Identification of vulnerable areas in municipal corporation of Greater Mumbai due to extreme events based on socio economic indicators

Transcription

1 Indian Journal of Geo-Marine Sciences Vol. 42 (7), November 2013, pp Identification of vulnerable areas in municipal corporation of Greater Mumbai due to extreme events based on socio economic indicators Abhijat Arun Abhyankar, 1 Mukta Paliwal, 2 Anand Patwardhan & 2 Arun B. Inamdar National Institute of Construction Management and Research, Pune, India 1 SAS Research and Development, Indian Institute of Technology, 2 Institute of Technology, Bombay, Mumbai, Pin India [ abhijat1974@gmail.com] Received 9 July 2012; revised 24 November 2012 Study area, Municipal Corporation of Greater Mumbai (MCGM) has very high population density. There are a large number of environmental issues and climate related concerns which collectively make the city vulnerable to both natural and man-made hazards. Mumbai is the financial capital of India and also have a large port facility. In this scenario the effects of both man-made and natural hazards are immense on the economy. Socio-economic parameters play an important role in reducing impacts due to these events. This study identifies the exposure and relief parameters for Mumbai city and Mumbai suburban. These two districts are classified into 24 wards. To group these wards on the basis of similar socio-economic characteristics, exploratory cluster analysis was performed. Paper reports high exposure and low relief capacity wards in MCGM. It was found that six Mumbai municipal wards ly, B, C, H/E, K/E, N and M/E have high exposure and low relief capacity. In case of extreme events these wards are expected to have high impacts. Policies can be formulated for high exposure and low relief capacity wards identified in the present study. [Keywords: Municipal Corporation, Exposure, Relief capacity, Hierarchical Cluster, Socio-economic indicators] Introduction India has a long coastal with 9 coastal states, 3 of the 4 major metropolitan cities are located near the coastal region. Natural disasters struck the Indian coastline which impacts the coastal region, its activities due to low-lying coastal area and high population density. It was found that out of total of 964 cyclonic events crossing Indian coastline between , total of 65 cyclonic events of different intensities crossed western coastline. Further, 11 cyclonic events crossed Maharashtra coast during this time period. Municipal Corporation of Greater Mumbai has two coastal district ly, Mumbai and Mumbai suburban district. Mumbai and its suburban district have just faced only one severe storm during this period 1. In 2005, Mumbai experienced unprecedented flooding, causing direct economic damages estimated at almost two billion USD and 500 fatalities. It is estimated that in suburban Mumbai, 174,885 houses were partially damaged and 2,000 fully damaged, costing Rs. 29,800 lakhs ($70 million) and Rs. 800 lakhs ($1.9 million) respectively 2. It is important to reduce impacts due to these kinds of extreme events. The socio economic factors especially Human Development Index (HDI) plays an important role in reducing overall impacts and vulnerability due to these disasters at country level 3. Present study tries to determine wards with high exposure and with low relief profiles. The specific objective of the present study is to identify wards with possible high exposure and low relief capacity to storm based on socioeconomic data using cluster analysis. Materials and Methods Study area Mumbai is located on the western seacoast of India on the Arabian Sea at ` N to 19 16` N latitude and 72 E to 72 59` E longitude. Mumbai city is divided into two revenue districts, Mumbai City District, i.e. the island city in the South and Mumbai Suburban District comprising the Western and Eastern suburbs. Mumbai occupies an area of 468 sq. kms and its width is 17 kms. east to west and 42 kms north to south 4. Municipal Corporation of Greater Mumbai (MCGM) is responsible for governance of the GMR or Mumbai city. The city is divided into different administrative zones known as wards to ease the day-to-day functioning of the civic authority. Map of Mumbai city, including the location of different administrative wards is shown in Figure 1. In all MCGM is divided into 24 municipal wards of which the Mumbai city district is divided into nine municipal wards and the Mumbai suburban district has 16 municipal wards.

