بررسی ابعاد و طبقه بندی شاخص های تاب آوری محلات شهری در برابر سیلاب، مطالعه موردی: محلات سیاحی و عین دو شهر اهواز (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
پژوهش حاضر باهدف بررسی ابعاد و طبقه بندی شاخص های تاب آوری محلات سیاحی و عین دو شهر اهواز در برابر سیلاب تدوین شده است. این پژوهش به لحاظ هدف کاربردی - نظری و ازلحاظ ماهیت و روش، توصیفی- تحلیلی است. جهت طبقه بندی شاخص ها از آزمون های تی تک نمونه ای، تی مستقل و آزمون فریدمن استفاده گردید است؛ که با روش نمونه گیری هدفمند 380 نفر از سرپرستان خانوار ساکن این محلات برگزیده توزیع گردید. برای بررسی وضعیت اثرات تاب آوری سیل در محلات موردمطالعه از ابزار رگرسیون و شاخص های (درمانی، بیمارستانی، راه شریانی، آتش نشانی، نوع ساخت وساز و نوع بافت محلات) استفاده و برای وزن دهی به شاخص های موردمطالعه و داده های فضایی از روش خودهمبستگی فضایی ابزار Weights Manager موجود در نرم افزار Geoda و محیط نرم افزار ArcGis استفاده شد. نتایج حاصل از پژوهش نشان می دهد که بین محلات موردمطالعه ازنظر شاخص های تاب آوری در برابر سیل، تفاوت وجود دارد. میانگین نمرات میزان تاب آوری نهادی ساکنان محله عین دو برابر با (49/79) و برای ساکنان محله سیاحی برابر با (22/84) به دست آمده است که نشان می دهد میزان تاب آوری نهادی ساکنان محله سیاحی بیشتر از ساکنان محله عین دو بوده است. همچنین با توجه به نتایج حاصل از آزمون T و بر اساس سطح معناداری (Sig) کمتر از 05/0، بین محلات سیاحی و عین دو ازنظر شاخص تاب آوری اقتصادی و نهادی تفاوت معناداری وجود دارد.Investigating Dimensions and Classification of Urban Neighbourhoods' Flood Resilience Indicators The Case study of Ahvaz Sayyahi and Eyn-e Do Neighbourhoods
The present study investigates the dimensions and classification of flood resilience indicators of Ahvaz Sayyahi and Eyn-e Do neighbourhoods. It employed an applied-theoretical research design and descriptive-analytical research method. A One-sample t-test, independent t-test, and Friedman test were used to classify the indicators. Via a purposive sampling method, 380 heads of households living in these selected neighbourhoods were selected as participants. Regression analysis was performed on the healthcare, hospital, arterial roads, fire stations, construction type, neighbourhood texture type indicators to investigate the effects of flood resilience in the study areas. The spatial autocorrelation of the Weights Manager instrument in Geoda software and the ArcGis software were used to weigh the indicators and spatial data. The research results show a difference between the studied areas in terms of flood resilience indicators. The institutional resilience mean of Eyn-e Do neighbourhood was 79.49, and of Sayyahi neighbourhood was 84.22, indicating that the institutional resilience of Sayyahi neighbourhood was higher than that of Eyn-e Do neighbourhood. Also, according to the t-test results and based on the significance level (Sig.) less than 0.05, there is a significant difference between Sayyahi and Eyn-e Do neighbourhoods regarding the economic-institutional resilience indicator
Extended Abstract
Introduction
In recent decades, the increase in world population, especially in urban areas as an essential phenomenon, has created many complexities and problems in various fields. The city is significant as a breeding ground for accidents. In the last few years, the world has witnessed unforeseen natural disasters such as the Asian tsunami, Hurricane Katrina, and the Wenchuan earthquake in China. Dealing with these natural hazards is one of the main challenges for most countries. Today, cities and residential communities have been built in places exposed to natural hazards in terms of natural hazards or due to technological advances due to various human-made accidents. Not only does it cause death and their emotional suffering, but it has also damaged their local economy. In some cases, these hazards, sometimes as catastrophes, may affect the vulnerable urban population. The hazards are considered as challenges for developing countries. The vulnerability caused by these hazards is increasing globally. The impacts on the cities are severe and widespread in the physical, economic, and social development, loss of life, property, resources, and overall destruction indicators. The present study investigates the dimensions and classification of flood resilience indicators of Ahvaz Sayyahi and Eyn-e Do neighborhoods.
Methodology
The present research employed an applied-theoretical research design and descriptive-analytical research method. It also used survey research to collect and analyze the data. To achieve research objectives, the two institutional and economic indicators and their eight sub-indicators, i.e., knowledge of organizational performance, institutional context, institutional relations, institutional performance, the number of damages, ability to compensate damages, ability to return to normal conditions, and disaster risk insurance, were extracted. Via a purposive sampling method, 380 heads of households living in these neighbourhoods were selected, and copies of a standard questionnaire were distributed among them to be completed.
A one-sample t-test, independent t-test, and the Friedman test were employed to explore the resilience pattern trend for classifying flood resilience indicators. To evaluate the effects of flood resilience in the study areas, regression analysis, and to weigh the healthcare, hospital, arterial roads, fire stations, construction type, neighbourhood texture type indicators, and spatial data from the spatial autocorrelation method of the Weights Manager instrument in Geoda software and Arc GIS software were used.
Results and discussion
The research results show a difference between the studied areas in terms of flood resilience indicators. The institutional resilience mean of Eyn-e Do neighbourhood was 79.49, and of Sayyahi neighbourhood was 84.22, indicating that the institutional resilience of Sayyahi neighbourhood was higher than that of Eyn-e Do neighbourhood. Also, according to the t-test results and based on the significance level (Sig.) less than 0.05, there is a significant difference between Sayyahi and Eyn-e Do neighbourhoods regarding the economic-institutional resilience indicator.
Conclusion
The study of spatial comparisons of resilience and priorities shows that significant parts of the two neighborhoods' textures are in very poor and poor resilience conditions. The main parts of the two flood-prone neighbourhoods' texture are in very poor and poor flood resilience conditions. Analytical results of the study areas show access to fire stations, crisis management centers, medical centers, and hospitals. Ahvaz's location, especially Eyn-e Do and Sayyahi neighborhoods, necessitates the revision of urban prioritization. Accordingly, these areas should be the priority of resilience planning.
The difference between Sayyahi and Eyn-e Do neighbourhoods in terms of the flood resilience indicator shows that the mean of economic resilience for Eyn-e Do neighbourhood is 66.67, and of Sayyahi neighbourhood is 74.56. In other words, in this study, the economic resilience rate of Sayyahi neighbourhood is higher than that of Eyn-e Do neighbourhood. The mean of institutional resilience of Eyn-e Do neighbourhood is 79.49, and of Sayyahi neighbourhood is 84.22, indicating that institutional resilience of Sayyahi neighbourhood is higher than that of Eyn-e Do neighbourhood. Also, according to the t-test results (Sig. less than 0.05), there is a significant difference between Sayyahi and Eyn-e Do neighbourhoods in terms of economic-institutional resilience.
Funding
There is no funding support.
Authors’ Contribution
Authors contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none.
Conflict of Interest
Authors declared no conflict of interest.
Acknowledgments
We are grateful to all the scientific consultants of this paper.
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