Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

  • Sujit Kumar SikderEmail author
  • Martin Behnisch
  • Hendrik Herold
  • Theo Koetter
Part of the following topical collections:
  1. Innovative approaches, tools and visualization techniques for analyzing land use structures and dynamics of cities and regions


Information on spatial building structures is limited, but it can support efficient planning and management in the context of fast-growing big cities in many developing countries. In this paper, we present a spatial analysis approach that includes an estimate of building intensity in the megacity of Dhaka and a spatial analysis using spatial statistics. The entire city was divided into regular grids and the building intensity (both horizontal and vertical) was extracted using vector type building information; the spatial statistics were calculated on the basis of Moran’s I and Gini indices. The variability of the estimated spatial statistics is interpreted according to co-relationship or clustering patterns with the location of the central business district (CBD) area as well as the public bus transit infrastructure (routes and stops). The results show that the residential building structure intensity is prominent and the concentrations are distributed all over the city. The mixed-use type building structures show highest clustering, with fewer outliers in the old part of the city. The vertical-use intensities indicate extreme clustering within highly intensified building activity in the nearby CBD area. The higher presence of low-low clustering of horizontal intensity indicated low development at the suburban area. However, the strongly clustered grid cells within residential sector as well as horizontal development classes are less accessible by bus transit within a defined catchment area, whereas the service sector and vertical development type seem to be more accessible. This type of geographic approach, visualization, and statistical information can help in making data-driven planning decisions with the advantage of monitoring urban development; however, the modeling sensitivity and uncertainties in the building data set remain open for further investigation.


Building structure Spatial analysis Spatial statistics Geographical information system Megacity 