2 908 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013 Figure 1 Mumbai-ward wise map Mumbai and Mumbai Suburban district is seeing rapid urbanization leading to change in land cover pattern, increase in industrialization, and increase in air and coastal marine pollution. Demographic changes and migration has led to increase in population density of these regions over past five decades. Unplanned development has lead to overcrowding of these districts. This has lead to pressure on housing, drinking water, energy, sanitation and transportation network. It is to be noted that Mumbai is the financial capital of India and also have a large port facility. The geographical location of the city and its physical, economic and social characteristics make the city more vulnerable to the threats posed by climate risks, such as, sea level rises, storms and floods. Any natural calamity or hazard or disaster would severely impact the economy. Data Analysis wise socioeconomic data of MCGM was obtained mainly from Human Development Report of Selected socioeconomic data was further classified into two parameters ly exposure and relief. Four parameters for exposure and five parameters for relief were selected. Exposure data had four parameters ly, No. of Households, population density, percent of slum population and total literacy whereas relief data had five parameters ly, Total school, No. of seats in toilet blocks, refuse generated in MT/day, total no. of available open spaces and total health units. Proxy variables/parameters considered under relief integrates evacuation and rescue capacity. Term relief should be looked at immediate post response strategy. Mumbai ward wise exposure and relief data is depicted in Table 1 and Table 2 respectively. Cluster analysis was used identifying the most vulnerable coastal districts based on different combinations of the components of vulnerability 6. Cluster analysis has been used widely in other disciplines as diverse as social sciences (market segementation 7 ), computer science (image segmentation 8 ), and biology (clustering of genes 9 ). Kaufman and Rousseeuw 10 is a good introduction to traditional cluster analysis techniques. In this study, we have used unsupervised classification technique ly, hierarchical cluster analysis. Hierarchical cluster analysis is primarily an exploratory rather than confirmatory analysis. Hierarchical clustering methods group together objects into a tree of clusters whose patterns of scores on variables are similar 11. This method can be used with two different approaches; divisive and agglomerative hierarchical techniques. In this work we have used agglomerative approach which starts by assigning each object to its own cluster and at every step, merging the pairs of clusters for forming a new cluster according to the similarities between the clusters until a cluster which contains all objects is found or a certain stopping criterion is met. Further, different algorithms are developed for hierarchical agglomerative method with respect to the criteria that indicate how the pairs of the clusters are merged. We have used s linkage criterion, accordingly new clusters are formed by determining the smallest increase in overall sum of the squared within-cluster distances among all possible clusters. Results and Discussion We have initiated the analysis by ranking the wards based on individual parameters for both exposure and relief measures. Table 3 depicts the ranking of Mumbai wards based on individual exposure parameters. It can be seen from Table 3 that number of household are maximum in K/E ward, population density is highest in C ward, percent of slum population highest in S ward and lowest literacy rate in M/E ward. These are the top wards for individual exposure parameters. Table 4

3 ABHYANKAR et al.: VULNERABLE AREAS IN MUNICIPAL CORPORATION OF GREATER MUMBAI 909 Table 1 Mumbai-ward wise exposure data Households population density % of slum population Total Literacy % A 43,661 16, B 27,225 56, C 39, , D 79,131 58, E 80,970 59, F/S 80,777 28, F/N 112,765 40, G/N 120,643 63, G/S 92,525 45, H/E 114,423 43, H/W 73,874 29, K/W 149,161 29, K/E 175,859 32, P/S 95,188 17, P/N 171,009 41, R/S 128,995 33, R/C 117,294 10, R/N 83,433 20, L 151,964 48, M/E 133,416 20, M/W 86,911 21, N 129,228 23, S 148,731 10, T 73,540 7, Total Schools No. of seats in toilet blocks Table 2 Mumbai-ward wise relief data Refuse Generated in MT/day total no. of available open spaces Total health units A B C D E F/S F/N G/N G/S H/E H/W K/W K/E P/S P/N R/S R/C R/N L M/E M/W N S T

