地理科学进展 ›› 2016, Vol. 35 ›› Issue (11): 1317-1328.doi: 10.18306/dlkxjz.2016.11.002

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基于居住空间属性的人口数据空间化方法研究

董南1,2(), 杨小唤1,2,*(), 蔡红艳1   

  1. 1. 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
    2. 中国科学院大学,北京 100049
  • 出版日期:2016-11-25 发布日期:2016-11-25
  • 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,博士研究生,主要从事人口地理、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(41271173);国家科技支撑计划课题(2012BAI32B06)

A method for demographic data spatialization based on residential space attributes

Nan DONG1,2(), Xiaohuan YANG1,2,*(), Hongyan CAI1   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of GeographicSciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2016-11-25 Published:2016-11-25
  • Contact: Xiaohuan YANG E-mail:dongnan67@126.com;yangxh@igsnrr.ac.cn
  • Supported by:
    National Natural Science Foundation of China, No.41271173;National Science and Technology Support Program, No.2012BAI32B06

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,在灾害评估、资源配置、智慧城市建设等方面应用广泛。居住建筑物尺度作为精细尺度的重要内容,其人口数据空间化日益引起学术界的关注。本文以居住建筑斑块面积、斑块内建筑面积比重、建筑物层数、公摊率等居住空间属性为人口分布数量的指示因子,以居住建筑的轮廓斑块为人口分布位置的指示因子,利用街道界线和街道常住人口数据为控制单元,建立线性模型,获得了2015年宣城市宣州区6个街道的居住建筑物尺度的人口分布矢量数据,刻画了城市市区人口空间分布的细节信息。结果表明:①以居住空间属性作为人口空间分布的指示因子,获取的人口空间数据精度高,结果可信。29个社区(村)估算人数的相对误差绝对值的平均值低于7%,其中25个社区(村)的相对误差绝对值低于10%。在1102个居住建筑斑块中,估算人数在合理区内的斑块个数占比高于74%,轻微低估区(-10%, 0)和轻微高估区(0, 10%)的斑块总数占比高于9%;②由斑块面积和建筑物层数共同表征的建筑物体积,是建筑物尺度上影响人口空间分布的关键因素;斑块内建筑面积比重属性能进一步提高模型精度;公摊率属性具有“降高升低”作用,但将估算人数调节到合理区的“能力”较弱。

关键词: 人口, 空间化, 居住空间, 居住建筑, 斑块, 宣城市宣州区

Abstract:

Fine spatial scale population distribution has increasingly become a research hotspot yet a difficult question in the field of population geography. It has practical values in application and scientific significance for relevant research, such as disaster risk and impact assessment, resource allocation, and construction of smart cities. Residential building scale is considered an important part of fine spatial scales for population distribution. Research on the spatialization of population data at this scale has increasingly attracted academic attention. In this study, a population distribution vector data set at the residential building scale was established for six residential committees in Xuanzhou District, Anhui Province in 2015 based on residential space attribute data. Data used in the study include residential building patch area, percentage of housing area within residential building patches, building floor number, and public area rate. The method takes residential space attributes as variables for spatializing population data and treats residential building patches as population distribution location in geographical space with town boundary and town-level demographic data as controls. The spatialization method used in this study reveals detailed information about population distribution in urban areas. The results show that: (1) The population distribution data, obtained by using residential space attributes, are proved to be of high accuracy and reliability. The mean absolute relative error for 29 communities (villages) is less than 7%. The absolute relative error of 25 out of 29 communities (villages) is less than 10%. The proportion of patches whose estimated number of people is in reasonable range is higher than 74% in a total of 1102 residential building patches. The proportion of patches whose relative error is in slightly underestimated area (-10%, 0) and overestimated area (0, 10%) is higher than 9%. (2) Building volume , defined by residential building patch area and building floor number, is a key factor to estimate accurately the number of people within a residential building. The percentage of housing area can further improve model accuracy. Public area rate plays an important role to increase estimated number of people in underestimated area and decrease that in overestimated area, but is too weak to adjust the estimated number of people to reasonable range. In conclusion, spatialization based on residential space attributes can be an important method for population spatialization research at the residential building scale.

Key words: population, spatialization, residential space, residential building, patch, Xuanzhou District of Xuancheng City