XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Salmani M, Kazemi Sani Ataallah N, S. Ali B, Motavaf S. Identifying and Analyzing the Impact Resilience Indicators in the Rural Areas of North and Northeast Tehran. Journal of Spatial Analysis Environmental Hazards 2016; 3 (2) :1-22
URL: http://jsaeh.khu.ac.ir/article-1-2557-en.html
Abstract:   (7396 Views)

Human communities are affected by hazards, disasters and catastrophic events throughout history, including natural disasters (such as: earthquakes, hurricanes, floods, tornadoes) man-made disasters (such as: nuclear accidents, explosions, socio or political crisis, economic disturbances). Therefore, catastrophic events can have human or natural causes. These conditions show that human communities not only ever been stable, but they are continuously unstable and are exposed to disarranging events. Godschalk knows resiliency an important goal for two reasons. “First, because the vulnerability of technological and social systems cannot be predicted completely, resilience –the ability to accommodate change gracefully and without catastrophic failure- is critical in times of disaster. If we knew exactly when, where, and how disasters would occur in the future, we could engineer our systems to resist them. Since hazard planners must cope with uncertainty, it is necessary to design communities that can cope effectively with contingencies. Second, people and property should fare better in resilient communities struck by disasters than in less flexible and adaptive places faced with uncommon stress. In resilient communities, fewer building should collapse. Fewer power outages should occur. Fewer households and business should be put at risk. Fewer deaths and injuries should occur. Fewer communications and coordination breakdowns should take placeStructural analysis is first of all a tool of structuring the ideas. It gives the possibility to describe a system with the help of a matrix connecting all its components. By studying these relations, the method gives the possibility to reveal the variables essential to the evolution of the system. It is possible to use it alone (as a helps for reflection and/or decision making), or as part of a more complex forecasting activity. This method has 3 phases. Phase 1: considering the variables: The first stage consists in considering all the variables characterizing the studied system (external as well as internal variables); it is good at this point to be the most comprehensive possible and not to exclude, a priori, any possible path of research. Phase 2: description of the relations between the variables: In a systemic vision, a variable doesn’t exist other than as part of the relational web with the other variables. Also, structural analysis allows to connect the variables in a two-entries table (direct relations). Phase 3: identification of the key variables: This last phase consists in identifying the key variables; first, by a direct classification (easy to realize), then by an indirect classification. Direct classification:  The total of the connections in a row indicates the importance of the influence of a variable on the whole system (level of direct motricity). The total in a column indicates the degree of dependence of a variable (level of direct dependence). Indirect classification: One detects the hidden variables thanks to a program of matrix multiplication applied to an indirect classification. The structural analysis method seeks to highlight key variables, hidden or not, in order to ask the right questions and encourage participants to think about counter-intuitive aspects or behavior within the system. The direct influences of each variable on the set of other variables are illustrated in matrix form. Each element of the matrix represents an influence (0 = no direct relationship of influence on the two variables considered; 1 = a direct relationship of influence). We also took into account the level of influence between two variables. The following convention was used: 1 = low relationship; 2 = average; 3 = strong; P = potential relationship.. P levels were also given 0-3 ratings. By reading the matrix, we can classify the variables by their -level of direct influence: importance of influence of a variable on the whole system, obtained through the total of links created per line; - level of direct dependence: degree of dependence of a variable, obtained by the total of links created per column. The direct and indirect influences of the variable represent the system the most realistically. Highlighted are the determining factors (main determinants) of the situation under investigation. The input variables and result or output variables help participants understand the organization and structuring of the system under the microscopeBased on the results of direct influence matrix, social, economic and institutional variables are effectiveness in comparison to others. They have a great impact on system but physical variable effectiveness is much less than its impact. Among of mentioned variables, institutional variable had a significant numerical difference. Indirect cross-impact matrix showed significant differences in the institutional and social variables compared to other variables in the effectiveness and affected. The results indicate the high impact of these two variables on the system. In other words, institutional and social variables were influential factors in their community resilience. According to the results of direct influence matrix, strategic and key factors are including participation, assistance and interactions from social variables, readiness from intuitional variable and in indirect influence matrix; these factors are including participation, social identity, assistance and interactions from social variables and readiness from intuitional variable. Distribution of factors in axis influences of direct and indirect suggests that this system is unstable.

Full-Text [PDF 1150 kb]   (4232 Downloads)    
Type of Study: Research | Subject: Special
Received: 2016/11/1 | Accepted: 2016/11/1 | Published: 2016/11/1

Add your comments about this article : Your username or Email:

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Spatial Analysis Environmental hazarts

Designed & Developed by : Yektaweb