University of Kansas, USA.
* Corresponding author
University of Kansas, USA.

Article Main Content

Objective: The overall goal of this investigation is to describe the pattern of distribution of various socio-economic groups in Bhubaneswar, India. Background: Social stratification studies are inconsistent across countries. While the deprived communities are clustered around the center of the city in the US and Shanghai, recent Indian studies showed that they are mostly located at the periphery of the city. Most of these studies have used census data to differentiate the socio-economic groups. However, there are limited studies that examine the spatial structure of various socio-economic neighborhoods in India. This study is unique because it combines census data and satellite images to examine how different socio-economic neighborhoods are different geographically and spatially. Method: Census tract or ward is considered representative of neighborhoods. Census data on socio-economic indicators such as household size, literacy, home ownership, car-jeep ownership, availing banking facility, and workers population are used for an empirical PCA analysis. The wards are divided into five quintiles based on their deprivation index. A spatial study is performed for two of the most deprived neighborhoods and two of the least deprived neighborhoods using satellite maps. Results: The least deprived neighborhoods are located mostly around airports. However, the most deprived neighborhoods are located at the periphery. Informal housing or slums are not clumped in deprived neighborhoods only but are distributed throughout the city. All four wards studied are heterogeneous with informal housing combined with independent housing and apartments. Conclusion: Most deprived and least deprived neighborhoods are not differentiated based on informal housing locations but on parcel and apartment size.