4 910 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013 No. of Households Table 3 Ranking of Mumbai wards based on individual exposure parameter population density % of slum population Total Literacy % K/E 175,859 C 112,734 S 85.8 M/E 66.1 P/N 171,009 G/N 63,957 L 84.7 L 73.5 L 151,964 E 59,505 H/E 78.8 F/N 74.9 K/W 149,161 D 58,006 M/E 77.5 E 75 S 148,731 B 56,253 N 70.2 M/W 75 M/E 133,416 L 48,945 M/W 68.5 G/N 75.3 N 129,228 G/S 45,793 P/N 63.7 P/N 75.3 R/S 128,995 H/E 43,025 K/E 58.3 A 75.5 G/N 120,643 P/N 41,821 F/N 58.1 B 75.7 R/C 117,294 F/N 40,338 G/N 55.8 R/S 75.9 H/E 114,423 R/S 33,140 R/S 55.3 H/E 76 F/N 112,765 K/E 32,661 P/S 48.1 P/S 77.2 P/S 95,188 K/W 29,944 R/N 46.6 N 77.5 G/S 92,525 H/W 29,085 K/W 45.1 K/W 77.8 M/W 86,911 F/S 28,294 H/W 41.1 S 78.5 R/N 83,433 N 23,829 F/S 35.8 R/N 78.6 E 80,970 M/W 21,233 T 35.2 G/S 79.1 F/S 80,777 M/E 20,765 R/C 33.7 K/E 79.7 D 79,131 R/N 20,213 G/S 33.1 F/S 80.1 H/W 73,874 P/S 17,945 A 28.9 H/W 81 T 73,540 A 16,868 B 13.3 T 81.1 A 43,661 S 10,800 E 11.9 R/C 81.8 C 39,657 R/C 10,262 D 9.9 D 82.4 B 27,225 T 7,273 C 0.0 C 83.5 Table 4 Ranking of Mumbai wards based for individual relief parameter Total Schools No. of seats in toilet blocks Refuse Generated in MT/Day total no. of available open spaces Total health units C 24 S 8380 R/N 147 P/N 122 A 49 B 32 K/E 7850 B 163 R/C 104 M/E 53 A 39 P/N 6378 T 246 F/N 97 B 70 R/N 63 N 5537 C 254 R/S 85 C 87 F/S 69 M/E 5461 R/S 254 K/W 84 R/N 113 G/S 74 L 5402 M/E 273 H/W 77 T 116 D 76 H/E 4945 M/W 274 G/N 76 F/S 117 P/S 80 G/N 3985 R/C 276 K/E 75 H/E 123 H/W 81 R/S 3727 N 331 R/N 74 N 124 T 84 M/W 3172 F/S 340 F/S 72 P/N 125 H/E 86 R/N 2750 P/S 352 D 70 P/S 140 E 87 R/C 2712 P/N 359 P/S 67 H/W 157 M/W 90 F/S 2631 H/E 366 G/S 66 R/C 250 G/N 93 K/W 2474 F/N 383 M/W 58 D 288 R/S 94 P/S 2371 S 384 E 57 R/S 303 R/C 98 F/N 2349 A 399 S 52 G/N 338 M/E 106 G/S 2154 H/W 408 T 47 G/S 364 F/N 122 T 1712 G/S 444 L 45 E 377 N 122 H/W 1660 K/W 445 C 42 F/N 440 K/W 127 E 966 E 484 A 40 M/W 443 K/E 141 D 695 K/E 496 N 39 S 475 S 144 A 215 D 549 M/E 31 K/W 481 P/N 158 B 40 L 584 B 24 L 548 L 164 C 0 G/N 619 H/E 17 K/E 907

5 ABHYANKAR et al.: VULNERABLE AREAS IN MUNICIPAL CORPORATION OF GREATER MUMBAI 911 depicts the ranking of Mumbai wards based on individual relief parameters. In case of relief, the least no. of schools are in C ward, least no. of toilet blocks in C ward, least refuse generated in MT in R/N ward, lowest no. of available open spaces in H/E ward and least no. of health units A ward. With the objective to group these wards on the basis of similar socioeconomic characteristics, a hierarchical cluster analysis was performed. This analysis was carried two times, one for exposure with four variables and second for relief measures with five variables. Since not all the variables were on the same scale, the standardized values (z-values) were used in the analyses. To determine the number of clusters present in the data set an initial hierarchical cluster analysis was carried out. Although there are no formal rules to determine the number of clusters 12, some heuristics have been suggested. By observing the coefficients which indicate the distance between each cluster, it should be possible to see a sudden jump in the distance between the coefficients. The stage before the sudden change indicates the optimal stopping point for merging clusters 11. The hierarchical cluster analyses, based on the value of semipartial R-square, suggested two clusters in the data set corresponding to the exposure and relief measures. Corresponding dendograms are presented in Figures 2 and 3 for exposure and relief parameters respectively illustrating the membership of the wards included in the clusters produced by the hierarchical clustering. The wards are then classified into these two clusters and these cluster memberships are presented in Tables 5 and 6 corresponding to exposure (cluster 1: low exposure and cluster 2: high exposure) and relief parameters (cluster 1: low relief capacity and cluster 2: high relief capacity). Cluster means are calculated to determine the profiles of these clusters and are present in the Tables 7 and 8. On the basis of these means we have identified these clusters as low exposure and high exposure for the exposure parameters. Similarly for the relief parameters clusters are identified and are d as low relief capacity and high relief capacity. Further, we have presented the cluster means of exposure and relief variables as mean plots in Figures 4 and 5 respectively to make these clusters more interpretable. From Figure 4 it can be seen for exposure variables; for cluster 1, the mean values for number of households, population density, percent of slum population are lower in comparison to cluster 2 but higher literacy rate for cluster 1 Hence the s low exposure and high exposure for Cluster 1 and 2 respectively. Similarly, from Figure 5 it can be seen for relief variables; for cluster 1, the mean values for total school, no. of seats in toilet block, Refuge generated, Open spaces and total health unit are lower than cluster 2 hence the low relief capacity for Cluster 1 and high relief capacity for Cluster 2. Figure 6 shows spatial distribution of Mumbai wards with low and high exposure based on cluster analysis. Figure 7 shows spatial distribution of Mumbai wards with low and high relief capacity based on cluster analysis. It can be seen from Table 5 and Table 6 that six wards ly, B, C, H/E, K/E, N and M/E have high exposure and low relief capacity. The extreme events like floods, heavy monsoon would have high impact on these identified six wards Figure 2-Dendogram for exposure parameters illustrating the membership of the clusters produced by hierarchical clustering. Figure 3-Dendogram of relief parameters illustrating the membership of the clusters produced by hierarchical clustering.

