Identification of Vulnerable Areas in Municipal Corporation of Greater Mumbai Due to Extreme Events Based on Socio Economic Indicators

Indian Journal of Geo-Marine Sciences Vol. 42 (7), November 2013, pp. 907-914

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

Abhijat Arun Abhyankar, 1Mukta Paliwal, 2Anand Patwardhan & 2Arun B. Inamdar National Institute of Construction Management and Research, Pune, India 1SAS Research and Development, Indian Institute of Technology, 2Institute of Technology, Bombay, Mumbai, Pin-400 076 India [E-Mail: [email protected]] 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 namely, 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 Present study tries to determine wards with high India has a long coastal with 9 coastal states, 3 of the exposure and with low relief profiles. The specific 4 major metropolitan cities are located near the coastal objective of the present study is to identify wards with region. Natural disasters struck the Indian coastline possible high exposure and low relief capacity to storm which impacts the coastal region, its activities due to based on socioeconomic data using cluster analysis. low-lying coastal area and high population density. It Materials and Methods was found that out of total of 964 cyclonic events crossing Indian coastline between 1877-1990, total of Study area 65 cyclonic events of different intensities crossed Mumbai is located on the western seacoast of India western coastline. Further, 11 cyclonic events crossed on the Arabian Sea at 18°053` N to 19°16` N latitude and Maharashtra coast during this time period. Municipal 72° E to 72°59` E longitude. Mumbai city is divided into Corporation of Greater Mumbai has two coastal district two revenue districts, Mumbai City District, i.e. the namely, Mumbai and Mumbai suburban district. island city in the South and Mumbai Suburban District Mumbai and its suburban district have just faced only comprising the Western and Eastern suburbs. Mumbai one severe storm during this period1. In 2005, Mumbai occupies an area of 468 sq. kms and its width is 17 kms. experienced unprecedented flooding, causing direct east to west and 42 kms north to south4. economic damages estimated at almost two billion Municipal Corporation of Greater Mumbai (MCGM) USD and 500 fatalities. It is estimated that in suburban is responsible for governance of the GMR or Mumbai Mumbai, 174,885 houses were partially damaged and city. The city is divided into different administrative 2,000 fully damaged, costing Rs. 29,800 lakhs zones known as ‘wards’ to ease the day-to-day ($70 million) and Rs. 800 lakhs ($1.9 million) functioning of the civic authority. Map of Mumbai city, respectively2. It is important to reduce impacts due to including the location of different administrative wards these kinds of extreme events. The socio economic is shown in Figure 1. In all MCGM is divided into factors especially Human Development Index (HDI) 24 municipal wards of which the Mumbai city district is plays an important role in reducing overall impacts and divided into nine municipal wards and the Mumbai vulnerability due to these disasters at country level3. suburban district has 16 municipal wards. 908 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013

population density, percent of slum population and total literacy whereas relief data had five parameters namely, 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 vulnerability6. Cluster analysis has been used widely in other disciplines as diverse as social sciences (market segementation7), computer science (image segmentation8), and biology (clustering of genes9). Kaufman and Rousseeuw10 is a good introduction to traditional cluster analysis techniques. In this study, we have used unsupervised classification technique namely, 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 Figure 1Mumbai-ward wise map on variables are similar11. This method can be used Mumbai and Mumbai Suburban district is seeing with two different approaches; divisive and rapid urbanization leading to change in land cover agglomerative hierarchical techniques. In this work pattern, increase in industrialization, and increase in we have used agglomerative approach which starts by air and coastal marine pollution. Demographic assigning each object to its own cluster and at every changes and migration has led to increase in step, merging the pairs of clusters for forming a new population density of these regions over past five cluster according to the similarities between the decades. Unplanned development has lead to clusters until a cluster which contains all objects is overcrowding of these districts. This has lead to found or a certain stopping criterion is met. Further, pressure on housing, drinking water, energy, different algorithms are developed for hierarchical sanitation and transportation network. It is to be noted agglomerative method with respect to the criteria that that Mumbai is the financial capital of India and also indicate how the pairs of the clusters are merged. We have a large port facility. The geographical location of have used Ward’s linkage criterion, accordingly new the city and its physical, economic and social clusters are formed by determining the smallest characteristics make the city more vulnerable to the increase in overall sum of the squared within-cluster threats posed by climate risks, such as, sea level rises, distances among all possible clusters. storms and floods. Any natural calamity or hazard or disaster would severely impact the economy. Results and Discussion We have initiated the analysis by ranking the wards Data Analysis based on individual parameters for both exposure and Ward wise socioeconomic data of MCGM was relief measures. Table 3 depicts the ranking of Mumbai obtained mainly from Human Development Report of wards based on individual exposure parameters. It can 20095. Selected socioeconomic data was further be seen from Table 3 that number of household are classified into two parameters namely exposure and maximum in K/E ward, population density is highest in relief. Four parameters for exposure and five C ward, percent of slum population highest in S ward parameters for relief were selected. Exposure data had and lowest literacy rate in M/E ward. These are the four parameters namely, No. of Households, top wards for individual exposure parameters. Table 4 ABHYANKAR et al.: VULNERABLE AREAS IN MUNICIPAL CORPORATION OF GREATER MUMBAI 909

