Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints

Urban agglomeration is a continuous urban spread and generally comprises a main city at the core and its adjoining growth areas. These agglomerations are studied using different concepts, theories, models, criteria, indices, and approaches, where population distribution and its associated characteri...

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Main Authors: Nelunika Priyashani, Nayomi Kankanamge, Tan Yigitcanlar
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Land
Subjects:
Online Access:https://www.mdpi.com/2073-445X/12/2/407
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author Nelunika Priyashani
Nayomi Kankanamge
Tan Yigitcanlar
author_facet Nelunika Priyashani
Nayomi Kankanamge
Tan Yigitcanlar
author_sort Nelunika Priyashani
collection DOAJ
description Urban agglomeration is a continuous urban spread and generally comprises a main city at the core and its adjoining growth areas. These agglomerations are studied using different concepts, theories, models, criteria, indices, and approaches, where population distribution and its associated characteristics are mainly used as the main parameters. Given the difficulties in accurately demarcating these agglomerations, novel methods and approaches have emerged in recent years. The use of geospatial big data sources to demarcate urban agglomeration is one of them. This promising method, however, has not yet been studied widely and hence remains an understudied area of research. This study explores using a multisource open geospatial big data fusion approach to demarcate urban agglomeration footprint. The paper uses the Southern Coastal Belt of Sri Lanka as the testbed to demonstrate the capabilities of this novel approach. The methodological approach considers both the urban form and functions related to the parameters of cities in defining urban agglomeration footprint. It employs near-real-time data in defining the urban function-related parameters. The results disclosed that employing urban form and function-related parameters delivers more accurate demarcation outcomes than single parameter use. Hence, the utilization of a multisource geospatial big data fusion approach for the demarcation of urban agglomeration footprint informs urban authorities in developing appropriate policies for managing urban growth.
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spelling doaj.art-04fb35844b89477bb37f10e4d658ba3a2023-11-16T21:36:55ZengMDPI AGLand2073-445X2023-02-0112240710.3390/land12020407Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration FootprintsNelunika Priyashani0Nayomi Kankanamge1Tan Yigitcanlar2Department of Town and Country Planning, University of Moratuwa, Katubedda, Moratuwa 10400, Sri LankaDepartment of Town and Country Planning, University of Moratuwa, Katubedda, Moratuwa 10400, Sri LankaCity 4.0 Lab, School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, AustraliaUrban agglomeration is a continuous urban spread and generally comprises a main city at the core and its adjoining growth areas. These agglomerations are studied using different concepts, theories, models, criteria, indices, and approaches, where population distribution and its associated characteristics are mainly used as the main parameters. Given the difficulties in accurately demarcating these agglomerations, novel methods and approaches have emerged in recent years. The use of geospatial big data sources to demarcate urban agglomeration is one of them. This promising method, however, has not yet been studied widely and hence remains an understudied area of research. This study explores using a multisource open geospatial big data fusion approach to demarcate urban agglomeration footprint. The paper uses the Southern Coastal Belt of Sri Lanka as the testbed to demonstrate the capabilities of this novel approach. The methodological approach considers both the urban form and functions related to the parameters of cities in defining urban agglomeration footprint. It employs near-real-time data in defining the urban function-related parameters. The results disclosed that employing urban form and function-related parameters delivers more accurate demarcation outcomes than single parameter use. Hence, the utilization of a multisource geospatial big data fusion approach for the demarcation of urban agglomeration footprint informs urban authorities in developing appropriate policies for managing urban growth.https://www.mdpi.com/2073-445X/12/2/407urban agglomerationurban formurban functionurban growthurban footprintsustainable urban development
spellingShingle Nelunika Priyashani
Nayomi Kankanamge
Tan Yigitcanlar
Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints
Land
urban agglomeration
urban form
urban function
urban growth
urban footprint
sustainable urban development
title Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints
title_full Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints
title_fullStr Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints
title_full_unstemmed Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints
title_short Multisource Open Geospatial Big Data Fusion: Application of the Method to Demarcate Urban Agglomeration Footprints
title_sort multisource open geospatial big data fusion application of the method to demarcate urban agglomeration footprints
topic urban agglomeration
urban form
urban function
urban growth
urban footprint
sustainable urban development
url https://www.mdpi.com/2073-445X/12/2/407
work_keys_str_mv AT nelunikapriyashani multisourceopengeospatialbigdatafusionapplicationofthemethodtodemarcateurbanagglomerationfootprints
AT nayomikankanamge multisourceopengeospatialbigdatafusionapplicationofthemethodtodemarcateurbanagglomerationfootprints
AT tanyigitcanlar multisourceopengeospatialbigdatafusionapplicationofthemethodtodemarcateurbanagglomerationfootprints