6 912 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013 Table 5 Classification of Mumbai wards into low exposure and high exposure based on cluster analysis Observation No. (OBS) Households population density % of slum population Total Literacy % Cluster No. Observation No. (OBS) 1 A B C D E F/S F/N G/N G/S H/E H/W K/W K/E P/S P/N R/S R/C R/N L M/E M/W N S T Table 6 Classification of Mumbai wards into low and high relief capacity based on cluster analysis Total Schools No. of seats in toilet blocks Refuse Generated in MT/day Total no. of available open spaces Total health units 1 A B C D E F/S F/N G/N G/S H/E H/W K/W K/E P/S P/N R/S R/C R/N L M/E M/W N S T Cluster No.

7 ABHYANKAR et al.: VULNERABLE AREAS IN MUNICIPAL CORPORATION OF GREATER MUMBAI 913 Figure 4-Means of individual exposure variables in Cluster 1 (low exposure) and Cluster 2 (high exposure) Figure 6-Spatial distribution of low and high exposure Mumbai wards based on cluster analysis Figure 5-Means of individual relief variables in cluster 1 (low relief capacity) and cluster 2 (high relief capacity) Figure 7-Spatial distribution of low and high relief capacity Mumbai wards based on cluster Analysis Table 7 The means of individual exposure variables in each cluster after cluster analysis Cluster Households Population Density % of Slum Population Total Literacy % Table 8 The means of individual relief variables in each cluster after cluster analysis Cluster Total School No. of seats in Toilet Blocks Refuse Generated in MT/Day Total no. of available open space Total health Units

8 914 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013 Conclusion Present study indicates the high exposure zones and low relief zones based on socio-economic data. Similar work could be carried out on Indian coastal districts/block to identify possible high impact area due to a hazard. This type of study would play a crucial role in policy formulation, crop insurance sector, land cover policy formulation etc. The present study doesn t subgroup Mumbai wards into coastal and non coastal. It is possible that vulnerability and impacts to coastal and non coastal wards due to extreme events may be different. Development of physical infrastructure index for these wards may lead to new vulnerability index which can be integrated with socio-economic parameter and index in future. This analysis presented in this work is exploratory and further refinements for the clustering algorithms would be carried out in near future. Further we are trying to get some more data on hazard exposure, impact and vulnerability and related statistical analysis will be carried out in order to strengthen the results of the present study. Acknowledgement Authors thank Department of Science and Technology, New Delhi for sponsoring this research. References 1 Abhyankar A., Singh A., Sharma U., Patwardhan A. and Inamdar, A., Constructing a tropical cyclone hazard index for coastal India, International Symposium on Natural Hazards, Hyderabad, February (2004). 2 Hallegatte S., Coastal Cities, Climate Change Vulnerability and Adaptation, OECD Project led by Jan Corfee Morgot, (last accessed on June 8, 2012). 3 UNDP-Bureau for Crisis Prevention and Recovery, Reducing Disaster Risk: A challenge for Development, UNDP, 2004 (last accessed on July 2, 2012) 4 MCGM, Statistics on Mumbai, Municipal Corporation of Greater Mumbai, available at (last accessed on June 5, 2012), (last accessed on June 2, 2012). 6 Sharma U and Patwardhan A, Methodology for identifying vulnerability hotspots to tropical cyclone hazard in India, Mitigation and Adaptation Strategies for Global Change, 13, (7) (2008), Goyat, S., The basis of market segmentation: a critical review of literature, European Journal of Business and Management, 3(9), pg 45, Vincken, K., Koster, A. and Viergever, M.: Probabilistic multiscale image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), pp , (1997). 9 D.J. Witherspoon, S. Wooding, A.R. Rogers, E.E. Marchani, W.S. Watkins, M.A. Batzer and L.B. Jorde., Genetic Similarities Within and Between Human Populations (2007) by Genetics, 176(1), Kaufman, Leonard Peter J. Rousseeuw, 1990: Finding Groups in Data: An Introduction to Cluster Analysis, New York: John Wiley and Sons. 11 Aldenderfer M.S. and Blashfield, R. K, Cluster Analysis, Volume , 10th SAGE Publications Ltd., London, Everitt, B. S., Landau, S. and Leese, M. Cluster analysis, Arnold, London, 2001.