Advertisement

GeoJournal

pp 1–15 | Cite as

Spatial pattern analysis of manufacturing industries in Keraniganj, Dhaka, Bangladesh

  • Mohammad Mehedy HassanEmail author
  • Meshari S. Alenezi
  • Ryan Z. Good
Article
  • 53 Downloads

Abstract

Understanding industrial clustering and its patterns of development are important steps in linking regional policy development, strategic decision making, business site management, and fostering a country’s economic growth. A considerable variety of common location-based cluster measures are available in practice, including area-based measures and a variety of indicators based on analyses of point data. This study uses the geostatistical approaches kernel density, multi-distance Reply’s-K, and spatial autocorrelation, both global Moran’s-I and local Moran’s-I, to assess the degree of spatial clustering of manufacturing locations in Keranignaj, located at the southern periphery of the urban region of Dhaka, Bangladesh. Results indicated a non-random pattern for all manufacturing locations in the study region. Small-scale industries such as garment manufacturing, metal, and brick making have a strong presence in Keranignaj. Expansion of such industries were highly associated with proximity to a river, while food processing, rubber and plastics manufacturing industries were clustered in relation to road proximity. The spatial association Global Moran’s-I with higher positive coefficient value indicates homogeneity, or spatial auto-correlation, exist in the industrial locations studied here. Local Moran’s-I, which documents regional clustering, has yielded a statistically significant manufacturing cluster (0.05 level) for the manufacturing areas of Zinjira, Kaliganj, Mirerbagh, and Chunkutia. Since cluster-based economic development has recently been an area of increasing interest for both developed and developing nations, the outcomes from this study provide an insight into spatial processes of industrial development in Bangladesh, and the Dhaka area in particular, enabling planners and policymakers to make rational, informed decisions and strengthening the economic growth and capacity for development of micro-industries clusters for the area studied here and the region beyond.

Keywords

Cluster analysis Kernel density Reply’s-K Global and local Moran’s-I Keraniganj Dhaka 