References

  1. Adlakha, D., Hipp, J., Sallis, J., & Brownson, R. (2018). Exploring Neighborhood Environments and Active Commuting in Chennai, India. International Journal of Environmental Research and Public Health, 15(9), 1840. https://doi.org/10.3390/ijerph15091840.
     Google Scholar
  2. Allik, M., Leyland, A., Travassos Ichihara, M. Y., & Dundas, R. (2020). Creating small-area deprivation indices: A guide for stages and options. Journal of Epidemiology and Community Health, 74(1), 20–25. https://doi.org/10.1136/jech-2019-213255.
     Google Scholar
  3. Bardhan, R., Kurisu, K., & Hanaki, K. (2015). Does compact urban forms relate to good quality of life in high density cities of India? Case of Kolkata. Cities, 48, 55–65. https://doi.org/10.1016/j.cities.2015.06.005.
     Google Scholar
  4. Bonfim, C., Aguiar-Santos, A. M., Pedroza, D., Costa, T. R., Portugal, J. L., Oliveira, C., & Medeiros, Z. (2009). Social deprivation index and lymphatic filariasis: A tool for mapping urban areas at risk in northeastern Brazil. International Health, 1(1), 78–84. https://doi.org/10.1016/j.inhe.2009.06.007.
     Google Scholar
  5. Boone, C. G.-, Buckley, G. L., Grove, J. M., & Sister, C. (2021). Parks and People: An Environmental Justice Inquiry in Baltimore, Maryland. 22.
     Google Scholar
  6. Carstairs, V. (1995). Deprivation indices: Their interpretation and use in relation to health. Journal of Epidemiology & Community Health, 49(Suppl 2), S3–S8. https://doi.org/10.1136/jech.49.Suppl_2.S3.
     Google Scholar
  7. Chapple, K. (2006). Overcoming Mismatch: Beyond Dispersal, Mobility, and Development Strategies. Journal of the American Planning Association, 72(3), 322–336. https://doi.org/10.1080/01944360608976754.
     Google Scholar
  8. Chimankar, D. A. (2016). Urbanization and Condition of Urban Slums in India. Indonesian Journal of Geography, 48(1), 28. https://doi.org/10.22146/ijg.12466.
     Google Scholar
  9. Filmer, D., & Pritchett, L. H. (2001). Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application to Educational Enrollments in States of India. 38(1), 18.
     Google Scholar
  10. Guillaume, E., Pornet, C., Dejardin, O., Launay, L., Lillini, R., Vercelli, M., Marí-Dell’Olmo, M., Fernández Fontelo, A., Borrell, C., Ribeiro, A. I., Pina, M. F. de, Mayer, A., Delpierre, C., Rachet, B., & Launoy, G. (2016). Development of a cross-cultural deprivation index in five European countries. Journal of Epidemiology and Community Health, 70(5), 493–499. https://doi.org/10.1136/jech-2015-205729.
     Google Scholar
  11. Hess, D. B. (2005). Access to Employment for Adults in Poverty in the Buffalo-Niagara Region. Urban Studies, 42(7), 1177–1200. https://doi.org/10.1080/00420980500121384.
     Google Scholar
  12. Janevic, T., Stein, C. R., Savitz, D. A., Kaufman, J. S., Mason, S. M., & Herring, A. H. (2010). Neighborhood Deprivation and Adverse Birth Outcomes among Diverse Ethnic Groups. Annals of Epidemiology, 20(6), 445–451. https://doi.org/10.1016/j.annepidem.2010.02.010.
     Google Scholar
  13. Kuffer, M. (2017). Spatial patterns of deprivation in cities of the global south in very high resolution imagery [PhD, University of Twente]. https://doi.org/10.3990/1.9789036543699.
     Google Scholar
  14. McKenzie, D. J. (2005). Measuring inequality with asset indicators. Journal of Population Economics, 18(2), 229–260. https://doi.org/10.1007/s00148-005-0224-7.
     Google Scholar
  15. Messer, L. C., Laraia, B. A., Kaufman, J. S., Eyster, J., Holzman, C., Culhane, J., Elo, I., Burke, J. G., & O’Campo, P. (2006). The Development of a Standardized Neighborhood Deprivation Index. Journal of Urban Health, 83(6), 1041–1062. https://doi.org/10.1007/s11524-006-9094-x.
     Google Scholar
  16. Mishra, S. V. (2018). Urban deprivation in a global south city-a neighborhood scale study of Kolkata, India. Habitat International, 80, 1–10. https://doi.org/10.1016/j.habitatint.2018.08.006.
     Google Scholar
  17. Morgan, O. (2006). Measuring deprivation in England and Wales using 2001 Carstairs scores. Health Statistics Quarterly, 6.
     Google Scholar
  18. Pampalon, R., Hamel, D., Gamache, P., & Raymond, G. (2009). A deprivation index for health planning in Canada. Chronic Diseases in Canada, 29(4), 178–191. https://doi.org/10.24095/hpcdp.29.4.05.
     Google Scholar
  19. Panigrahi, A., & Sharma, D. (2019). Exclusive breast feeding practice and its determinants among mothers of children aged 6–12 months living in slum areas of Bhubaneswar, eastern India. Clinical Epidemiology and Global Health, 7(3), 424–428. https://doi.org/10.1016/j.cegh.2018.11.004.
     Google Scholar
  20. Pawasarat, J., & Stetzer, F. (n.d.). Removing Transportation Barriers to Employment: Assessing Driver’s License and Vehicle Ownership Patterns of Low- Income Populations. 54.
     Google Scholar
  21. Rajan, K., Kennedy, J., & King, L. (2013). Is wealthier always healthier in poor countries? The health implications of income, inequality, poverty, and literacy in India. Social Science & Medicine, 88, 98–107. https://doi.org/10.1016/j.socscimed.2013.04.004.
     Google Scholar
  22. Sathyakumar, V., Ramsankaran, R., & Bardhan, R. (2019). Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES)—A multi-scale probabilistic analysis based in Mumbai, India. GIScience & Remote Sensing, 56(5), 645–669. https://doi.org/10.1080/15481603.2018.1549819.
     Google Scholar
  23. Shaban, A., & Aboli, Z. (2021). Socio-spatial Segregation and Exclusion in Mumbai. In M. van Ham, T. Tammaru, R. Ubarevičienė, & H. Janssen (Eds.), Urban Socio-Economic Segregation and Income Inequality (pp. 153–170). Springer International Publishing. https://doi.org/10.1007/978-3-030-64569-4_8.
     Google Scholar
  24. Tan, P. Y., & Samsudin, R. (2017). Effects of spatial scale on assessment of spatial equity of urban park provision. Landscape and Urban Planning, 158, 139–154. https://doi.org/10.1016/j.landurbplan.2016.11.001.
     Google Scholar
  25. Taubenböck, H., & Kraff, N. J. (2014). The physical face of slums: A structural comparison of slums in Mumbai, India, based on remotely sensed data. Journal of Housing and the Built Environment, 29(1), 15–38. https://doi.org/10.1007/s10901-013-9333-x.
     Google Scholar
  26. Taubenböck, H., Kraff, N. J., & Wurm, M. (2018). The morphology of the Arrival City—A global categorization based on literature surveys and remotely sensed data. Applied Geography, 92, 150–167. https://doi.org/10.1016/j.apgeog.2018.02.002.
     Google Scholar
  27. Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21(6), 459–468. https://doi.org/10.1093/heapol/czl029.
     Google Scholar
  28. Xiao, Y., Wang, Z., Li, Z., & Tang, Z. (2017). An assessment of urban park access in Shanghai – Implications for the social equity in urban China. Landscape and Urban Planning, 157, 383–393. https://doi.org/10.1016/j.landurbplan.2016.08.007.
     Google Scholar