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Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata

Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata
Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata
The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities’ urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions.
These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions.
Cellular automata, Land use, Port city, Transport, Urban modeling
0198-9715
Nugraha, Aditya Tafta
df33fa16-daeb-4d68-bd65-c26cda240a5b
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Blainey, Simon
ee6198e5-1f89-4f9b-be8e-52cc10e8b3bb
Nash, Frederick, James
063327dc-829e-4abf-a47d-71e9ff2a9b00
Nugraha, Aditya Tafta
df33fa16-daeb-4d68-bd65-c26cda240a5b
Waterson, Ben
60a59616-54f7-4c31-920d-975583953286
Blainey, Simon
ee6198e5-1f89-4f9b-be8e-52cc10e8b3bb
Nash, Frederick, James
063327dc-829e-4abf-a47d-71e9ff2a9b00

Nugraha, Aditya Tafta, Waterson, Ben, Blainey, Simon and Nash, Frederick, James (2022) Unravelling the dynamics behind the urban morphology of port-cities using a LUTI model based on cellular automata. Computers, Environment and Urban Systems, 92 (3), [101733]. (doi:10.1016/j.compenvurbsys.2021.101733).

Record type: Article

Abstract

The urban morphology is characterised by self-organisation where interactions of multiple agents produce emerging patterns on the urban form. Port-urban relationship added to the complexity of port cities’ urban form. Most urban cellular automata (CA) models simulate land-use evolution through transition rules representing multi-factored local interactions. However, calibration of CA-based urban land use and transport interaction (LUTI) models often utilise manual methods due to complexity of the process. This limits insights on urban interactions to a few explored settlements and prevents applications for planning and assessment of transport policies in other contexts. This paper, therefore, addresses three main points. The paper (i) demonstrates an improved method for the calibration of CA-based LUTI models, (ii) contributes to a better understanding of the urban dynamics in port city systems by quantifying generalizable interactions from a wide range of port-urban settlements, and (iii) illustrates how the use of these interactions in a simulation model can allow long-term impact predictions of planning interventions.
These were done by formulating a model in a similar structure as a neural network model to enable automatic calibration using an application of the gradient-descent algorithm. The model was then used to quantify the dynamics between land-use, geographic, and transport factors in 46 port-based and 10 non-port settlements across Great Britain, thus enabling cross-sectional analysis. Cluster analysis of the calibrated interactions in the study areas was conducted to examine the variations of these interactions. This produced two main groups. In the first group, consisting larger settlements, connections between ports and other urban activities were weaker than in the second group which consisted of smaller port-settlements. Overall, the findings of the research are consistent with existing evidence in the port-cities literature but go further in quantifying the interaction between urban agents within port-urban systems of various sizes and types. These quantified interactions will enable planners to better predict the longer-term consequences of their interventions.

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Unravelling Urban Dynamics of Port Cities - Accepted Manuscript
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Accepted/In Press date: 1 November 2021
e-pub ahead of print date: 25 November 2021
Published date: 1 March 2022
Additional Information: Funding Information: This work was supported by Indonesia's Ministry of Finance through The Indonesia Endowment Fund for Education. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funder. Funding Information: This work made use of Ordnance Survey's AddressBase? Plus dataset, supplied in 2019, under the authorisation given (Contract Number: 40133005). ? Crown copyright (2019) OS. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. This work was supported by Indonesia's Ministry of Finance through The Indonesia Endowment Fund for Education. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funder. Publisher Copyright: © 2021 Elsevier Ltd Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Cellular automata, Land use, Port city, Transport, Urban modeling

Identifiers

Local EPrints ID: 453553
URI: http://eprints.soton.ac.uk/id/eprint/453553
ISSN: 0198-9715
PURE UUID: 69b00780-fc8b-4023-82b6-e6c312bc4609
ORCID for Aditya Tafta Nugraha: ORCID iD orcid.org/0000-0002-4754-4713
ORCID for Ben Waterson: ORCID iD orcid.org/0000-0001-9817-7119
ORCID for Simon Blainey: ORCID iD orcid.org/0000-0003-4249-8110

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Date deposited: 19 Jan 2022 17:46
Last modified: 17 Mar 2024 06:57

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Contributors

Author: Aditya Tafta Nugraha ORCID iD
Author: Ben Waterson ORCID iD
Author: Simon Blainey ORCID iD
Author: Frederick, James Nash

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