Estimated distribution of urban resilience from the perspective of the earthquake crisis using the Spatial Stats Model (Case Study of Ilam)

Authors

• Elias Mavedat ¹
• parsto garmsiri ²
• korosh momeni ³

¹ Assistant Professor of Urbanization, Jundi-Shapur University of Technology, Dezful, Iran

² Master of Architecture, Faculty of Architecture and Urbanization, Isfahan University of Art, Isfahan, Iran

³ Associate Professor of Architecture, Jundi-Shapur University of Technology, Dezful, Iran

Abstract

Abstract
         From the point of view of crisis management, Resilience, One of the most important issues is to achieve sustainability. Indeed, it is a way of strengthening communities by using their capacities. The issue of choice in the city of Ilam is located in the Ilam city of Zagros. In terms of seismic state, it is in a dangerous position. So that the Zagros are a has the highest earthquake with magnitude 4 to 4/5 richter has. Based on the importance of the subject, the present study is based on a developmental-applied approach and a combination of descriptive-analytical research methods, Thus, with the field studies of the 14 areas of Ilam and referring to the relevant organizations, the required information has been compiled from two physical-social dimensions. Finally, data analysis with spatial statistics theory and GRAFER, EQS, EXCEL and ArcGIS, VPLS and AMOS-SPSS software were used. The results of the research have shown that, based on the results of the Ilam earthquake classification, vulnerable areas are classified in seven classes. According to the results, zone 2 of area 4 and area 2 of area 3 respectively at a confidence level of 99% and 90% as the cold and hot spatial areas of the city of Ilam, there is a degree of volatility and vulnerability. And based on the results of the Z-Score, which is equal to 586.5%, there is probably less than 1% of the radiation in the city of Ilam.
 
Keywords: Resilience, Modeling, Space Statistics, Ilam.
 
 
 
Extended Abstract
Introduction:
           The key characteristic of resilience as a tool is to provide the best scale and method for intervention achieve maximum profit and minimum cost to show. The key characteristic of resilience as a tool is to provide the best scale and method for intervention Reaching the maximum profit and spending the least money. Therefore, urban planning plays a vital role in shaping resilient cities. This requires strategic planning and good urban form to accommodate the issue.
        From the point of view of crisis management, Resilience, One of the most important issues is to achieve sustainability. Indeed, it is a way of strengthening communities by using their capacities. The issue of choice in the city of Ilam is located in the Ilam city of Zagros. In terms of seismic state, it is in a dangerous position. So that the Zagros area has the highest earthquake with magnitude 4 to 4/5 richter has.  The city of Ilam is first considered as the best place for human gathering and the other as the city most vulnerable to natural disasters in order to mitigate the effects of these catastrophic disasters.Therefore, based on the necessity of the subject, the city of Ilam is located in the Zagros zone. Seismically, Ilam is in a dangerous position. The purpose of this study was to investigate the status of resilience in two social-physical dimensions of the 14 districts of Ilam.
Methodology:
         Based on the necessity of the subject, the overall purpose of the present study was to investigate the use of spatial statistics approach to investigate the issue of urban resilience caused by earthquakes in Ilam. This can be used to increase the confidence rating of resilience areas. That is to say, in terms of spatial statistics, the reduction of the error coefficient and the presentation of confidence intervals by increasing the volume of variables.For statistical analysis of spatial statistics it is necessary to consider a statistical model. A random field is usually considered as a statistical model for spatial data.
[├ Z (S): S∈D⊑R ^ d; d≥1] ┤
Is where D is the set of transition indexes. Each random field can be parsed as follows:
 Z (S) = µ (S) + δ (S)
       Where µ (S) is the large scale change or trend and δ (S) is the small scale change or random field error process. It is noteworthy that if the finite distribution of any finite number of random variables of a field has a normal distribution , Will be the Gaussian random field. If the mean of the random field is constant and does not depend on the position, that is, E ({Z (S)) and the variance of the expression It is only a function of the distance of the positions, then they are called the random field of the second order. Also, the transform and change functions are always conditional negative and always positive, respectively. In addition to the value of h, the variogram depends on its direction. In other words, only a function of the size of the distance
h means | h | = | S_i-S_j |, they are called (or random field) isotropic.
         Based on the importance of the subject, the present study is based on a developmental-applied approach and a combination of descriptive-analytical research methods, Thus, with the field studies of the 14 areas of Ilam and referring to the relevant organizations, the required information has been compiled from two physical-social dimensions. Finally, data analysis with spatial statistics theory and GRAFER, EQS, EXCEL and ArcGIS, VPLS and AMOS-SPSS software were used.
Results and discussion:
 