  1. Ahmed B, Ahmed R (2012) Modeling urban land cover growth dynamics using multitemporal Satellite images: A case study of Dhaka, Bangladesh. ISPRS Int J Geo-Inform 1:3–31. CrossRefGoogle Scholar
  2. Ahmed SJ, Bramley G, Dewan AM (2012) Exploratory growth analysis of a megacity through different spatial metrics: a case study on Dhaka, Bangladesh (1960-2005). URISA Journal 24(1):9–25Google Scholar
  3. Allen MR, Fernandez SJ, Fu JS, Olama MM (2016) Impacts of climate change on sub-regional electricity demand and distribution in the southern United States. Nat Energy 1:16103. CrossRefGoogle Scholar
  4. Amado M, Poggi F, Ribeiro Amado A, Breu S (2017) A cellular approach to net-zero Energy Cities. Energies 10(11):1826CrossRefGoogle Scholar
  5. Anas A, Arnott R, Small KA (1998) Urban spatial structure. J Econ Lit 36(3):1426–1464Google Scholar
  6. Anselin L (2015) Spatial data science for an enhanced understanding of urban dynamics. The cities papers. Get access: Accessed 15 Oct 2018
  7. Arbia G (2001) The role of spatial effects in the empirical analysis of regional concentration. J Geogr Syst 3(3):271–281CrossRefGoogle Scholar
  8. Barros J (2004) Urban Dynamics in Latin American cities: An Agent Based Simulation Approach. Environ Plann B Plann DesGoogle Scholar
  9. Batty M, Longley PA (1986) The fractal simulation of urban structure. Environ Plan A 18(9):1143–1179CrossRefGoogle Scholar
  10. Besussi E, Chin N, Batty M, Longley P (2010) The structure and form of urban settlements. In: Remote sensing of urban and suburban areas. Springer, Dordrecht, 13–31CrossRefGoogle Scholar
  11. Biljecki F, Ledoux H, Stoter J (2017) Generating 3D city models without elevation data. Comput Environ Urban Syst 64:1–18CrossRefGoogle Scholar
  12. Cai J, Huang B, Song Y (2017) Using multi-source geospatial big data to identify the structure of polycentric cities. Remote Sens Environ 202:210–221CrossRefGoogle Scholar
  13. Chowdhury FJ, Amin AN (2006) Environmental assessment in slum improvement programs: some evidence from a study on infrastructure projects in two Dhaka slums. Environ Impact Assess Rev 26(6):530–552CrossRefGoogle Scholar
  14. Crooks AT (2010) Constructing and implementing an agent-based model of residential segregation through vector GIS. Int J Geogr Inf Sci 24:661–675. CrossRefGoogle Scholar
  15. Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401CrossRefGoogle Scholar
  16. EC (2011) Housing Space Per Person. Retrieved from Accessed 15 June 2018
  17. Fu WJ, Jiang PK, Zhou GM, Zhao KL (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):2401–2409CrossRefGoogle Scholar
  18. Gonzalez MC (2015) From Data to Models that Improve Urban Policy. In: The Social Science Research CouncilGoogle Scholar
  19. Griffith D, Chun Y (2014) Spatial autocorrelation and spatial filtering. In: Handbook of regional science. Springer, p 1477–1507Google Scholar
  20. Groves-Kirkby CJ, Denman AR, Phillips PS (2009) Lorenz curve and Gini coefficient: novel tools for analysing seasonal variation of environmental radon gas. J Environ Manag 90(8):2480–2487CrossRefGoogle Scholar
  21. Hecht R, Kunze C, Hahmann S (2013) Measuring Completeness of Building Footprints in OpenStreetMap over Space and Time. ISPRS Int J Geo-Inform 2(4):1066–1091. CrossRefGoogle Scholar
  22. Hedin K, Clark E, Lundholm E, Malmberg G (2012) Neoliberalization of Housing in Sweden: Gentrification, Filtering, and Social Polarization. Ann Assoc Am Geogr 102:443–463. CrossRefGoogle Scholar
  23. Howard B, Parshall L, Thompson J, Hammer S, Dickinson J, Modi V (2012) Spatial distribution of urban building energy consumption by end use. Energ Buildings 45:141–151. CrossRefGoogle Scholar
  24. Huang J, Lu XX, Sellers JM (2007) A global comparative analysis of urban form: applying spatial metrics and remote sensing. Landsc Urban Plan 82:184–197. CrossRefGoogle Scholar
  25. Inkoom JN, Nyarko BK, Antwi KB (2017) Explicit Modeling of Spatial Growth Patterns in Shama, Ghana: an Agent-Based Approach. J Geovisualization Spat Anal 1(1-2):7. CrossRefGoogle Scholar
  26. Inostroza L, Baur R, Csaplovics E (2013) Urban sprawl and fragmentation in Latin America: A dynamic quantification and characterization of spatial patterns. J Environ Manag 115:87–97CrossRefGoogle Scholar
  27. Islam M, Ahmed R (2011) Land use change prediction in Dhaka city using GIS aided Markov chain modeling. J Life Earth Sci 6:81–89CrossRefGoogle Scholar
  28. Ishtiaque A, Ullah MS (2013) The influence of factors of migration on the migration status of rural-urban migrants in Dhaka, Bangladesh. Hum Geogr 7(2):45Google Scholar
  29. Jayasinghe A, Sano K, Rattanaporn K (2017) Application for developing countries: Estimating trip attraction in urban zones based on centrality. J Traffic Transp Eng (English Edition) 4(5):464–476CrossRefGoogle Scholar
  30. Jiang B, Ma D, Yin J, Sandberg M (2016) Spatial distribution of city Tweets and their densities. Geogr Anal 48:337–351. CrossRefGoogle Scholar
  31. Kabir A, Parolin B (2012) Planning and development of Dhaka - A story of 400 years. Paper presented at the 15th International Planning History Society Conference, Sao Paulo, BrazilGoogle Scholar
  32. Kennedy C (2015) Energy and material flows of megacities [Online]. University of Ontario, Canada Available: Accessed 2018
