DOI: https://doi.org/10.14710/geoplanning.11.2.121-138

Informal Settlement Characterization and Socio-Economic Vulnerability Assessment in Kolkata Metropolitan City, India

Shravani Banerjee orcid  -  Department of Geoinformatics, Central University of Jharkhand, Ranchi -834205. India, India
Diksha Diksha orcid  -  Department of Geoinformatics, Central University of Jharkhand, Ranchi -834205. India, India
Alisha Prasad orcid  -  Department of Geoinformatics, Central University of Jharkhand, Ranchi -834205. India, India
*Amit Kumar orcid  -  Department of Geography, Sikkim University, Gangtok-737102, India, India

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Abstract

The study investigates the physical, social, and economic environment of the Kolkata Metropolitan Area (KMA) to elucidate the living conditions of informal settlements and its influence on the local environment using geoinformatics and multi-criteria decision making-analytical hierarchical process (MCDM-AHP). The informal settlements were delineated using high-resolution Google Earth imagery and generic ontology informal settlements. knowledge considering building characteristics, building density, locations of the dwelling units, and their characteristics. The study exhibits that most informal settlements were concentrated in the wards located in the eastern and central parts of the city. The neighborhood land-use functions of the major informal settlements indicated that the informal settlements were highly influenced by green space (R2=0.97), followed by water bodies (R2=0.74), unplanned settlement (R2=0.68) and planned settlement (R2=0.67) in KMA. In addition, the informal settlements were closely associated with very low relief zones (3m to 13m) followed by moderate relief zones (13-23m). The municipal ward-level analysis of the physical-socio-economic health conditions exhibited that most of the areas located in the low vulnerable zones (53.71 km2; primarily in southern, and eastern periphery), followed by very highly vulnerable zones (43.09 km2; primarily in central and northern parts). The study provides an insight into urban areas with special reference to informal settlements and necessitates the implication of effective policy for poverty alleviation. This study encourages the availability of real-time data that can improve mitigation activities in the event of a health disaster, such as SARS COVID-19 through methods for qualitative investigation of disadvantaged locations in Kolkata.

 

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Keywords: Slums Ontology, Informal Settlements, Geoinformatics, AHP

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Section: Original Research
Language : EN
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  1. Agarwal, S. (2011). The state of urban health in India; comparing the poorest quartile to the rest of the urban population in selected states and cities. Environment and Urbanization, 23(1), 13–28. https://doi.org/10.1177/0956247811398589">[Crossref]

  2. Ajibade, I., & McBean, G. (2014). Climate extremes and housing rights: A political ecology of impacts, early warning and adaptation constraints in Lagos slum communities. Geoforum, 55, 76–86. https://doi.org/10.1016/j.geoforum.2014.05.005">[Crossref]

  3. Ali, J., & Islam, J. (2015). Slums as a barrier to urban development in Kolkata a case study of urban planning. Indian Journal of Spatial Science, 6(2), 1–9.

  4. Ali, M. H., & Sulaiman, M. S. (2006). The causes and consequences of the informal settlements in Zanzibar. XXIII Congress of the International Federation of Surveyors, Munich, Germany. https://www. fig. net/resources/proceedings/fig_proceedings/fig2006/papers/ts35/ts35_01_ali_sulaiman_0320. pdf

  5. Awadall, H. I. (2013). Health effect of slums: A consequence of urbanization. Scholarly Journal of Medicine, 3(1), 7–14.

  6. Basu, J. (2020). COVID-19: Does Kolkata face community transmission. Down To Earth.  https://www.downtoearth.org.in/health/covid-19-does-kolkata-face-community-transmission-70586">  

  7. Bhattacharya, N., & Chatterjee, A. K. (1973). Some characteristics of jute industry workers in Greater Calcutta. Economic and Political Weekly, 297–308. https://doi.org/https:/www.jstor.org/stable/4362301">[Crossref]

  8. Bose, R., & Ghosh, S. (2015). Slums in Kolkata: A socio-economic analysis. The Empirical Econometrics and Quantitative Economics Letters, 3(3), 134–148.

  9. Braun, B., & Aßheuer, T. (2011). Floods in megacity environments: vulnerability and coping strategies of slum dwellers in Dhaka/Bangladesh. Natural Hazards, 58(2), 771–787. https://doi.org/10.1007/s11069-011-9752-5">[Crossref]

  10. Dana, T. (2011). Unhygienic living conditions and health problems: a study in selected slums of Dhaka city. OIDA International Journal of Sustainable Development, 2(11), 27–34.

