سبزواری Seyed Arash Hosseini Sabzevari MSc Student of Post-Disaster Reconstruction, Shahid Beheshti University, Tehran, Iran Iran arash.hosseini.arch@gmail.com آتوسا حسنی Atoosa Hassani PhD Student of Architecture, Shahid Beheshti University, Tehran, Iran Iran atoosa_h70@yahoo.com AHP District 5 of Isfahan Earthquake GIS Rescue Vulnerability AHP District 5 of Isfahan Earthquake GIS Rescue Vulnerability 1. Bartels SA, VanRooyen MJ. Medical complications associated with earthquakes. Lancet 2012; 379(9817): 748-57. ##2. Muyser-Boucher I, Secula F. Shelter after disaster: strategies for transitional settlement and reconstruction. London: Department for International Development; 2010.##3. Dong L, Shan J. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J Photogrammetry Remote Sensing 2013; 84: 85-99. ##4. Kial A, Aghili, M. Analysis and location of fire stations in Mashhad using AHP and GIS. National Conference on Spatial Information System (GIS), Tehran, Iran; 2009 [In Persian].##5. Hosseini G, Haidary HE. Measuring the resilience of historical sites against earthquakes and its upgrading (Case study: Sang-e Siah neighborhood of Shiraz). J Sustainable Architecture Urban Design 2018; 6(1): 89-103 [In Persian].##6. Zhang W, Xu X, Chen X. Social vulnerability assessment of earthquake disaster based on the catastrophe progression method: a Sichuan province case study. Int J Disast Risk Reduct 2017; 24: ##361-72. ##7. Zangiabadi A, Mohammadi G, Safaei H, Gaedrahmati S. Vulnerability indicators assessment of urban housing against the earthquake Hazard case study: Isfahan housing. Geography Dev Iran J 2008; 6(12): 61-79 [In Persian].##8. Ghaed Rahmati S, Fazel S. Evaluation of urban safety area about the risk possibility of urban in habitats of Isfahan province. Geography Dev Iran J 2014; 12(36): 123-34 [In Persian].##9. Ganjeie S, Omidvar B, Fallah K, Rahimi Mamaghani M. Determining effective indicators in determining rescue and relief evacuation routes in urban areas from the perspective of crisis management. Second National Conference on Crisis Management: The Role of New Technologies in Reducing Vulnerability to Unexpected Disasters, Tehran, Iran; 2012 [In Persian].##10. Anari F, Eghbali N, Moayedfar R. Analysis and evaluation of effective variables on improving the urban road network resilience in the natural environment Man-made (case study: five areas of the eastern part of Tehran). Geography 2019; 9(3): 351-64 [In Persian].##11. Jafari EM, Arzegan M. A new model for location – routing – relief logistic inventory in earthquake situations under fuzzy conditions based on risk management (Case study: Tehran city). Rahvar Sci Quart 2019; 28(8): 87-110 [In Persian].##12. Mohammadbeigi A, Mohammadsalehi N, Aligol M. Validity and reliability of the instruments and types of measurements in health applied researches. J Rafsanjan Univ Med Sci 2015; 13(12): 1153-70 [In Persian].##13. Ülengin B, Ülengin F, Güvenç Ü. A multidimensional approach to urban quality of life: the case of Istanbul. Eur J Operat Res 2001; 130(2): 361-74. ##14. Ahadnejad RM, Norozi MJ, Zolfi A, Jalili K. The assessment of urban social vulnerability to earthquake (A case study: Khoramdareh city). Geographical J Chashmandaz-e-Zagros 2011; 3(7): 81-98 [In Persian].##15. Saghaei M. Identification and prioritization of urban deteriorated texture in order to reduce the earthquake-induced vulnerability-case study: region 5 in Esfahan. Geographical Data 2018; 27(105): 171-82 [In Persian].##16. Dousti A. Earthquake vulnerability reduction of ##the historic fabrics of Jolfa Neighborhood. Reconstruction research department faculty of architecture and urban planning, Tehran. [Master Thesis]. Tehran: University of Tehran; 2016 [In Persian]. ##17. Hajinezhad A, Badali A, Aghaei V. The survey effective factors in vulnerability due earthquake in informal district of city zones with application of GIS: case study: 1 and 5 zones of Tabriz. J Natl Environ Hazards 2016; 4(6): 33-56 [In Persian].##18. Earthquake Vulnerability Reduction for Cities (EVRC-2). Bangkok, Thailand: Asian Disaster Preparedness Center; 2008. ##19. Li S. The development of disaster prevention green space in China. Landscape Architecture Frontiers 2014; 2(4): 44-52. ##20. Habibi K, Sheieh E, Torabi K. The role of physical planning in mitigating urban vulnerability against earthquake. Armanshahr J 2010; 2(3): 23-31 [In Persian].##21. Zangiabadi A, Rezaei M, Shahraki M, Mirzaei S. The evaluation and vulnerability analyzes of urban zone against the earthquake crises by using IHWP model case study: 3th zone of Isfahan city. Geographical Plan Space 2013; 3(8): 137-56 [In Persian].##22. Kazeminia A, Meimandi Parizi S. Evaluating the potency of city crossovers network with the approach of crisis managing by using GIS. J Geomatics Sci Technol 2017; 6(4): 87-106 [In Persian].##23. Soltani Fard H, Zanganeh A, Nodeh M, Hosseini F. Spatial analysis of the street network impacts on urban neighborhoods earthquake vulnerability. Case study: Amiriyeh neighborhood, Sabzevar. J Spatial Analysis Environ Hazards 2016; 3(1): 31-49 [In Persian].##24. Ebrahimzadeh I, Kashefidust DD, Hoseini A. Evaluating the vulnerability of urban regions against earthquake, case study: the city of Piranshahr. J Spatial Plan 2019; 5(1): 1-25 [In Persian].##25. Fu X. Planning and design of earthquake disaster relief corridor in stricken cities-Taking the design of the Yucheng district, Ya’an as application case. [Master Thesis]. Karlskrona, Sweden: Blekinge Institute of Technology; 2014.##26. Ali Asl Khiabani E, Sadeghi Niaraki A, Ghoddousi M. Relief routing after an earthquake (Case study: part of the district of Tehran city). Sci J Resc Reli 2018; 9(4): 1-17 [In Persian].##27. Fallahi A, Hassani A. Physical vulnerability assessment of the Hashtgerd new town against a probable earthquake. Geographical Res Quart J 2020; 35(4): 293-306.##28. Khodadadi F, Entezari M, Hassanpour F. Analysis of urban vulnerability to earthquake risk by ELECTRE FUZZY method (Case study: Karaj metropolis). J Appl Res Geographical Sci 2020; 20(56): 93-113 [In Persian]. ##29.##1. Bartels SA, VanRooyen MJ. Medical complications associated with earthquakes. Lancet 2012; 379(9817): 748-57. ##2. Muyser-Boucher I, Secula F. 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