  33. Kennedy CA, Stewart I, Facchini A, Cersosimo I, Mele R, Chen B, Uda M, Kansal A, Chiu A, Kim KG (2015) Energy and material flows of megacities. Natl Acad Sci 112(19):5985–5990CrossRefGoogle Scholar
  34. Kushol SAS, Ahmed KS, Hossain MM, Rahman I (2013) Effect of street morphology on microclimate in residential areas following FAR rule in Dhaka City. PLEA 1–6Google Scholar
  35. Larondelle N, Hamstead ZA, Kremer P, Haase D, McPhearson T (2014) Applying a novel urban structure classification to compare the relationships of urban structure and surface temperature in Berlin and New York City. Appl Geogr 53:427–437CrossRefGoogle Scholar
  36. Maruyama Y (2015) An alternative to Moran’s I for spatial autocorrelation arXiv preprint arXiv: 1501.06260Google Scholar
  37. Mundia CN, Murayama Y (2010) Modeling spatial processes of urban growth in African cities: A case study of Nairobi City. Urban Geogr 31(2):259–272CrossRefGoogle Scholar
  38. Newman P, Kosonen L, Kenworthy J (2016) Theory of urban fabrics: Planning the walking, transit/public transport and automobile/motor Car cities for reduced Car dependency. Town Plan Rev 87:429–458CrossRefGoogle Scholar
  39. Prasannakumar V, Vijith H, Charutha R, Geetha N (2011) Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia Soc Behav Sci 21:317–325CrossRefGoogle Scholar
  40. Rahman MS-U, Timms P, Montgomery F (2012) Integrating BRT systems with Rickshaws in developing cities to promote energy efficient travel. Procedia Soc Behav Sci 54:261–274. CrossRefGoogle Scholar
  41. Raja DR (2012) Spatial analysis of land surface temperature in Dhaka metropolitan area. Journal of Bangladesh Institute of Planners 5:151–167Google Scholar
  42. RAJUK (2005) Dhaka metropolitan development plan - detail area plan. Final report. Rajdhani Unnayan Katripakha (Capital Development Authority), Dhaka. BangladeshGoogle Scholar
  43. RAJUK (1995) Dhaka Metropolitan Development Plan (1995-2015). Rajdhani Unnayan Kartripakka (Capital Development Authority), DhakaGoogle Scholar
  44. Rey SJ, Smith RJ (2013) A spatial decomposition of the Gini coefficient. Lett Spat Resour Sci 6(2):55–70CrossRefGoogle Scholar
  45. Roy M (2009) Planning for sustainable urbanisation in fast growing cities: mitigation and adaptation issues addressed in Dhaka, Bangladesh. Habitat International 33(3):276–286CrossRefGoogle Scholar
  46. Ruth M, Coelho D (2007) Understanding and managing the complexity of urban systems under climate change. Clim Pol 7(4):317–336CrossRefGoogle Scholar
  47. Sikder SK, Eanes F, Asmelash HB, Kar S, Koetter T (2016) The contribution of energy-optimized urban planning to efficient resource use -a case study on residential settlement development in Dhaka city, Bangladesh. Sustainability (Switzerland) 8. CrossRefGoogle Scholar
  48. Sikder S, Nagarajan M, Kar S, Koetter T (2018) A geospatial approach of downscaling urban energy consumption density in mega-city Dhaka, Bangladesh. Urban Climate 26:10–30. CrossRefGoogle Scholar
  49. Singh BB, Roy P, Spiess T, Venkatesh B (2015) Sustainable integrated urban & energy planning, the evolving electrical grid and urban energy transition. TorontoGoogle Scholar
  50. Stanley N, Alao S, Jacob B (2016) Quantitating non-zero autocorrelation to determine Moran's I coefficients for mapping clustering tendencies of fast food restaurants in lower SES neighborhoods in Hillsborough County, Florida. J Remote Sensing & GIS 5(179):2Google Scholar
  51. Stewart ID, Oke TR (2012) Local climate zones for urban temperature studies. Bull Am Meteorol Soc 93:1879–1900. CrossRefGoogle Scholar
  52. Taubenböck H, Wegmann M, Roth A, Mehl H, Dech S (2009) Urbanization in India–Spatiotemporal analysis using remote sensing data. Comput Environ Urban Syst 33(3):179–188CrossRefGoogle Scholar
  53. Tobler W (2004) On the first law of geography: A reply. Ann Assoc Am Geogr 94(2):304–310CrossRefGoogle Scholar
  54. Torrens PM (2010) Agent-based Models and the Spatial Sciences. Geogr Compass 4(5):428–448CrossRefGoogle Scholar
  55. UN (2008) World urbanization prospects: The 2007 revision. New York. USAGoogle Scholar
  56. Wahyudi A, Liu Y (2015) Spatial dynamic models for inclusive cities: A brief concept of cellular automata (CA) and agent-based model (ABM). J Reg City Plan 26(1):54–70Google Scholar
  57. Wong MS, Peng F, Zou B, Shi WZ, Wilson GJ (2016) Spatially analyzing the inequity of the Hong Kong urban heat island by socio-demographic characteristics. Int J Environ Res Public Health 13(3):317CrossRefGoogle Scholar
  58. Wurm M, Taubenböck H, Dech S (2010) Quantification of urban structure on building block level utilizing multisensoral remote sensing data. Paper presented at the SPIE Europe Remote Sensing 2010Google Scholar
  59. Xie X, Hou W, Herold H (2018) Ex post impact assessment of master plans—the case of Shenzhen in shaping a polycentric urban structure. ISPRS International Journal of Geo-Information 7(7):252CrossRefGoogle Scholar
  60. Yeh AG-O, Li X (2006) Errors and uncertainties in urban cellular automata. Comput Environ Urban Syst 30:10–28. CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Leibniz Institute of Ecological Urban and Regional DevelopmentDresdenGermany
  2. 2.Institute of Geodesy and GeoinformationUniversity of BonnBonnGermany

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