地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (7): 918-928.doi: 10.12082/dqxxkx.2018.170625

所属专题: 人口与城市研究

• 地球信息科学理论与方法 • 上一篇    下一篇

引入城市公共设施要素的人口数据空间化方法研究

董南1(), 杨小唤2,3,*(), 黄栋2,3, 韩冬锐2,3   

  1. 1. 建设综合勘察研究设计院有限公司, 北京 100007
    2. 中国科学院地理科学与资源研究所 资源环境信息系统国家重点实验室, 北京100101
    3. 中国科学院大学,北京 100049;
  • 收稿日期:2017-12-21 修回日期:2018-01-22 出版日期:2018-07-20 发布日期:2018-07-13
  • 通讯作者: 杨小唤 E-mail:dongnan67@126.com;yangxh@igsnrr.ac.cn
  • 作者简介:

    作者简介:董 南(1984-),男,河北唐山人,博士,注册测绘师,主要从事人口地理、智慧城市、遥感与GIS应用研究。E-mail: dongnan67@126.com

  • 基金资助:
    国家自然科学基金项目(41771460、41271173)

Spatialization Method of Demographic Data Based on Urban Public Facility Elements

DONG Nan1(), YANG Xiaohuan2,3,*(), HUANG Dong2,3, HAN Dongrui2,3   

  1. 1. Comprehensive Institute of Geotechnical Investigation and Surveying, Company Limited, Beijing 100007, China
    2. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101,China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2017-12-21 Revised:2018-01-22 Online:2018-07-20 Published:2018-07-13
  • Contact: YANG Xiaohuan E-mail:dongnan67@126.com;yangxh@igsnrr.ac.cn
  • Supported by:
    National Natural Science Foundation of China, No.41771460, 41271173

摘要:

精细尺度的人口分布是当前人口地理学研究的热点和难点,其在灾害评估、资源配置、智慧城市建设等方面应用广泛。城区是人口分布集中的区域,揭示该区域人口分布差异是精细尺度人口空间化研究的核心内容。本研究基于城市公共设施要素点位数据,对居住建筑斑块进行分类,以社区作为人口数据空间化转换尺度,构建各类别斑块面积与人口数量的多元回归模型,生成了宣州城区居住建筑尺度的人口空间数据,揭示了研究区人口空间分布差异。结果表明:① 该方法生成的人口空间数据精度较高,结果可信。779个居住建筑斑块中,估算人数在合理区内的斑块个数占比为35.4%,相对误差在-20%~20%范围内的斑块个数比例之和为61.2%;城东社区、思佳社区作为精度验证单元,其人数估算的相对误差绝对值低于9%;② 城市公共设施要素数据,尤其是中小学及幼儿园、菜市场及水果店,是建筑物尺度上人口分布的指示性因素,其对多层居住建筑人数的估算精度较高,但对中高层居住建筑人数的估算精度偏低。

关键词: 人口, 空间化, 公共设施要素, 居住建筑, 斑块

Abstract:

The spatial distribution of population at fine-scale has increasingly become research hotspot and a difficulty issue in the field of population geography. It has practical application value and scientific significance for relevant researches, such as disaster assessment, resource allocation and construction of smart cities. The population is concentrated in the urban area. Revealing the population distribution difference in this area is the core content of spatializing population data at the fine scale. In this paper, the urban area of Xuanzhou District was selected as the research area. The population distribution vector data at residential building scale was established by proposing a spatialization method based on urban public facility elements. The method classified residential building patches. And it treated residential building patches as population distribution locations in geographical space with community boundary and community-level demographic data as the control unit. A multiple regression model of patch area and population was constructed. The spatialization method used in this study can reveal the detailed information about the population distribution in urban area. Results show that: ① The population distribution data, obtained by adopting urban public facility elements, is proved to be high accurate and reliable. The number of patches with estimated population in a reasonable range is 35.4% of 779 residential building patches. And the proportion of patches with relative errors of ±20% in population estimation is 61.2%. Moreover, the Chengdong community and Sijia community served as accuracy verification units, the absolute relative error of population estimation in these communities is less than 9%; ② Urban public facility elements, especially primary and secondary schools and kindergartens, vegetable markets and fruit shops, are important factors for accurate estimation of population within a residential building. Their estimation accuracy of number of people is high ifor multi-storied building, but lower for moderate high-rise building.

Key words: population, spatialization, public facility elements, residential building, patch