Figure 1- Overall Resilience Pattern of Ilam City Based on Qi Ranking
- Based on the above model: Value is 0.227%. This indicates an inappropriate distribution and random pattern in Ilam.
 
Figure 2- Land Use Resilience Distribution of Ilam City
        As mentioned, the estimation of spatial statistics, based on the Kriging interpolation method, is a process by which the value of one quantity at a point with known coordinates can be used to determine the value of the same quantity at other points with known coordinates. Achieved. Kriging is based on weighted moving averages and can be called the best linear estimator. In this method different fitting patterns are used to estimate the resilience rate in the city, then the higher accuracy model is used as a suitable model for mapping the resilience zoning. Kriging methods are based on the variogram definition and the success of the method depends on the selection of the appropriate or optimal variogram model and the variogram is used to determine and describe the spatial structure of the data. Variography is the first step in modeling spatial structure for use in kriging. In this case, we have the following:
 
Figure 3- Pattern of social resilience in Ilam city areas
 
  A variogram is used to determine and describe the spatial structure of the data. Variography is the first step in modeling spatial structure for use in kriging. The variogram is calculated by the following relation: In the above relation: (h) γˆ: The value of the variogram for the number of N sample pairs separated by h step or Lag distance, and z (xi + h) Also, the values the x-area variables are at points i and i + h.
- Based on the results, the value was 5.58%. That is probably less than 1 percent of resilience in the city of Ilam. In other words, less than 1% of the elements studied can play a role in resilience of Ilam.
Conclusion:
         The results of the research have shown that, based on the results of the Ilam earthquake classification, vulnerable areas are classified in seven classes. According to the results, zone 2 of area 4 and area 2 of area 3 respectively at a confidence level of 99% and 90% as the cold and hot spatial areas of the city of Ilam, there is a degree of volatility and vulnerability. And based on the results of the Z-Score, which is equal to 586.5%, there is probably less than 1% of the radiation in the city of Ilam.
Accordingly, to the research, the characteristics of resilient communities can be summarized as follows:
Internal Independence: Able to operate independently of external control.
Strength: The ability to withstand attacks or other external forces.
Interdependent: with related system components, so that they support one another.
Compatibility: The capacity to learn from experiences and the flexibility to change. In this case, we have the following:Cooperation or Cooperation: Many opportunities and incentives for stakeholder engagement.

Keywords

• Resilience
• Modeling
• Space Statistics
• Ilam

20.1001.1.22516735.1398.9.36.8.7

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References

-        Askari, Alireza (2017): Analysis of Space Statistics with Geographic Information System, Second Edition, Tehran. [In Persian]

-        Batty. M, (2009): Urban Modeling, in N. Thrift and R. Kitchin (Editors) International Encyclopedia of Human Geography,Vol 12, Elsevier, Oxford, UK.

-        Belkhouche, B.; Reda, B., Salah, A. (2001): An Expert Decision Support System for Crisis Management, Tulane University New Orleans.

-        Bhandari, R.K. (2010): An Overview on Natural and Manmade Disasters and Their Reduction, World Congress on Sustainable Development (Seminar), Calcutta.

-        Boon, D. (2012): Recovery from disaster: Resilience, adaptability and perceptions of climate change, James Cook University Press.

-        Brenner, N. (2004): New State Spaces, Urban Governance and the Rescaling of Statehood, Oxford University Press.

-        Bristow, G. (2010): Resilient Regions Re-Placing Regional Competitiveness, Cambridge Journal of Regions, Economy and Society, 3 (1).

