Abstract
Change in urban construction land use is an important factor when studying urban expansion. Many scholars have combined cellular automata (CA) with data mining algorithms to perform relevant simulation studies. However, the parameters for rule extraction are difficult to determine and the rules are simplex, and together, these factors tend to introduce excessive fitting problems and low modeling accuracy. In this paper, we propose a method to extract the transformation rules for a CA model based on the Classification and Regression Tree (CART). In this method, CART is used to extract the transformation rules for the CA. This method first adopts the CART decision tree using the bootstrap algorithm to mine the rules from the urban land use while considering the factors that impact the geographic spatial variables in the CART regression procedure. The weights of individual impact factors are calculated to generate a logistic regression function that reflects the change in urban construction land use. Finally, a CA model is constructed to simulate and predict urban construction land expansion. The urban area of Xinyang City in China is used as an example for this experimental research. After removing the spatial invariant region, the overall simulation accuracy is 81.38% and the kappa coefficient is 0.73. The results indicate that by using the CART decision tree to train the impact factor weights and extract the rules, it can effectively increase the simulation accuracy of the CA model. From convenience and accuracy perspectives for rule extraction, the structure of the CART decision tree is clear, and it is very suitable for obtaining the cellular rules. The CART-CA model has a relatively high simulation accuracy in modeling urban construction land use expansion, it provides reliable results, and is suitable for use as a scientific reference for urban construction land use expansion.
Highlights
The cellular automata (CA) model is a grid dynamics model that uses completely discrete state, time, and space variables as well as local spatial interaction and temporal causality [1]
The main research ideas were as follows: (1) we determined the factors that affect changes in urban construction land use; (2) based on the analysis of existing data, we adopted the Classification and Regression Tree (CART) decision tree to train the weight of impacting factors and construct the logistic regression function; (3) combined with the logistic regression function obtained by the CART training, we constructed a CA model to predict variations in urban construction land use; (4) we simulated and predicted changes in urban construction land use; and (5) we conducted an evaluation of the accuracy of the simulation results
By introducing the random factor, we improved the optimum results of the CART decision tree, and avoided over fitting
Summary
The cellular automata (CA) model is a grid dynamics model that uses completely discrete state, time, and space variables as well as local spatial interaction and temporal causality [1]. The main approaches included Markov China, multi-criteria decision making and grayscale, and knowledge discovery of rough sets These transformation methods are mainly expressed using mathematical formulas, but determination of the parameters for the formulas is very difficult [3]. Chinese and international scholars use artificial intelligence (AI) methods such as the ant colony algorithm, the support vector machine, and the neural network algorithm [4,5,6,7,8,9,10], to define the transformation rules of a model These artificial intelligence algorithms can macroscopically reflect the interactive relationships among the structure, function, and behavior of the land use system. The CART decision tree has a clear structure, fast operation speed, and convenient processing of spatial data By combining it with the logistic regression and cellular automaton model, we can fairly well consider the impacting factors that affect the change of land use. Based on the results of the experiments to validate this technique, the CART-CA model is relatively accurate when simulating urban construction land use expansion in Xinyang City
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