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

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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.

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References

  • 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.

  • Anselin, L. (1988). Spatial econometrics: Methods and models (Vol. 4). Springer.

  • 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.

  • Barff, R. A., & Knight, P. L., III. (1988). Dynamic shift-share analysis. Growth and Change,19(2), 1–10.

    Article  Google Scholar 

  • 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.

  • BBS. (2008). Bangladesh Bureau of Statistics. Urban Area Report, Bangladesh Bureau of Statistics, Statistics and Informatics Division, Ministry of Planning. www.bbs.gov.bd.

  • BBS. (2013a). Bangladesh Bureau of Statistics, Economic Census, District Report Dhaka, Ministry of Planning, Government of the People’s Republic of Bangladesh.

  • BBS. (2013b). Bangladesh Bureau of Statistics, Economic Census, Ministry of Planning, Government of the People’s Republic of Bangladesh.

  • 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.

  • 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.

    Article  Google Scholar 

  • Boasson, E., & Boasson, V. (2011). The spatial-temporal cluster development in the healthcare service industry–an integrated GIS approach.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Choe, K., & Roberts, B. (2011). Competitive cities in the 21st century: Cluster-based local economic development. Mandulayong City: Asian Development Bank.

    Google Scholar 

  • 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.

  • Cortright, J. (2006). Making sense of clusters: Regional competitiveness and economic development. Brookings Institution, Metropolitan Policy Program.

  • CPD. (2018). State of the Bangladesh economy in FY 2017–2018. Retrieved August 21, 2018, from www.cpd.org.bd.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Feser, E. J., & Bergman, E. M. (2000). National industry cluster templates: A framework for applied regional cluster analysis. Regional Studies,34(1), 1–19.

    Article  Google Scholar 

  • Feser, E. J., & Koo, K. (2001). Industry clusters in Kentucky. Lexington: Kentucky Science and Technology Corporation.

    Google Scholar 

  • Frizado, J., Smith, B., & Carroll, M. (2007). Identification of economic clusters using ArcGIS spatial statistics. In ESRI international user conference.

  • 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.

    Article  Google Scholar 

  • Fujishima, S. (2013). Growth, agglomeration, and urban congestion. Journal of Economic Dynamics and Control,37(6), 1168–1181.

    Article  Google Scholar 

  • Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis,24(3), 189–206.

    Article  Google Scholar 

  • 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).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

  • Isard, W., & Langford, T. W. (1971). Regional input-output study: Recollections, reflections, and diverse notes on the Philadelphia experience. Cambridge: MIT Press.

    Google Scholar 

  • 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.

  • Kamal, M. R. (1984). Problems of small-scale and cottage industries in Bangladesh. Doctoral dissertation. Nagoya: University of Nagoya Press.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Kim, D., & Lee, H. (2016). Crime trend analysis by changes of spatial autocorrelation and hot-spot. http://ieomsociety.org/ieom_2016/pdfs/538.pdf.

  • Kreft, H., & Jetz, W. (2007). Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences,104(14), 5925–5930.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Marshall, A. (1961). Principles of Economics: An Introductory Volume. London: Macmillan.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Momen, A. (1994). Industrial pattern of Chittagong city. Master’s thesis. Dhaka: University of Dhaka.

  • Mori, T., & Smith, T. E. (2015). On the spatial scale of industrial agglomerations. Journal of Urban Economics,89(2015), 1–20.

    Article  Google Scholar 

  • Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis,27(4), 286–306.

    Article  Google Scholar 

  • Paradis, E. (2018). Moran’s autocorrelation coefficient in comparative methods. ReCALL,2(2010), 1–9.

    Google Scholar 

  • Perry, J. N. (1995). Spatial analysis by distance indices. Journal of Animal Ecology,64, 303–314.

    Article  Google Scholar 

  • Porter, M. E. (1990). The competitive advantage of nations. Competitive Intelligence Review, 1(1), 14–14.

    Article  Google Scholar 

  • Porter, M. E. (1998). Clusters and the new economics of competition, 76(6), 77–90. Boston: Harvard Business Review.

    Google Scholar 

  • Porter, M. E. (2000). Location, competition, and economic development: Local clusters in a global economy. Economic Development Quarterly,14(1), 15–34.

    Article  Google Scholar 

  • 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 

  • Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B (Methodological),39, 172–212.

    Google Scholar 

  • 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.

  • Roth, A. V., & Miller, J. G. (1992). Success factors in manufacturing. Business Horizons,35(4), 73–81.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sultana, S. (2002). Location characteristic of industries at Savar municipalities. Master’s thesis. Dhaka: University of Dhaka.

  • Turok, I., & McGranahan, G. (2013). Urbanization and economic growth: The arguments and evidence for Africa and Asia. Environment and Urbanization,25(2), 465–482.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Watts, D. (1987). Industrial geography. New York: Wiley.

    Google Scholar 

  • 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.

    Article  Google Scholar 

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  1. University of Florida, Gainesville, FL, USA

    Mohammad Mehedy Hassan, Meshari S. Alenezi & Ryan Z. Good

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  1. Mohammad Mehedy Hassan
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Hassan, M.M., Alenezi, M.S. & Good, R.Z. Spatial pattern analysis of manufacturing industries in Keraniganj, Dhaka, Bangladesh. GeoJournal 85, 269–283 (2020). https://doi.org/10.1007/s10708-018-9961-5

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