Urban spatial growth modeling using logistic regression and cellular automata: A case study of Hangzhou

Abstract

Abstract Why does some urban area grow faster than others? Although the role of spatial optimization of urban construction land is at the core of regional economic development, the question remains to be answered so far. This paper aims to explore the spatial dynamics of urban construction land in the central urban areas by integrating Logistic regression and cellular automata models. The combination of the two modeling approaches aims to investigate the evolving dynamics of urban land use patterns and further visualize how predictions on spatial expansion will benefit urban planners and policymakers. Simulation analysis emphasized to what extent do the influencing factors promote or inhibit urban growth. Theoretical frameworks tend to explain the underlying mechanism of urban growth in a spatial, economic and social way, taking the city growth as a self-organized organism with complex actors and rules. In this paper, we present a hybrid Logistic cellular automata model to examine the city’s self-organizing spatial growth process from a bottom-up perspective and interpret why non-construction land was converted to construction land for urban development purposes at Hangzhou in the past two decades. We argue that although the construction land is dispersed irregularly across the city, the logistic cellular automata model will generate the underlying patterns of urban expansion and offer more facts-based implications.