  11. Das, B., Khara, U., Giri, P., & Bandyopadhyay, A. (2012). The Challenge of Slum Development in India: A Case Study of Meltala-Dasnagar Slum Area of Howrah Municipal Corporation. Int. J. Adv. Syst. Soc. Eng. Res, 2, 22–27.

  12. Duque, Juan C, Patino, J. E., Ruiz, L. A., & Pardo-Pascual, J. E. (2015). Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning, 135(1), 11–21. https://doi.org/10.1016/j.landurbplan.2014.11.009">[Crossref]

  13. Duque, Juan Carlos, Vélez‐Gallego, M. C., & Echeverri, L. C. (2018). On the Performance of the Subtour Elimination Constraints Approach for the p ‐Regions Problem: A Computational Study. Geographical Analysis, 50(1), 32–52. https://doi.org/10.1111/gean.12132">[Crossref]

  14. Durand, N., Derivaux, S., Forestier, G., Wemmert, C., Gancarski, P., Boussaid, O., & Puissant, A. (2007). Ontology-Based Object Recognition for Remote Sensing Image Interpretation. 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007), 1, 472–479. https://doi.org/10.1109/ICTAI.2007.111">[Crossref]

  15. Ebert, A., Kerle, N., & Stein, A. (2009). Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data. Natural Hazards, 48(2), 275–294. https://doi.org/10.1007/s11069-008-9264-0">[Crossref]

  16. Frank, A. U. (1997). Spatial ontology: A geographical information point of view. In Spatial and temporal reasoning (pp. 135–153). Dordrecht: Springer Netherlands.

  17. Gamba, P., Du, P., Juergens, C., & Maktav, D. (2011). Foreword to the Special Issue on “Human Settlements: A Global Remote Sensing Challenge.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(1), 5–7. https://doi.org/10.1109/JSTARS.2011.2106332">[Crossref]

  18. Gambo, Y. L., Idowu, O. B., & Anyakora, I. M. (2012). Impact of poor housing condition on the economy of the urban poor: Makoko, Lagos State in view. Journal of Emerging Trends in Economics and Management Sciences, 3(4), 302–307.

  19. Ghosh, S. (2013). Regional disparities of slums, 2013—An overview with special emphasis to Kolkata. International Journal of Humanities and Social Science Invention, 2(3), 48–54.

  20. Gopal, D., & Nagendra, H. (2014). Vegetation in Bangalore’s Slums: Boosting Livelihoods, Well-Being and Social Capital. Sustainability, 6(5), 2459–2473. https://doi.org/10.3390/su6052459">[Crossref]

  21. Hanchett, S., Akhter, S., Khan, M. H., Mezulianik, S., & Blagbrough, V. (2003). Water, sanitation and hygiene in Bangladeshi slums: an evaluation of the WaterAid– Bangladesh urban programme. Environment and Urbanization, 15(2), 43–56. https://doi.org/10.1177/095624780301500219">[Crossref]

  22. Hasan, A. (2006). Orangi Pilot Project: the expansion of work beyond Orangi and the mapping of informal settlements and infrastructure. Environment and Urbanization, 18(2), 451–480. https://doi.org/10.1177/0956247806069626">[Crossref]

  23. Hofmann, P. (2001). Detecting informal settlements from IKONOS image data using methods of object oriented image analysis-an example from Cape Town (South Africa). Jürgens, C.(Ed.): Remote Sensing of Urban Areas/Fernerkundung in Urbanen Räumen, 41–42.

  24. Hughes, B. B., Hanna, T., McNeil, K., Bohl, D. K., & Moyer, J. D. (2021). Pursuing the sustainable development goals in a world reshaped by COVID-19. New York, NY and Denver, CO: United Nations Development Programme and Frederick S. Pardee Center for International Futures. https://sdgintegration.undp.org/sites/default/files/Foundational_research_report.pdf">[Crossref]

  25. Jha, C. V, & Bairagya, H. (2013). Flood and flood plains of West Bengal, India: A comparative analysis. Revista Geoaraguaia, 3(1), 1–10.

