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Urban Big Data Analytics: A Novel Approach for Tracking Urbanization Trends in Sri Lanka

Author

Listed:
  • Nimesh Akalanka

    (Department of Town and Country Planning, University of Moratuwa, Katubedda, Moratuwa 10400, Sri Lanka)

  • Nayomi Kankanamge

    (Department of Town and Country Planning, University of Moratuwa, Katubedda, Moratuwa 10400, Sri Lanka)

  • Jagath Munasinghe

    (Department of Town and Country Planning, University of Moratuwa, Katubedda, Moratuwa 10400, Sri Lanka)

  • Tan Yigitcanlar

    (City 4.0 Lab, School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

Abstract

The dynamic nature of urbanization calls for more frequently updated and more reliable datasets than conventional methods, in order to comprehend it for planning purposes. The current widely used methods to study urbanization heavily depend on shifts in residential populations and building densities, the data of which are static and do not necessarily capture the dynamic nature of urbanization. This is a particularly the case with low- and middle-income nations, where, according to the United Nations, urbanization is mostly being experienced in this century. This study aims to develop a more effective approach to comprehending urbanization patterns through big data fusion, using multiple data sources that provide more reliable information on urban activities. The study uses five open data sources: national polar-orbiting partnership/visible infrared imaging radiometer suite night-time light images; point of interest data; mobile network coverage data; road network coverage data; normalized difference vegetation index data; and the Python programming language. The findings challenge the currently dominant census data and statistics-based understanding of Sri Lanka’s urbanization patterns that are either underestimated or overestimated. The proposed approach offers a more reliable and accurate alternative for authorities and planners in determining urbanization patterns and urban footprints.

Suggested Citation

  • Nimesh Akalanka & Nayomi Kankanamge & Jagath Munasinghe & Tan Yigitcanlar, 2024. "Urban Big Data Analytics: A Novel Approach for Tracking Urbanization Trends in Sri Lanka," Land, MDPI, vol. 13(6), pages 1-45, June.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:888-:d:1417972
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    References listed on IDEAS

    as
    1. Tan Yigitcanlar & Mirko Guaralda & Manuela Taboada & Surabhi Pancholi, 2016. "Place Making for Knowledge Generation and Innovation: Planning and Branding Brisbane's Knowledge Community Precincts," Journal of Urban Technology, Taylor & Francis Journals, vol. 23(1), pages 115-146, January.
    2. Guoliang Zhao & Xinqi Zheng & Zhiyuan Yuan & Lulu Zhang, 2017. "Spatial and Temporal Characteristics of Road Networks and Urban Expansion," Land, MDPI, vol. 6(2), pages 1-19, April.
    3. Nelunika Priyashani & Nayomi Kankanamge & Tan Yigitcanlar, 2023. "Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints," Land, MDPI, vol. 12(2), pages 1-23, February.
    4. Jun Zhang & Xiaodie Yuan & Xueping Tan & Xue Zhang, 2021. "Delineation of the Urban-Rural Boundary through Data Fusion: Applications to Improve Urban and Rural Environments and Promote Intensive and Healthy Urban Development," IJERPH, MDPI, vol. 18(13), pages 1-19, July.
    5. Sandulika Abesinghe & Nayomi Kankanamge & Tan Yigitcanlar & Surabhi Pancholi, 2023. "Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data," Future Internet, MDPI, vol. 15(1), pages 1-21, January.
    6. Xueming Li & Yishan Song & He Liu & Xinyu Hou, 2023. "Extraction of Urban Built-Up Areas Using Nighttime Light (NTL) and Multi-Source Data: A Case Study in Dalian City, China," Land, MDPI, vol. 12(2), pages 1-18, February.
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