Urban expansion in Auckland, New Zealand: a GIS simulation via an intelligent self-adapting multiscale agent-based model

ABSTRACT

Abstract: When modelling urban expansion dynamics, cellular automata models focus mostly on the physical environments and cell neighbours, but ignore the ‘human’ aspect of the allocation of urban expansion cells. This limitation is overcome here using an intelligent self-adapting multiscale agent-based model. To simulate the urban expansion of Auckland, New Zealand, a total of 15 urban expansion drivers/constraints were considered over two periods (2000–2005, 2005–2010). The modelling takes into consideration both a macro-scale agent (government) and micro-scale agents (residents of three income levels), and their multi-level interactions. In order to achieve reliable simulation results, ABM was coupled with an artificial neural network to reveal the learning process and heterogeneity of the multi-sub-residential agents. The ANN-ABM accurately simulated the urban expansion of Auckland at both the global and local scales, with kappa simulation value at 0.48 and 0.55, respectively. The validated simulation result shows that the intelligent and self-adapting ANN-ABM approach is more accurate than an ABM with a general type of agent model (kappa simulation = 0.42) at the global scale, and more accurate than an ANN-based CA model (kappa simulation = 0.47) at the local scale. Simulation inaccuracy stems mostly from the outdated master land use plan.

KEYWORDS:

• Agent-based model
• artificial neural network
• multi-sub-agent
• urban expansion
• human decision
• master plan
• auckland

Data and Codes Availability Statement

The data and codes that support the findings of this study are available with a DOI at https://doi.org/10.17608/k6.auckland.11660415.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Supplementary Materials

supplemental data for this article can be accessed here

Additional information

Notes on contributors

Tingting Xu

Tingting Xu is a Ph.D. student in the school of environment, the University of Auckland. His research is focused on advanced geographical information science technology with machine learning algorithms, remote sensing, and dynamic urban modeling (cellular automata and agent-based model).

Jay Gao

Jay Gao is an Associate Professor in the school of environment, the University of Auckland. His research is focused on quantitative remote sensing and integration of geo-computational methods.

Giovanni Coco

Giovanni Coco is an Associate Professor in the school of environment, the University of Auckland. His research is focused on complexity and pattern formation and machine learning.

Shuliang Wang

Shuliang Wang is a Professor in the school of software, Beijing Institute of Technology. His research is focusing on spatial data mining.