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Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change

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Abstract

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

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Acknowledgements

This research has been jointly supported by the National Natural Science Foundation of China (41771414 and 41406146), Shanghai Universities First-Class Disciplines Project-Fisheries (A), and the Demonstration System of High-Resolution Remote Sensing Application in Surveying and Mapping.

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Authors and Affiliations

  1. College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China

    Yongjiu Feng

  2. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Shanghai, 201306, China

    Yongjiu Feng

  3. College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China

    Xiaohua Tong

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  1. Yongjiu Feng
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Correspondence to Xiaohua Tong.

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Feng, Y., Tong, X. Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change. Environ Monit Assess 189, 515 (2017). https://doi.org/10.1007/s10661-017-6224-8

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