  26. Joshi, P., Sen, S., & Hobson, J. (2002). Experiences with surveying and mapping Pune and Sangli slums on a geographical information system (GIS). Environment and Urbanization, 14(2), 225–240. https://doi.org/10.1630/095624702101286241">[Crossref]

  27. Kjellstrom, T., Friel, S., Dixon, J., Corvalan, C., Rehfuess, E., Campbell-Lendrum, D., Gore, F., & Bartram, J. (2007). Urban Environmental Health Hazards and Health Equity. Journal of Urban Health, 84(S1), 86–97. https://doi.org/10.1007/s11524-007-9171-9">[Crossref]

  28. Kohli, D. (2015). Identifying and classifying slum areas using remote sensing [University of Twente]. https://doi.org/10.3990/1.9789036540087">[Crossref]

  29. Kohli, D., Sliuzas, R., Kerle, N., & Stein, A. (2012). An ontology of slums for image-based classification. Computers, Environment and Urban Systems, 36(2), 154–163. https://doi.org/10.1016/j.compenvurbsys.2011.11.001">[Crossref]

  30. Kuffer, M., Pfeffer, K., & Sliuzas, R. (2016). Slums from Space—15 Years of Slum Mapping Using Remote Sensing. Remote Sensing, 8(6), 455. https://doi.org/10.3390/rs8060455">[Crossref]

  31. Kuffer, M., Pfeffer, K., Sliuzas, R., Baud, I., & Maarseveen, M. (2017). Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai. Remote Sensing, 9(4), 384. https://doi.org/10.3390/rs9040384">[Crossref]

  32. Kundu, N. (2003). The Case of Kolkata, India. Understanding Slums: Case Studies for the Global Report on Human Settlements.

  33. Lahon, S. (2017). Educational status and level of health awareness of the children of urban slums with special reference to Guwahati city-A study. International Journal of Applied Research, 3(3), 680–686.

  34. Lal, P., Kumar, A., Kumar, S., Kumari, S., Saikia, P., Dayanandan, A., Adhikari, D., & Khan, M. L. (2020). The dark cloud with a silver lining: Assessing the impact of the SARS COVID-19 pandemic on the global environment. Science of The Total Environment, 732, 139297. https://doi.org/10.1016/j.scitotenv.2020.139297">[Crossref]

  35. Lemma, T., Sliuzas, R., & Kuffer, M. (2006). A participatory approach to monitoring slum conditions: an example from Ethiopia. Participatory Learning and Action, 54(1), 58–66.

  36. Mahabir, R., Crooks, A., Croitoru, A., & Agouris, P. (2016). The study of slums as social and physical constructs: challenges and emerging research opportunities. Regional Studies, Regional Science, 3(1), 399–419. https://doi.org/10.1080/21681376.2016.1229130">[Crossref]

  37. Marques, E., & Saraiva, C. (2017). Urban integration or reconfigured inequalities? Analyzing housing precarity in São Paulo, Brazil. Habitat International, 69, 18–26. https://doi.org/10.1016/j.habitatint.2017.08.004">[Crossref]

  38. Martínez, J., Mboup, G., Sliuzas, R., & Stein, A. (2008). Trends in urban and slum indicators across developing world cities, 1990–2003. Habitat International, 32(1), 86–108. https://doi.org/10.1016/j.habitatint.2007.08.018">[Crossref]

  39. Ministry of Housing & Urban Poverty Alleviation Government of India. (2013). State of slums in India: A statistical Compendium 2013. https://nbo.gov.in/pdf/Slums_in_India_Compendium_English_Version.pdf">[Crossref]

  40. Napier, M. (2007). Informal settlement integration, the environment and sustainable livelihoods in sub-Saharan Africa. Council for Scientific & Industrial Research in South Africa.

  41. Ooi, G. L., & Phua, K. H. (2007). Urbanization and Slum Formation. Journal of Urban Health, 84(S1), 27–34. https://doi.org/10.1007/s11524-007-9167-5">[Crossref]

  42. Patel, A., Crooks, A., & Koizumi, N. (2012). Slumulation: An Agent-Based Modeling Approach to Slum Formations. Journal of Artificial Societies and Social Simulation, 15(4), 2. https://doi.org/10.18564/jasss.2045">[Crossref]

  43. Patel, A., Koizumi, N., & Crooks, A. (2014). Measuring slum severity in Mumbai and Kolkata: A household-based approach. Habitat International, 41, 300–306. https://doi.org/10.1016/j.habitatint.2013.09.002">[Crossref]

  44. Patel, R. B., & Burke, T. F. (2009). Urbanization — An Emerging Humanitarian Disaster. New England Journal of Medicine, 361(8), 741–743. https://doi.org/10.1056/NEJMp0810878">[Crossref]

  45. Pawar, D. H., & Mane, V. D. (2013). Socio-economic status of slum dwellers with special reference to women: Geographical investigation of Kolhapur Slum. Research Front, 1(1), 69–72.

