Calibrating a Land Parcel Cellular Automaton (LP-CA) for urban growth simulation based on ensemble learning. Issue 12 (2nd December 2017)

• Record Type: Journal Article
• Authors: Chen, Yimin; Liu, Xiaoping; Li, Xia
• Is Part Of: International journal of geographical information science. Volume 31:Issue 12(2017)
• Page Start: 2480
• Page End: 2504
• Publication Date: 2017-12-02
• Subjects: Cellular automata -- land parcels -- irregular cells -- ensemble learning; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2017.1367004
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: The reliability of raster cellular automaton (CA) models for fine-scale land change simulations has been increasingly questioned, because regular pixels/grids cannot precisely represent irregular geographical entities and their interactions. Vector CA models can address these deficiencies due to the ability of the vector data structure to represent realistic urban entities. This study presents a new land parcel cellular automaton (LP-CA) model for simulating urban land changes. The innovation of this model is the use of ensemble learning method for automatic calibration. The proposed model is applied in Shenzhen, China. The experimental results indicate that bagging-Naïve Bayes yields the highest calibration accuracy among a set of selected classifiers. The assessment of neighborhood sensitivity suggests that the LP-CA model achieves the highest simulation accuracy with neighbor radius r = 2. The calibrated LP-CA is used to project future urban land use changes in Shenzhen, and the results are found to be consistent with those specified in the official city plan.