Abstract
Developing countries have been undergoing dramatic urban growth over the past three decades. It is essential to understand and simulate the urban growth process for smart urban planning and sustainable development purposes. Cellular automata (CA) modeling is an efficient approach to simulating urban land use/cover change; however, the traditional CA method has limitations in simulating the various urban growth patterns and processes. This study aims to analyze the influences of different urban growth characteristics on the effectiveness of CA modeling by conducting a case study over the area in the Pearl River Delta of Southern China. We used the growth rate, landscape expansion index, and spatial dependency to quantify the urban growth characteristics. The effectiveness of CA modeling was measured through a comparison of the simulation results with the reference data. The simulation results and validation analyses reveal that the traditional CA is not applicable for the following three situations: (1) the urban growth pattern characterized by less growth area or a higher ratio of outlying expansion; (2) the urban region that includes several subregions with disparate growth characteristics; and (3) the existence of temporal differences in growth characteristics over a long period.
Highlights
Urban growth, a complex spatial process, is an important social and economic phenomenon in developing countries
Rapid progress in computer science, remote sensing, and geographic information systems technology have facilitated the emerging of various efficient dynamic spatial modeling approaches, such as cellular automata (CA), CLUE-S models [7], and multi-agent models [9]
Urban planners and policy-makers can analyze the different scenarios of urban growth and further evaluate their impacts to support policy-making on urban planning and sustainable development [10]
Summary
A complex spatial process, is an important social and economic phenomenon in developing countries. Rapid progress in computer science, remote sensing, and geographic information systems technology have facilitated the emerging of various efficient dynamic spatial modeling approaches, such as cellular automata (CA), CLUE-S models [7], and multi-agent models [9]. Using these models, urban planners and policy-makers can analyze the different scenarios of urban growth and further evaluate their impacts to support policy-making on urban planning and sustainable development [10]. These techniques require observational data or historical data for the study area to calibrate the CA transition rules
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