Disaster, Risk and Vulnerablity Conference 2011 School of Environmental Sciences, Mahatma Gandhi University, India in association with the
Applied Geoinformatics for Society and Environment, Germany March 12–14, 2011
Creating an empirically derived community resilience index for disaster prone area: A case study from Orissa Meher M K, Patra H S and Sethy K M Department of Geography, Utkal University, Bhubaneswar, Orissa Email:
[email protected]
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Keywords disaster resilience disaster index Orissa community resilience
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B S T R A C T
‘Resilience can be understood as Capacity to absorb stress or destructive forces through resistance or adaptation, the capacity to manage, or maintain certain basic functions and structures, during disastrous events, the capacity to recover or ‘bounce back’ after an event (Twigg, 2007). ‘Resilience’ is generally seen as a broader concept than ‘capacity’ because it goes beyond the specific behavior, strategies and measures for risk reduction and management that are normally understood as capacities. However, it is difficult to separate the concepts clearly. In everyday usage, ‘capacity’ and ‘coping capacity’ often mean the same as ‘resilience” The state of Orissa is located in the eastern coast of India at 17◦ 490 N to 22◦ 340 N Latitude & 81◦ 290 E to 87◦ 290 E Longitude. The state is divided into five morphological units: Mountainous and Highlands Region, Coastal Plains, Western Rolling Uplands, Central Plateaus and Flood Plains. It has been found out that the state is hub of disasters. The state was recurrently victimized by climatic chaos (floods, droughts, flash flood, cyclone, heat wave, high risk zone for earth quake & lightning) causing people more vulnerable and pushing state development more backward. Magnitude of poverty, hunger, trafficking, distress migration followed by social exclusion has widened the development gap many fold, despite the presence of rich resource base. In this context, there is an increasing need to identify which community characteristics are most resilient to disasters. This research paper proposes method to quantify community resilience. The factor analysis method results in a weighted additive index model of 9 variables to derive district wise community resilience. These variables are from five capital groups namely Social Capital, Economic Capital, Human Capital, Physical Capital and Natural Capital. For this purpose, data related to Rural School, Health Center, Par capital income, Percentage of above Poverty Line Families, Educational attainment of the population, which can be measured by the number of years of formal schooling of the average person, People having concrete House, Road density, Forest cover and Access to safe drinking water are taken. This study represents a preliminary attempt in quantifying community resilience. It outlines the method that can be used to define resilience index and offers a general guideline about the variables that might contribute to a communities’ ability to recover from a disaster. The 30 districts of Orissa is ranked according to the score and categorises the districts into five groups i.e. least, low, moderately, high and highly resilient.
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Meher M K et al.
Introduction
Over the last century human beings have been over exploiting the earth resources for the sake of its comfort, which leads to generation of waste infusing the environment in many dimensions thus resulting in many changes in the earth. The recently observed climate change and the frequent natural disaster is an indicator of this change. Previously many attempts have been made to manage the natural disaster but it goes in vain. Presently we are investing most of our resources to mitigate the disaster, less emphasis is being given to the Resilience mechanism. The disaster resilience can be defined as the “Capacity to Bounce Back”. Resilience in social systems has the added capacity of humans to plan and anticipate the future. Humans are also part of the natural world and depend on the ecosystems in which they live to survive. They continuously impact these ecosystems and contribute to their structure and functions. Orissa is located in the eastern coast of India at 17◦ 0 49 N to 22◦ 340 N Latitude & 81◦ 290 E to 87◦ 29’E Longitude. Surrounded by Andhra Pradesh on the South-East, Chattishgarh on the west, Jharkhand in north, west Bengal in North-east and placeBay of Bengal in the east, it occupies a total area of 155,707 square kilometers. The state is bound on the east by the 460 kilometer coastline of placeBay of Bengal of which the Chilika lake is a part. Climate of Orissa is usually humid or hot and moist. The state is divided into five morphological units. These five units are Mountainous and Highlands Region, Coastal Plains, Western Rolling Uplands, Central Plateaus and Flood Plains. Known as the hub of disasters, the state was recurrently victimized by climate chaos (floods, droughts, flash flood, cyclone, heat wave & lightning) causing people more vulnerable and pushing state development backward. Magnitude of fear, deprivation, poverty, hunger, trafficking, distress migration and distress sale followed by social exclusion has widened the development gap many fold, despite rich resource base. The research objective of this study is to use the concepts of Community Disaster Resilience and vulnerability to empirically define a set of indicators that can measure elements of Community Disaster Resilience. This set of indicators can be applied across scales and contains elements of adaptive capacity, Social Capital, Economic Capital, Human Capital, Physical Capital, Natural Capital and measures of self governance.