Table 1Mumbai-ward wise exposure data

Ward name Households population density % of slum population Total Literacy % A 43,661 16,868 28.9 75.5 B 27,225 56,253 13.3 75.7 C 39,657 112,734 0.0 83.5 D 79,131 58,006 9.9 82.4 E 80,970 59,505 11.9 75 F/S 80,777 28,294 35.8 80.1 F/N 112,765 40,338 58.1 74.9 G/N 120,643 63,957 55.8 75.3 G/S 92,525 45,793 33.1 79.1 H/E 114,423 43,025 78.8 76 H/W 73,874 29,085 41.1 81 K/W 149,161 29,944 45.1 77.8 K/E 175,859 32,661 58.3 79.7 P/S 95,188 17,945 48.1 77.2 P/N 171,009 41,821 63.7 75.3 R/S 128,995 33,140 55.3 75.9 R/C 117,294 10,262 33.7 81.8 R/N 83,433 20,213 46.6 78.6 L 151,964 48,945 84.7 73.5 M/E 133,416 20,765 77.5 66.1 M/W 86,911 21,233 68.5 75 N 129,228 23,829 70.2 77.5 S 148,731 10,800 85.8 78.5 T 73,540 7,273 35.2 81.1

Table 2Mumbai-ward wise relief data

Ward name Total Schools No. of seats in toilet Refuse Generated in total no. of available open Total health units blocks MT/day spaces A 39 215 399 40 49 B 32 40 163 24 70 C 24 0 254 42 87 D 76 695 549 70 288 E 87 966 484 57 377 F/S 69 2631 340 72 117 F/N 122 2349 383 97 440 G/N 93 3985 619 76 338 G/S 74 2154 444 66 364 H/E 86 4945 366 17 123 H/W 81 1660 408 77 157 K/W 127 2474 445 84 481 K/E 141 7850 496 75 907 P/S 80 2371 352 67 140 P/N 158 6378 359 122 125 R/S 94 3727 254 85 303 R/C 98 2712 276 104 250 R/N 63 2750 147 74 113 L 164 5402 584 45 548 M/E 106 5461 273 31 53 M/W 90 3172 274 58 443 N 122 5537 331 39 124 S 144 8380 384 52 475 T 84 1712 246 47 116 910 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013

Table 3Ranking of Mumbai wards based on individual exposure parameter

Ward No. of Ward name population Ward name % of slum Ward name Total name Households density population 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

Ward Total Schools Ward No. of seats in Ward Refuse Generated Ward total no. of available Ward Total health name name toilet blocks name in MT/Day name open spaces name 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