  46. PTI. (2020). Tally of Dharavi’s COVID-19 patients up by 50 to 783. The Week. https://www.theweek.in/wire-updates/national/2020/05/07/bom28-mh-virus-dharavi.html">  

  47. Ray, B. (2017). Quality of life in selected slums of Kolkata: a step forward in the era of pseudo-urbanisation. Local Environment, 22(3), 365–387. https://doi.org/10.1080/13549839.2016.1205571">[Crossref]

  48. Richter, C., Miscione, G., De’, R., & Pfeffer, K. (2011). Enlisting SDI for urban planning in India: Local practices in the case of slum declaration. In Spatial data infrastructures in context.

  49. Riley, L. W., Ko, A. I., Unger, A., & Reis, M. G. (2007). Slum health: Diseases of neglected populations. BMC International Health and Human Rights, 7(1), 2. https://doi.org/10.1186/1472-698X-7-2">[Crossref]

  50. Roy, D., Lees, M. H., Palavalli, B., Pfeffer, K., & Sloot, M. A. P. (2014). The emergence of slums: A contemporary view on simulation models. Environmental Modelling & Software, 59, 76–90. https://doi.org/10.1016/j.envsoft.2014.05.004">[Crossref]

  51. Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073–1076.

  52. Satterthwaite, D. (1993). The impact on health urban environments. Environment and Urbanization, 5(2), 87–111. https://doi.org/10.1177/095624789300500208">[Crossref]

  53. Schenk, W. C. (2010). Slum diversity in Kolkata. Columbia Undergraduate Journal of South Asian Studies, 1(2), 91–108.

  54. Shekhar, S. (2013). Slum modelling by using ontology and geoinformatics: Case study of Gulbarga. International Journal of Geoinformatics.

  55. Sliuzas, R. V, Kerle, N., & Kuffer, M. (2008). Object-oriented mapping of urban poverty and deprivation. 4th EARSeL Workshop on Imaging Spectroscopy 2005: New Quality in Environmental Studies.

  56. Tomai, E., Herlin, I., Berroir, J.-P., & Prastacos, P. (2009). Ontology‐based documentation of land degradation assessment from satellite images. International Journal of Remote Sensing, 30(13), 3315–3330. https://doi.org/10.1080/01431160802558709">[Crossref]

  57. Tripathi, S. (2015). Determinants of Large City Slum Incidence in India: A Cross-Sectional Study. Poverty & Public Policy, 7(1), 22–43. https://doi.org/10.1002/pop4.93">[Crossref]

  58. Tripathy, P., & Kumar, A. (2019). Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics. Cities, 90(January), 52–63. https://doi.org/10.1016/j.cities.2019.01.021">[Crossref]

  59. Uddin, N. (2018). Assessing urban sustainability of slum settlements in Bangladesh: Evidence from Chittagong city. Journal of Urban Management, 7(1), 32–42.

  60. UN Habitat. (2003). The Challenge of Slums - Global Report on Human Settlements 2003. https://www.un.org/ruleoflaw/files/Challenge%20of%20Slums.pdf"> . 

  61. UN Habitat. (2022). World Cities Report 2022: Envisaging the Future of Cities. https://unhabitat.org/sites/default/files/2022/06/wcr_2022.pdf"> .  

  62. United Nations Development Programme. (2012). Fighting Climate Change: Human Solidarity in a Divided World (pp. 166–168). https://www.rrojasdatabank.info/hdr_20072008_en_overview.pdf

  63. Weeks, J. R., Hill, A., Stow, D., Getis, A., & Fugate, D. (2007). Can we spot a neighborhood from the air? Defining neighborhood structure in Accra, Ghana. GeoJournal, 69(1–2), 9–22. https://doi.org/10.1007/s10708-007-9098-4">[Crossref]

  64. Wekesa, B. W., Steyn, G. S., & Otieno, F. A. O. (Fred). (2011). A review of physical and socio-economic characteristics and intervention approaches of informal settlements. Habitat International, 35(2), 238–245. https://doi.org/10.1016/j.habitatint.2010.09.006">[Crossref]

  65. Zaman, T. U., Goswami, H. D., & Hassan, Y. (2018). The impact of growth and development of slums on the health status and health awareness of slum dwellers. International Journal of Medical Research & Health Sciences, 7(3), 55–65.


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