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Data sources and methodology
The data used for the purpose of this study were collected from various secondary sources. The main sources are from Rural School (2008) (OPEPA), Health Center (2008) (Director of Health Services), Par capital Income (2009–10) (Directorate of Economics and Statistics), BPL (1997) (Department of Panchayat Raj), Educational attainment of the population (measured by the number of years of formal schooling of the average person) (2001)(Census), Access to Safe Drinking 2011
Water and People having concrete House (2007–08) (DLHS-3), Road (2008) information’s from Chief Engineer NH, Road and Rural Works, Panchayat raj and Principal Chief Conservator of forest Orissa. To derive a composite index from a set of indicators, a wide variety of multivariate statistical techniques are available. The choices of most appropriate method depend upon the type of problem, the nature of data and objective. Many studies of social vulnerability are found in risk management literature by Peacock and Ragsdale 1997; Anderson and Woodrow 1998; Alwang, Siegel et al., 2001; Conway and Norton 2002, vulnerability as a framework for measuring resilience by Cutter et al.(2003). However, social vulnerability is a preexisting condition that affects a society’s ability to prepare for and recover from a disruptive event. The theme of multivariate analysis is simplification and to summarize a large body of data by means of relatively few parameters (Chatfield & Collins, 1980). Many studies of social vulnerability are found in risk management literature Peacock and Ragsdale 1997; Anderson and Woodrow 1998; Alwang, Siegel et al., 2001; Conway and Norton 2002, vulnerability as a framework for measuring resilience by Cutter et al., (2003). The calculations for identifying Community Resilience Index of districts of Orissa are multiplication of the standardized neighborhood total values for each of the variable and the dependant variable’s weight, which was found previously with the Principal Component Analysis Method. The last step of the calculation involves the summation of all weighted variable’ values for each of the neighborhood and ranked according to hierarchy of their variables.
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Discussion
The disaster history of state during last 100 years shows that state experienced 56 times flood, 40 Drought, 11 Cyclone and innumerous heat Wave, Lightning & Hail Storms. Most part of the state is vulnerable to natural disaster. Every year people experience at least a single disaster. During some of the year more than one disaster also experience by the people. The state is one of the most disaster prone state in the country in terms of loss of live and property followed by vulnerability and low resilience. The calculations for identifying Community Resilience Index levels of districts of Orissa are multiplication of the standardized neighborhood total values for each of the variable and the dependant variable’s weight (Table 1), which was found previously with the Principal Component Analysis Method. The last step of the calculation involves the summation of all weighted variable’s values for each of the neighbourhood and ranked according to hierarchy of their variables. The composite index for the different districts is obtained by post multiplying the Eigen vector with data matrix. The districts are grouped in to five categories on the basis of quartile methods. The Table 2 shows the Index for the different districts and Table 3 shows the districts coming under Disaster Risk Vulnerablity Conference
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Table 1. Weightage given to the various indicator using first principal component. Sl No 1 2 3 4 5 6 7 8 9
Indicator Rural school (primary no) Health center (Total medical institution) Par capital NDDP(2004-05) APL Educational attainment of the population People having concrete house Road density Forest cover Access to safe drinking water
Weight 0.317 0.490 0.270 0.665 0.824 0.909 0.631 0.596 0.547
Table 2. Resilience Index developed for different district of Orissa. Sl No 1 2 3 4 5
Ranking Highly resilience High resilience Moderately resilience Low resilience Least resilience
Districts and score Sundargarh, Sambalpur, Jagatsinghpur, Cuttack, Khurda, Ganjam Gajapati, Dhenkanal, Jharsuguda, Puri, Jajpur, Anugul Balasore, Mayurbhanj, Bargarh, Keonjhar, Nayagarh, Kendrapada Kalahandi, Bhadrak, Koraput, Rayagada, Kandhamal, Bolangir Malkangiri, Nuapada, Boudh, Sonepur, Nabarangapur, Deogarh
Table 3. Categorization of district according to the Resilience. Sl No 1 2 3 4 5 6 7 8 9 10
Name of the district Anugul Balasore Bargarh Bhadrak Bolangir Boudh Cuttack Deogarh Dhenkanal Gajapati