ABHYANKAR et al.: VULNERABLE AREAS IN MUNICIPAL CORPORATION OF GREATER MUMBAI 911

depicts the ranking of Mumbai wards based on (cluster 1: low exposure and cluster 2: high exposure) individual relief parameters. In case of relief, the least and relief parameters (cluster 1: low relief capacity no. of schools are in C ward, least no. of toilet blocks in and cluster 2: high relief capacity). Cluster means are C ward, least refuse generated in MT in R/N ward, calculated to determine the profiles of these clusters lowest no. of available open spaces in H/E ward and and are present in the Tables 7 and 8. On the basis of least no. of health units A ward. these means we have identified these clusters as low With the objective to group these wards on the exposure and high exposure for the exposure basis of similar socioeconomic characteristics, a parameters. Similarly for the relief parameters clusters hierarchical cluster analysis was performed. This are identified and are named as low relief capacity analysis was carried two times, one for exposure with and high relief capacity. Further, we have presented four variables and second for relief measures with the cluster means of exposure and relief variables as five variables. Since not all the variables were on the mean plots in Figures 4 and 5 respectively to make same scale, the standardized values (z-values) were these clusters more interpretable. From Figure 4 it used in the analyses. To determine the number of can be seen for exposure variables; for cluster 1, the clusters present in the data set an initial hierarchical mean values for number of households, population cluster analysis was carried out. Although there are no density, percent of slum population are lower in formal rules to determine the number of clusters12, comparison to cluster 2 but higher literacy rate for some heuristics have been suggested. By observing cluster 1 Hence the names low exposure and high the coefficients which indicate the distance between exposure for Cluster 1 and 2 respectively. Similarly, each cluster, it should be possible to see a sudden from Figure 5 it can be seen for relief variables; for jump in the distance between the coefficients. The cluster 1, the mean values for total school, no. of stage before the sudden change indicates the optimal seats in toilet block, Refuge generated, Open spaces stopping point for merging clusters11. and total health unit are lower than cluster 2 hence The hierarchical cluster analyses, based on the the name low relief capacity for Cluster 1 and high value of semipartial R-square, suggested two clusters relief capacity for Cluster 2. Figure 6 shows spatial in the data set corresponding to the exposure and distribution of Mumbai wards with low and high relief measures. Corresponding dendograms are exposure based on cluster analysis. Figure 7 shows presented in Figures 2 and 3 for exposure and relief spatial distribution of Mumbai wards with low and parameters respectively illustrating the membership of high relief capacity based on cluster analysis. It can the wards included in the clusters produced by the be seen from Table 5 and Table 6 that six wards hierarchical clustering. namely, B, C, H/E, K/E, N and M/E The wards are then classified into these two have high exposure and low relief capacity. The clusters and these cluster memberships are presented extreme events like floods, heavy monsoon in Tables 5 and 6 corresponding to exposure would have high impact on these identified six wards

Figure 2-Dendogram for exposure parameters illustrating the Figure 3-Dendogram of relief parameters illustrating the membership of the clusters produced by hierarchical clustering. membership of the clusters produced by hierarchical clustering. 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) Ward name Households population density % of slum population Total Literacy % Cluster No. 1 A 43661 16868 28.88018 75.5 1 2 B 27225 56253 13.32973 75.7 2 3 C 39657 112734 0 83.5 2 4 D 79131 58006 9.945904 82.4 2 5 E 80970 59505 11.86142 75 2 6 F/S 80777 28294 35.75994 80.1 1 7 F/N 112765 40338 58.06714 74.9 2 8 G/N 120643 63957 55.82167 75.3 2 9 G/S 92525 45793 33.0849 79.1 1 10 H/E 114423 43025 78.78692 76 2 11 H/W 73874 29085 41.06245 81 1 12 K/W 149161 29944 45.10832 77.8 2 1 K/E 175859 32661 58.29936 79.7 2 14 P/S 95188 17945 48.09672 77.2 1 15 P/N 171009 41821 63.65184 75.3 2 16 R/S 128995 33140 55.30466 75.9 2 17 R/C 117294 10262 33.74932 81.8 1 18 R/N 83433 20213 46.6326 78.6 1 19 L 151964 48945 84.67704 73.5 2 20 M/E 133416 20765 77.54671 66.1 2 21 M/W 86911 21233 68.48376 75 1 22 N 129228 23829 70.21302 77.5 2 23 S 148731 10800 85.83287 78.5 2 24 T 73540 7273 35.20647 81.1 1

Table 6Classification of Mumbai wards into low and high relief capacity based on cluster analysis

Observation No. Ward Total Schools No. of seats in Refuse Generated in Total no. of available Total health Cluster (OBS) name toilet blocks MT/day open spaces units No. 1 A 39 215 399 40 49 1 2 B 32 40 163 24 70 1 3 C 24 0 254 42 87 1 4 D 76 695 549 70 288 2 5 E 87 966 484 57 377 2 6 F/S 69 2631 340 72 117 1 7 F/N 122 2349 383 97 440 2 8 G/N 93 3985 619 76 338 2 9 G/S 74 2154 444 66 364 2 10 H/E 86 4945 366 17 123 1 11 H/W 81 1660 408 77 157 1 12 K/W 127 2474 445 84 481 2 1 K/E 141 7850 496 75 907 1 14 P/S 80 2371 352 67 140 2 15 P/N 158 6378 359 122 125 2 16 R/S 94 3727 254 85 303 2 17 R/C 98 2712 276 104 250 2 18 R/N 63 2750 147 74 113 1 19 L 164 5402 584 45 548 2 20 M/E 106 5461 273 31 53 1 21 M/W 90 3172 274 58 443 2 22 N 122 5537 331 39 124 1 23 S 144 8380 384 52 475 2 24 T 84 1712 246 47 116 1 ABHYANKAR et al.: VULNERABLE AREAS IN MUNICIPAL CORPORATION OF GREATER MUMBAI 913

