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• Spatiotemporal pattern of habitat quality in Hangzhou was assessed by InVEST model. • Spatial distribution of habitat quality showed positive spatial auto-correlation. • Negative impact of urbanization on habitat quality varied with time and space. • Impacts of landscape pattern on habitat quality differed in magnitude and direction. China has experienced the most serious habitat degradation, especially in fast-growing metropolis cities. Although increasing attentions have been brought to this issue, we still lack the understanding of the quantitative impacts of urbanization and landscape pattern on habitats. In this study, we used the "Integrated Valuation of Environmental Services and Trade-off" (InVEST) model to evaluate the habitat quality in Hangzhou city. We further employed spatial auto-correlation to analyze its spatiotemporal pattern variation characteristics. Finally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to explore the impacts of urbanization and landscape pattern change on habitat quality. The results show that the habitat quality index of Hangzhou decreased from 0.608 to 0.577 during 2004–2015, and these areas mainly located around the suburb decreased significantly. The spatial distribution of habitat quality showed significantly positive spatial auto-correlation, and the overall spatial auto-correlation degree of the habitat quality increased during this time. Rapid urbanization has significant negative effects on habitat quality in various areas, while the magnitude and direction of the impacts of landscape pattern on habitat quality differed in time and space. These results provide decision-making criteria for formulating differential urban development policies and landscape management measures for urban ecological sustainability. [ABSTRACT FROM AUTHOR]

• The spatial dynamics of urban construction land were explored by using a hybrid Logistic CA model. • We visualize how predictions on spatial expansion will benefit urban planners and policymakers. • The model could explain the underlying mechanism of urban growth in a spatial-economic and social way. • We indicate the effective quantification of land patches can improve the compact urban growth. • Effective quantification of the construction land patches could reduce the unordered urban growth. 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. [ABSTRACT FROM AUTHOR]