-        Commission, E. (1997): Compendium of European Planning Systems (Brussels: CEC), Department of Conservation and Land Management Cape Range National Park, Draft Management Plan (Perth, Australia: CALM).

-        Daddshpour, Hashem and Adeli, Zainab (2015): "Measurement of the Resilience Capacity in the City of Qazvin", Crisis Management Magazine, No. 8. [In Persian]

-        Dalaakeh, Hasan; Samara Mohsen Beigi, Hussein; Shahiandi, Ahmad (2017), "Measuring the level of social resilience in urban areas Isfahan ", Journal of Sociology of Social Institutions, No. 4. [In Persian]

-        Davis, A. (2006): Risk Work and Mental Health, Good Practice in Risk Assessment and Risk Management, London Jessica Kingsley.

-        Derisen, P. (2009), A Set-Based Approach to Support Decision-Making on the Restoration of Infrastructure Networks, Earthquake Spectra 33. 

-        Dervishzadeh, Ali and Mohammadi, Mahin (2012): Iranian Geology, Third Edition, Payame Noor University Press. [In Persian]

-        Engineering Consulting Engineers (2008): Ilam City Reconstruction Project, Ilam Province Road and Urban Development Office. [In Persian]

-        Folke, C. (2010): Resilience thinking: integrating resilience, adaptability and transformability, Journal Ecology and Society, Number1.

-        Gharei, Fariba, Masnavi, Mohammad Reza and Haji Bande, Mona (2017): "Development of key indicators of spatial-urban spatial measurements; Comprehensive review Theoretical Literature ", Garden Pars Journal, No. 57. [In Persian]

-        Holling, C. S. (1993), Resilience and Stability of Ecological Systems, Annual Review of Ecology and Systematic, 4.

-        Karrholm, N. (2015): Spatial resilience and urban planning: Addressing the interdependence of urban retail areas, Journal Cities, Vol 36.

-        Kutum, D. (2016): Evaluation of dimensions, approaches and concepts of resilience in urban societies with an emphasis on natural disasters, Journal of Fundamental and Applied Sciences, No 8.

-        Lefteris, A.; Mantelas, H. and Poulicos, P. (2010): A Fuzzy Cellular Automata Modeling Approach, Accessing Urban Growth Dynamics in Linguistic Terms, ICCSA 2010, Part I, LNCS 6016.

-        Management and Planning Organization of Ilam Province (1965-1999): A Comprehensive Economic-Social Report of Ilam Province. [In Persian]

-        Mavedat, Elias (2017): Modeling of Urban Structure in terms of earthquake crisis, Ph.D., Assistant Professor S. Maleki, Shahid Chamran University of Ahvaz. [In Persian]

-        O’Brien, K. (2010): Vulnerable or Resilient? A Multi-Scale Assessment of Climate Impacts and Vulnerability in Norway, Journal Climatic change, 64 (1-2).

-        Olazabal, M.; Chelleri, L. and Waters J.J. (2012): Why urban resilience?. In: Chelleri, Marta, Multidisciplinary perspectives on urban resilience a workshop report, Basque Centre for Climate Change, Bilbao.

-        Pimm, S.L. (1984): The Balance of Nature? Ecological Issues in the Conservation of Species and Communities, University of Chicago Press, Chicago.

-        Rezaei, Mohammad Reza (2012): "Assessment of Economic and Institutional Resilience of Urban Communities against Natural Disasters, Case Study The earthquake in the neighborhoods of Tehran ", Crisis Management Quarterly, Volume 2, Number 1. [In Persian]

-        Suarez, M.; Gomez-Baggethun, M.; Benayas, J. and Tilbury, D. (2016): Towards an Urban Resilience Index: A Case Study in 50 Spanish Cities. Sustainability , 8(8): 774, 1-19.

-        United Nations International Strategy for Disaster Reduction (UN/ISDR) (2007 and 2016): Terminology, Basic Terms of Disaster Risk Reduction, International Strategy for Disaster Reduction.