Notes

References

  1. Aldstadt, J., Chen, D., & Arthur, G. (2016). Point pattern analysis. Retrieved from http://ceadserv1.nku.edu/longa//cgi-bin/cgi-tcl-examples/generic/ppa/ppa.cgi.
  2. Anselin, L. (1988). Spatial econometrics: Methods and models (Vol. 4). Springer.Google Scholar
  3. Bangladesh Economic Review. (2017). Economic Adviser’s Wing, Finance Division, Ministry of Finance, Government of the People’s Republic of Bangladesh. Retrieved December 2, 2018, from https://mof.gov.bd/site/page/44e399b3-d378-41aa-86ff-8c4277eb0990/BangladeshEconomicReview.
  4. Barff, R. A., & Knight, P. L., III. (1988). Dynamic shift-share analysis. Growth and Change, 19(2), 1–10.CrossRefGoogle Scholar
  5. BBS. (2005). Bangladesh Bureau of Statistics. Report on Survey of Manufacturing Industries 2005–2006, Ministry of Planning, Government of the People’s Republic of Bangladesh.Google Scholar
  6. BBS. (2008). Bangladesh Bureau of Statistics. Urban Area Report, Bangladesh Bureau of Statistics, Statistics and Informatics Division, Ministry of Planning. www.bbs.gov.bd.
  7. BBS. (2013a). Bangladesh Bureau of Statistics, Economic Census, District Report Dhaka, Ministry of Planning, Government of the People’s Republic of Bangladesh.Google Scholar
  8. BBS. (2013b). Bangladesh Bureau of Statistics, Economic Census, Ministry of Planning, Government of the People’s Republic of Bangladesh.Google Scholar
  9. BBS. (2014). Bangladesh Bureau of Statistics, Bangladesh Population and Housing Census 2011, Urban Area Report, Statistics and Informatics Division, Ministry of Planning, Government of Bangladesh. www.bbs.gov.bd.
  10. Bivand, R., Müller, W. G., & Reder, M. (2009). Power calculations for global and local Moran’s I. Computational Statistics & Data Analysis, 53(8), 2859–2872.CrossRefGoogle Scholar
  11. Boasson, E., & Boasson, V. (2011). The spatial-temporal cluster development in the healthcare service industry–an integrated GIS approach.Google Scholar
  12. Brachert, M., Titze, M., & Kubis, A. (2011). Identifying industrial clusters from a multidimensional perspective: Methodical aspects with an application to Germany. Papers in Regional Science, 90(2), 419–439.CrossRefGoogle Scholar
  13. Chen, M., Zhang, H., Liu, W., & Zhang, W. (2014). The global pattern of urbanization and economic growth: Evidence from the last three decades. PLoS ONE, 9(8), e103799.CrossRefGoogle Scholar
  14. Choe, K., & Roberts, B. (2011). Competitive cities in the 21st century: Cluster-based local economic development. Mandulayong City: Asian Development Bank.Google Scholar
  15. Cifranič, M. (2016). Localization factors in decision making of location of selected enterprises. The agri-food value chain: Challenges for natural resources management and society, 1st edn. online (1108 s.), pp. 978–980.Google Scholar
  16. Cortright, J. (2006). Making sense of clusters: Regional competitiveness and economic development. Brookings Institution, Metropolitan Policy Program.Google Scholar
  17. CPD. (2018). State of the Bangladesh economy in FY 2017–2018. Retrieved August 21, 2018, from www.cpd.org.bd.
  18. Czamanski, S., & Ablas, L. A. D. Q. (1979). Identification of industrial clusters and complexes: a comparison of methods and findings. Urban Studies, 16(1), 61–80.CrossRefGoogle Scholar
  19. Dogru, A. O., David, R. M., Ulugtekin, N., Goksel, C., Seker, D. Z., & Sözen, S. (2017). GIS based spatial pattern analysis: Children with hepatitis A in Turkey. Environmental Research, 156(2017), 349–357.CrossRefGoogle Scholar
  20. Drucker, J., & Feser, E. (2012). Regional industrial structure and agglomeration economies: An analysis of productivity in three manufacturing industries. Regional Science and Urban Economics, 42(1–2), 1–14.CrossRefGoogle Scholar
  21. Feser, E. J., & Bergman, E. M. (2000). National industry cluster templates: A framework for applied regional cluster analysis. Regional Studies, 34(1), 1–19.CrossRefGoogle Scholar
  22. Feser, E. J., & Koo, K. (2001). Industry clusters in Kentucky. Lexington: Kentucky Science and Technology Corporation.Google Scholar
  23. Frizado, J., Smith, B., & Carroll, M. (2007). Identification of economic clusters using ArcGIS spatial statistics. In ESRI international user conference.Google Scholar
  24. Fu, W. J., Jiang, P. K., Zhou, G. M., & Zhao, K. L. (2014). Using Moran’s I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences, 11(8), 2401–2409.CrossRefGoogle Scholar
  25. Fujishima, S. (2013). Growth, agglomeration, and urban congestion. Journal of Economic Dynamics and Control, 37(6), 1168–1181.CrossRefGoogle Scholar
  26. Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.CrossRefGoogle Scholar
  27. Grubesic, T. H., & Murray, A. T. (2001). Detecting hot spots using cluster analysis and GIS. In Proceedings from the fifth annual international crime mapping research conference (Vol. 26).Google Scholar
  28. Haase, P. (1995). Spatial pattern analysis in ecology based on Ripley’s K-function: Introduction and methods of edge correction. Journal of Vegetation Science, 6(4), 575–582.CrossRefGoogle Scholar
  29. Haining, R., Law, J., & Griffith, D. (2009). Modelling small area counts in the presence of overdispersion and spatial autocorrelation. Computational Statistics & Data Analysis, 53(8), 2923–2937.CrossRefGoogle Scholar
  30. Hassan, M. M. (2017). Monitoring land use/land cover change, urban growth dynamics and landscape pattern analysis in five fastest urbanized cities in Bangladesh. Remote Sensing Applications: Society and Environment, 7, 69–83.CrossRefGoogle Scholar
  31. Hassan, M. M., & Southworth, J. (2017). Analyzing land cover change and urban growth trajectories of the mega-urban region of Dhaka using remotely sensed data and an ensemble classifier. Sustainability, 10(1), 10.CrossRefGoogle Scholar
  32. Hossain, M. A., & Nazem, N. I. (2016). Cluster based urban economic growth in the Chittagong metro region: A case of manufacturing industries. Oriental Geographer, 57(1&2).Google Scholar