Score
Sl No
0.694 0.541 0.547 0.468 0.510 0.404 0.806 0.438 0.617 0.605
11 12 13 14 15 16 17 18 19 20
Name of the district Ganjam Jagatsinghpur Jajpur Jharsuguda Kalahandi Kandhamal Kendrapada Keonjhar Khurda Koraput
different categories. The Districts those are coming under the Most Highly Resilience are Sundargarh, Sambalpur, Jagatsinghpur, Cuttack, Khurda, Ganjam. These districts are advance in term of cultural, educational, infrastructure and industrial development apart from economical front. Gajapati, Dhenkanal, Jharsuguda, Puri, Jajpur, Anugul comes under the High Resilience Categories. Except Gajapati other districts have good communication network, education and economic development where as Gajapati district bears the high resilience categories in environmental capital. Balasore, Mayurbhanj, Bargarh, Keonjhar, Nayagarh, Kendrapada are coming under Moderately Resilience Districts and are good in environment and average in most of other indicator particularly economic capital and social capital. The Districts those are coming under the Low Resilience are Kalahandi, Bhadrak, Koraput, Rayagada, Kandhamal, Bolangir. Similarly Malkangiri, Nuapada, Boudh, Sonepur, Nabarangapur, Deogarh districts of southern and western Orissa are coming under Least Resilience Districts. These districts are poor in the HDI. The District Bhadrak is only costal district coming under these categories due to its low development in economic, institutional and environmental capital. Figure 1 Disaster Risk Vulnerablity Conference
Score
Sl No
1.008 0.756 0.675 0.639 0.448 0.504 0.586 0.561 0.911 0.470
21 22 23 24 25 26 27 28 29 30
Name of the district Malkangiri Mayurbhanj Nabarangapur Nayagarh Nuapada Puri Rayagada Sambalpur Sonepur Sundargarh
Score 0.339 0.544 0.427 0.577 0.386 0.666 0.498 0.731 0.419 0.721
shows the ranking of districts of Orissa in terms of resilience.
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Conclusion
The main purpose of this paper has been to develop a vigorous tool for use in Resilience assessment. Such a tool allows for comparative analysis and enables more in-depth exploration of the qualitative conditions that contribute to the quantitative results. Exercising any developing tool and critically examining the results are necessary to improve such a tool. This study of state of Orissa and its districts demonstrates the value of the Resilience indexing methodology given that it provides insight into resilience at state/district levels, enabling analysts to ask the next level of questions and explore directions for specific policy options that may mitigate disaster impacts. Because we have a tough framework and methodology to evaluate recent vulnerability and resilience, we have a basis on which to begin developing realistic scenarios and analyses for projections of future resilience 2011
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Figure 1. Showing the ranking of districts of Orissa in terms of resilience.
to disaster. Such scenarios must assimilate information about current Social Capital, Economic Capital, Human Capital, Physical Capital and Natural Capital, this integrated and differentiated information into the future. It may be precisely in this area that the most progress could be made in combining the more quantitative and more qualitative approaches to resilience.
References Alwang, J, Siegel, P B, et al., (2001). Vulnerability: A view from different disciplines. Social Protection Discussion Paper Series No. 0115. Washington, D.C., Social Protection Unit, Human Development Network, The World Bank. Anderson, M B and Woodrow, P J, (1998). Rising from the Ashes: Development Strategies in Times of Disaster. London, IT Publications. Bajpai, K, (2000) Human Security: Concepts and measurement; Occasional paper 19, The Joan B.Groc In-
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stitute for International Peace Studies, University of Notre Dere. Chatfield, C, and Collins, A J, (1980). Introduction to Multivariate Analysis, London; Chapman and Hall. Conway, T and Norton, A, (2002). ‘Poverty, Risk and Rights: New Directions in Social Protection.’ Development Policy Review 20(5). Cutter, S L, Mitchell, J T, et al., (2000). ‘Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, StateSouth Carolina.’ Annals of American Geographers 90(4): 713-737. Peacock, W G and Ragsdale, A K, (1997). Social Systems, Ecological Networks and Disasters. Hurricane Andrew: Ethnicity, Gender and the Sociology of Disasters. Twigg J, (2007) Characteristics of a disasterresilient community: A Guidance Note Version 1, 20p (www.phree-way.org/resources/documents/ characteristics-resilient.doc)
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