Optimizing the location of physical activity spaces (PAS) to ensure health, equity and efficiency has long been an important issue in urban planning. Given the health benefits of urban green spaces (UGS), taking Gongshu District in Hangzhou as a case, we examine the issue of where such PAS should be located to optimize three objectives: (1) minimize the distance between PAS and UGS; (2) maximize the accessibility of PAS and (3) maximize the population that falls within the coverage range. This study develops a multi-objective optimization of physical activity spaces model (MOPAS) based upon multi-objective particle swarm optimization to yield a set of non-dominated Pareto optimum solutions that can be used to determine the most practical tradeoffs between the conflicting objectives. It compares the advantages and disadvantages of the Pareto solutions and evaluates the construction situation of locations and the implementation feasibility. Decision-makers can choose the best solution according to subjective preferences and objective conditions. The MOPSO holds great promise for improving the location optimization of PAS and the methods applied can be adapted to support multi-objective optimization of facilities in urban planning globally.
The ecological restoration of territorial space emphasizes the synergy between ecology and social development. On this basis, we used landscape index analysis methods to explore the spatiotemporal evolution of landscape patterns in urban areas on a district scale. Then, we used multiple regression analysis to explore the driving factors behind this evolution. The results showed the following: (1) Landscape compositions have changed significantly. The growth rate of construction land in the main districts was about three times that in the urban area. (2) There were differences in the characteristics of landscape pattern evolution. Arable land is becoming more fragmented as construction land expands outward. The shapes of public green spaces, arable land, and woodlands tend to be simple and regular. The degree of both urban sprawl and agglomeration decreased in the urban area and the main districts. Meanwhile, landscape separation first decreased and then increased, and landscape diversity increased. (3) Population growth, industrial development, changes in industrial structure, and real estate development are the main driving factors of landscape pattern evolution. Based on this, this study puts forward some suggestions for landscape pattern optimization, which is significant for ecological restoration planning and promotion.
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