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Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques

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Abstract

In recent decades, attaining urban sustainability is the primary goal for urban planners and decision makers. Among various aspects of urban sustainability, environmental protection such as agricultural and forest conservations is very important in tropical countries like Malaysia. In this regard, compact urban development due to high density, rural development containment is known as the most sustainable urban forms. This paper attempts to propose an integrated modeling approach to predict the future land use changes by considering city compactness paradigms. First, the cellular automata (CA) were applied for calculating land use conversion. Next, weights-of-evidence (WoE) which is based on Bayes theory was utilized to calibrate CA model and to support the transitional rule assessment. Several urban-related parameters as well as compact city indicators were utilized to estimate the future land use maps. The results showed how compact development parameters and site characteristics can be combined using the WoE model to predict the probability of land use changes. The modeling approach supports the essential logic of probabilistic methods and indicates that spatial autocorrelation of various land use types and accessibility is the main drivers of urban land use changes.

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Acknowledgements

The authors would like to thank the local planning council (JPBD), Malaysia, for providing various data sets used in this paper. In addition, we wish to thank the Ministry of Higher Education of Malaysia for financial support for this research. Thanks to two anonymous reviewers for their valuable comments which helped us to improve the earlier version of the manuscript.

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Correspondence to Biswajeet Pradhan.

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Abdullahi, S., Pradhan, B. Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques. Environ Earth Sci 77, 251 (2018). https://doi.org/10.1007/s12665-018-7429-z

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