Figure 6-Spatial distribution of low and high exposure Mumbai wards based on cluster analysis Figure 4-Means of individual exposure variables in Cluster 1 (low exposure) and Cluster 2 (high exposure)

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 % of Slum Total Figure 5-Means of individual relief variables in cluster 1 Density Population Literacy % (low relief capacity) and cluster 2 (high relief capacity) 1 83022.56 21885.11 41.22 78.82

2 117545.13 45048.20 51.23 76.47 Table 8The means of individual relief variables in each cluster after cluster analysis

Cluster Total School No. of seats in Toilet Refuse Generated in Total no. of available open Total health Units Blocks MT/Day space 1 71.45 24.84 298.09 48.18 104.45 2 112.92 38.65 427.00 76.23 410.69 914 INDIAN J. MAR. SCI., VOL. 42, NO. 7, NOVEMBER 2013

Conclusion coastal India, International Symposium on Natural Hazards, Present study indicates the high exposure zones Hyderabad, February 24-28 (2004). 2 Hallegatte S., Coastal Cities, Climate Change Vulnerability and low relief zones based on socio-economic data. and Adaptation, OECD Project led by Jan Corfee Morgot, Similar work could be carried out on Indian coastal www.oecd.org/dataoecd/31/34/44104953.pdf (last accessed districts/block to identify possible high impact area on June 8, 2012). due to a hazard. This type of study would play a 3 UNDP-Bureau for Crisis Prevention and Recovery, Reducing crucial role in policy formulation, crop insurance Disaster Risk: A challenge for Development, UNDP, 2004 (last accessed on July 2, 2012) sector, land cover policy formulation etc. 4 MCGM, Statistics on Mumbai, Municipal Corporation of The present study doesn’t subgroup Mumbai wards Greater Mumbai, available at http://www.mcgm.gov.in (last into coastal and non coastal. It is possible that accessed on June 5, 2012), 2007. vulnerability and impacts to coastal and non coastal 5 http://mhupa.gov.in/W_new/Mumbai%20HDR%20Complete wards due to extreme events may be different. .pdf (last accessed on June 2, 2012). 6 Sharma U and Patwardhan A, Methodology for identifying Development of physical infrastructure index for vulnerability hotspots to tropical cyclone hazard in India, these wards may lead to new vulnerability index Mitigation and Adaptation Strategies for Global Change, 13, which can be integrated with socio-economic (7) (2008), 703-717. parameter and index in future. This analysis presented 7 Goyat, S., The basis of market segmentation: a critical review of literature, European Journal of Business and in this work is exploratory and further refinements for Management, 3(9), pg 45, 2011. the clustering algorithms would be carried out in near 8 Vincken, K., Koster, A. and Viergever, M.: Probabilistic future. Further we are trying to get some more data on multiscale image segmentation, IEEE Transactions on hazard exposure, impact and vulnerability and related Pattern Analysis and Machine Intelligence, 19(2), statistical analysis will be carried out in order to pp. 109–120, (1997). 9 D.J. Witherspoon, S. Wooding, A.R. Rogers, E.E. Marchani, strengthen the results of the present study. W.S. Watkins, M.A. Batzer and L.B. Jorde., Genetic Similarities Within and Between Human Populations (2007) Acknowledgement by Genetics, 176(1), 351–359. Authors thank Department of Science and 10 Kaufman, Leonard Peter J. Rousseeuw, 1990: Finding Groups in Data: An Introduction to Cluster Analysis, New Technology, New Delhi for sponsoring this York: John Wiley and Sons. research. 11 Aldenderfer M.S. and Blashfield, R. K, Cluster Analysis, Volume 07-044, 10th SAGE Publications Ltd., London, References 1984.

1 Abhyankar A., Singh A., Sharma U., Patwardhan A. and 12 Everitt, B. S., Landau, S. and Leese, M. Cluster analysis, Inamdar, A., Constructing a tropical cyclone hazard index for Arnold, London, 2001.