城市住区形态时空模拟——以厦门岛为例

住区形态变迁受到人口迁移、住区满意度和低碳城市发展政策等因素的限制,常用的土地利用模型难以有效表征这一相互制约关系,使得这方面的研究仍然相对不足。通过耦合SD模型和CLUE-S模型,充分发挥了2个模型在宏观情景模拟和微观土地分配上的优势,模拟了住区、人口、住区碳足迹等制约因素的相互关系,为住区形态变迁时空模拟提供了一种有效的方法。以厦门岛为例,根据研究区历史统计数据、问卷调查数据构建了住区形态变迁SD模型,模拟了基准情景、紧凑情景和低碳情景3种不同发展情景下各类住区类型的用地需求,结合CLUE-S模型预测了3种情景下2009年—2020年各类住区类型的用地范围。结果表明,基准年住区类型Ⅰ、Ⅱ、Ⅲ三者占地面积比例为1∶1.18∶0.83,基准情景下2018年住区类型Ⅲ将成为主要的住区类型。低碳发展和紧凑发展是惯性发展的两种极端情况,体现在总住区面积、人均住宅面积和人均碳足迹大小的变化,但是对厦门岛总人口数量的影响并不大。根据目前厦门的发展趋势,低碳发展情景与紧凑发展情景相结合可能更靠近现实。在空间分布上,住区类型Ⅰ未来不再新建;住区类型Ⅱ遵循现状继续发展的惯性较大;住区类型Ⅲ分布在征地成本相对较低的区域。模型模拟结果能够为住区用地规划、住区发展对策建议提供有效的技术支撑。

Spatiotemporal simulation of urban settlement morphology: a case study of Xiamen Island

Settlement morphology transition is constrained by population migration, settlement satisfaction, and low-carbon city policies. Many land use models poorly describe these transitions, rendering studies in this field relatively inadequate. Xiamen Island, a rapidly urbanizing area in the southeast of Fujian Province in China, was selected as a case study for settlement morphology transition. We generated a system dynamics (SD) model of the carbon footprint of this settlement based on settlement morphology transition and simulated the transition of settlement morphology using this SD model. In this model, the changing social needs of a residential area over the next 20 years were simulated, and the settlement carbon footprint was calculated under three different scenarios (basic, low-carbon, and compact city). We used the results of these simulations as the input of the non-spatial demand module in the CLUE-S (Conversion of Land use and its Effects at Small extent) model. In the study of settlement spatial allocation using the CLUE-S model, we simulated the transition of settlement morphology under three scenarios and depicted the spatial conversion of land use on Xiamen Island over the next 10 years. Next, we discussed the allocation characteristics of settlement types II and type III, which are predicted to be the major settlement types in decades. In summary, this study attempted to depict the transition of settlement morphology on Xiamen Island in multiple dimensions-time, space, quantity, and sequence. To address these issues, we clarified the development characteristics of the settlement in different ways, such as driving forces, structures, spatial allocation, and using various scenarios. The simulation results showed that the SD and CLUE-S models bridge the gap between non-spatial land use policies and the spatial characteristics. The simulation results of the SD model revealed that 1) the ratio of three types of settlement, I, II, and III, was 1:1.18:0.83 in 2009; type III will become predominant in 2018. 2) Over the next 20 years, the total population and the demand for land resources on Xiamen Island will continue to increase. The carbon footprint of Xiamen Island under different scenarios varied significantly. 3) In the basic scenario, the total population of Xiamen Island was predicted to reach 2.40 million in 2030, while the total settlement area was predicted to be 75.3 km². The carbon footprint was predicted to increase and reach a plateau phase, at 4605 kg C/cap, in 2022. 4) In the low-carbon scenario, the total population of Xiamen Island was predicted to reach 2.44 million in 2030, while the total settlement area was predicted to be only 66.4 km². The carbon footprint was predicted to increase and reach a plateau phase, at 3771 kg C/cap, in 2016. 5) In the compact-city scenario, the total population of Xiamen Island was predicted to reach 2.52 million in 2030, while the total settlement area was predicted to be 84.7 km². The carbon footprint was predicted to increase and reach a plateau phase, at 5759 kg C/cap, in 2028. The simulation results of CLUE-S model indicated that settlement type II tends to distribute in areas with high population density, convenient traffic, and proximity to the city center, which can be summarized as the inertial development mode. In contrast, settlement type III tends to distribute in areas with low population density, low elevation, and proximity to commercial centers, which can be summarized as the low-land-cost development mode. Finally, we provide some recommendations to facilitate a balance between the low-carbon and compact-city scenarios. We hope that this study will contribute to the formulation of energy-saving measures as well as the construction of new settlement types. We also hope that it will provide scientific support for the sustainable development of urban settlements.