  33. Isard, W., & Langford, T. W. (1971). Regional input-output study: Recollections, reflections, and diverse notes on the Philadelphia experience. Cambridge: MIT Press.Google Scholar
  34. Islam, Z. (1994). The ready-made garments industry: A location analysis and impact of it on the local residential environment: Described location pattern and the location factors. Master’s thesis. Dhaka: University of Dhaka.Google Scholar
  35. Kamal, M. R. (1984). Problems of small-scale and cottage industries in Bangladesh. Doctoral dissertation. Nagoya: University of Nagoya Press.Google Scholar
  36. Khaemba, W. M. (2001). Spatial point pattern analysis of aerial survey data to assess clustering in wildlife distributions. International Journal of Applied Earth Observation and Geoinformation, 3(2), 139–145.CrossRefGoogle Scholar
  37. Khan, A. U. (1996). Entrepreneurial process in Bangladesh: A study of industrial location decision-making in Dhaka City (No. 2). Urban Studies Program, Dept. of Geography, University of Dhaka.Google Scholar
  38. Kies, U., Mrosek, T., & Schulte, A. (2009). Spatial analysis of regional industrial clusters in the German forest sector. International Forestry Review, 11(1), 38–51.CrossRefGoogle Scholar
  39. Kim, D., & Lee, H. (2016). Crime trend analysis by changes of spatial autocorrelation and hot-spot. http://ieomsociety.org/ieom_2016/pdfs/538.pdf.
  40. Kreft, H., & Jetz, W. (2007). Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences, 104(14), 5925–5930.CrossRefGoogle Scholar
  41. Lamichhane, A. P., Warren, J., Puett, R., Porter, D. E., Bottai, M., Mayer-Davis, E. J., & Liese, A. D. (2013). Spatial patterning of supermarkets and fast food outlets with respect to neighborhood characteristics. Health & Place, 23, 157–164.CrossRefGoogle Scholar
  42. Lee, S. I. (2001). Developing a bivariate spatial association measure: An integration of Pearson’s r and Moran’s I. Journal of Geographical Systems, 3(4), 369–385.CrossRefGoogle Scholar
  43. Li, D., Lu, Y., & Wu, M. (2012). Industrial agglomeration and firm size: Evidence from China. Regional Science and Urban Economics, 42(1–2), 135–143.CrossRefGoogle Scholar
  44. Lin, G., & Zhang, T. (2007). Loglinear residual tests of Moran’s I autocorrelation and their applications to Kentucky breast cancer data. Geographical Analysis, 39(3), 293–310.CrossRefGoogle Scholar
  45. Marshall, A. (1961). Principles of Economics: An Introductory Volume. London: Macmillan.Google Scholar
  46. Messner, S. F., Anselin, L., Baller, R. D., Hawkins, D. F., Deane, G., & Tolnay, S. E. (1999). The spatial patterning of county homicide rates: An application of exploratory spatial data analysis. Journal of Quantitative Criminology, 15(4), 423–450.CrossRefGoogle Scholar
  47. Mohaymany, A. S., Shahri, M., & Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-spatial Information Science, 16(2), 113–119.CrossRefGoogle Scholar
  48. Momen, A. (1994). Industrial pattern of Chittagong city. Master’s thesis. Dhaka: University of Dhaka.Google Scholar
  49. Mori, T., & Smith, T. E. (2015). On the spatial scale of industrial agglomerations. Journal of Urban Economics, 89(2015), 1–20.CrossRefGoogle Scholar
  50. Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.CrossRefGoogle Scholar
  51. Paradis, E. (2018). Moran’s autocorrelation coefficient in comparative methods. ReCALL, 2(2010), 1–9.Google Scholar
  52. Perry, J. N. (1995). Spatial analysis by distance indices. Journal of Animal Ecology, 64, 303–314.CrossRefGoogle Scholar
  53. Porter, M. E. (1990). The competitive advantage of nations. Competitive Intelligence Review, 1(1), 14–14.CrossRefGoogle Scholar
  54. Porter, M. E. (1998). Clusters and the new economics of competition, 76(6), 77–90. Boston: Harvard Business Review.Google Scholar
  55. Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly, 14(1), 15–34.CrossRefGoogle Scholar
  56. Rajkumar, P. (2013). A study of the factors influencing the location selection decisions of information technology firms. Asian Academy of Management Journal, 18(1), 35.Google Scholar
  57. Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B (Methodological), 39, 172–212.Google Scholar
  58. Rosenthal, S. S., & Strange, W. C. (2004). Evidence on the nature and sources of agglomeration economies. In Handbook of regional and urban economics (Vol. 4, pp. 2119–2171). Elsevier.Google Scholar
  59. Roth, A. V., & Miller, J. G. (1992). Success factors in manufacturing. Business Horizons, 35(4), 73–81.CrossRefGoogle Scholar
  60. Shafabakhsh, G. A., Famili, A., & Bahadori, M. S. (2017). GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran. Journal of Traffic and Transportation Engineering (English Edition), 4(3), 290–299.CrossRefGoogle Scholar
  61. Sultana, S. (2002). Location characteristic of industries at Savar municipalities. Master’s thesis. Dhaka: University of Dhaka.Google Scholar
  62. Turok, I., & McGranahan, G. (2013). Urbanization and economic growth: The arguments and evidence for Africa and Asia. Environment and Urbanization, 25(2), 465–482.CrossRefGoogle Scholar
  63. Viswanadham, N., Sharma, S. M., & Taneja, M. (1996). Inspection allocation in manufacturing systems using stochastic search techniques. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 26(2), 222–230.CrossRefGoogle Scholar
  64. Wang, H., Li, C., & Zheng, Y. (2015). Space expression of industry status using GIS and SWOT analysis. Wuhan University Journal of Natural Sciences, 20(5), 445–454.CrossRefGoogle Scholar
  65. Waits, M. J. (2000). The added value of the industry cluster approach to economic analysis, strategy development, and service delivery. Economic Development Quarterly, 14(1), 35–50.CrossRefGoogle Scholar
  66. Watts, D. (1987). Industrial geography. New York: Wiley.Google Scholar
  67. Zhang, Z., Xiao, R., Shortridge, A., & Wu, J. (2014). Spatial point pattern analysis of human settlements and geographical associations in eastern coastal China—a case study. International Journal of Environmental Research and Public Health, 11(3), 2818–2833.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.University of FloridaGainesvilleUSA

Personalised recommendations