A geographic information system-based logical urban growth model for predicting spatial growth of an urban area

Ripan Debnath and Nurul Amin Atm

Environment and Planning B, 2016, vol. 43, issue 3, 580-597

Abstract: The paper describes the development of a forecasting model to render most logical or probable urban expansion over the spatial region of an urban area. Prediction of urban growth is very important for development planning and policy making as well as for urban management. In this model, five different but equal influential factors of urban expansion choices have been integrated. Those factors are city’s future development master plan, spatial and temporal population growth pattern, land elevation, road connectivity and distance from existing urban center(s). The integration processes were performed on equal area hexagonal grid through geographic information system application. The location-specific information were extracted and overlaid on the hexagonal grid and classified into a scale of scores in order to compare the cells with one another. The model presumes that the higher the cell value is the higher the probability to be developed as urban. As a case study, the model delineated spatial growth of Dhaka city by the years of 2025 and 2040. There it has been observed that Badda, Khilkhet, Khilgaon, Jatrabari, Demra, Ashulia, Savar, etc. areas are supposed to be urbanized in the near future. Subsequently, a huge portion of flood flow zone are most likely to be developed as urban though those are non-urban areas as declared by the authority. On the other hand, the regions across the lying rivers in Dhaka Metropolitan area may face sluggish urban development despite closer proximity to core urban. Since the model portrays future expansion over the geographical regions, the urban planners and decision makers may utilize it in physical planning to guide future urbanization.

Keywords: Urbanization; urban growth model; spatial analysis; geographic information system; Dhaka city (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:43:y:2016:i:3:p:580-597

DOI: 10.1177/0265813515618565

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