| 非首都功能疏解背景下北京市人口空间分布形态模拟 |
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| 引用本文： | 胡曾曾,赵志龙,张贵祥.非首都功能疏解背景下北京市人口空间分布形态模拟[J].地球信息科学,2018,20(2):205-216. |
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| 作者姓名： | 胡曾曾 赵志龙 张贵祥 |
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| 作者单位： | 1. 首都经济贸易大学 城市经济与公共管理学院,北京 1000702. 中国科学院地理科学与资源研究所 陆地表层格局与模拟院重点实验室,北京 1001013. 城市群系统演化与可持续发展的决策模拟研究北京市重点实验室,北京 100070 |
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| 基金项目： | 北京市社科基金研究基地项目（14JDZHB007）;北京市科委培育项目(Z171100002217021);首都经济贸易大学特大城市经济社会发展研究协同创新中心项目（TDJD201401）;首都经济贸易大学研究生科技创新资助项目 |
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| 摘 要： | 本文以北京市2005年和2010年公里网格人口分布数据为基础,运用CA-Markov模型模拟了北京市2015、2020、2025和2030年4期公里网格人口分布数据集;应用街道尺度的人口数据对模拟精度进行了验证;在模型可靠性良好的基础上,从产业疏解推动人口疏解的角度出发,结合北京市各街道各产业从业人口数据、产业疏解方向和中心城区人口疏解目标,确定了北京市各街道人口疏解的权重,并由此预测了2020年北京市中心城区人口疏解15%后的人口空间分布情况。研究表明： ① 2005-2010年,北京市约90%的公里网格人口密度等级为1级,集中在密云区、怀柔区、延庆区和房山区,而人口密度在10级以上的区域集中在西城区和东城区;② 在无人口疏解政策影响下,2015-2030年北京市公里网格人口分布呈现出低人口密度区域减少、中高人口密度区域增加的态势;③ 在人口疏解政策影响下,至2020年,中心城区人口分布从集中于中高密度等级转向集中于中低人口密度等级。中心城区最高人口密度等级的数值呈下降态势,并且高人口密度等级的区域占比也呈下降态势。除东城区外,其余5个中心城区的人口集中于人口密度等级为5-8级的区域。本文的研究成果可为人口管理、资源配置和政策制定提供科学参考。
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| 关 键 词： | 人口空间分布 公里网格 CA-Markov 非首都功能 北京市 |
| 收稿时间： | 2017-08-08 |
Simulation and Projection of the Spatial Pattern of the Population in Beijing under the Background of Non-capital Function Extraction |
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| HU Zengzeng,ZHAO Zhilong,ZHANG Guixiang.Simulation and Projection of the Spatial Pattern of the Population in Beijing under the Background of Non-capital Function Extraction[J].Geo-information Science,2018,20(2):205-216. |
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| Authors: | HU Zengzeng ZHAO Zhilong ZHANG Guixiang |
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| Institution: | 1. College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China3. Beijing Key Laboratory of Megaregions Sustainable Development Simulation, Beijing 100070, China |
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| Abstract: | Based on population distribution data of Beijing City at the spatial scale of 1 km×1 km grid in 2005 and 2010 and CA-Markov model, we simulated the spatial distribution of the population in 2015, 2020, 2025 and 2030. Then, we used the population data at the spatial scale of street to verify the simulation accuracy. On the basis of good reliability of the model and from the perspective that population redistribution is driven largely through industries transfer, we combined the data on employment quantity of different industries at the spatial scale of streets of Beijing city with the population redistribution goal and transfer direction of industries. Then, we calculated the decentralization weight of each street, and analyzed the spatial distribution after redistributing 15% of the population in the six central urban districts in 2020. The results indicated that, firstly, during 2005-2010, the region of the low population density at level 1 is accounting for 90%, focusing on Miyun District, Huairou District, Yanqing District and Fangshan District. Population density above level 10 focused on Xicheng District and Dongcheng District. Secondly, from 2015 to 2030, the low population density area shows a downward trend and the middle to high population density area shows an upward trend under natural conditions. Thirdly, under the impact of non-capital function extraction, population distribution in the six central urban districts shows a trend from focusing on middle-high level to low-middle level, and the high population density area shows a downward trend in 2020. Except Dongcheng District, the rest of five districts focus on population density area at level 5-8. In conclusion, this study can be useful for population management, resource allocation, and policy making. |
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| Keywords: | spatia distribution of the population kilometer grids CA-Markov model non-capital function Beijing City |
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