地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (10): 1381-1387.doi: 10.12082/dqxxkx.2018.180130

• 地球信息科学理论与方法 • 上一篇    下一篇

多目标多测度数据空间抽样方法

曹馨1,2(), 李新虎1,*(), 高丽玲1, 刑莉1,3   

  1. 1. 中国科学院城市环境研究所 中国科学院城市环境与健康重点实验室,厦门市城市代谢重点实验室,厦门 361021
    2. 中国科学院大学,北京 100049
    3. 厦门大学环境与生态学院,厦门 361102
  • 收稿日期:2018-03-14 修回日期:2018-07-12 出版日期:2018-10-25 发布日期:2018-10-17
  • 通讯作者: 李新虎 E-mail:xcao@iue.ac.cn;xhli@iue.ac.cn
  • 作者简介:

    作者简介:曹 馨(1993-),女,江西南昌人,硕士生,研究方向为城市功能与GIS数据分析。E-mail: xcao@iue.ac.cn

  • 基金资助:
    国家自然科学基金项目(41671444)

Site Selection of Multi-objective Survey

CAO Xin1,2(), LI Xinhu1,*(), GAO Liling1, XING li1,3   

  1. 1. Key Lab of Urban Environment and Health, Key lab of urban metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    2. University of Chinese Acadamy of Science, Beijing 100049
    3. College of the Environment & Ecology, Xiamen University, Xiamen 361102;
  • Received:2018-03-14 Revised:2018-07-12 Online:2018-10-25 Published:2018-10-17
  • Contact: LI Xinhu E-mail:xcao@iue.ac.cn;xhli@iue.ac.cn
  • Supported by:
    National Natural Science Foundation of China, No.41671444.

摘要:

社会问卷调查往往需要针对多目标测度不同类型的数据,而传统的抽样方法主要针对单目标对象,且数据类型为数值型数据。本研究以厦门岛出行调查为例,调查问卷包含了住区特征、居民社会经济状况、就业情况、出行方式、出行目的与时间等方面的指标,提出了以变异度模型为主的新方法。以厦门岛住区居民出行所带来的能耗问题收集的少量先验问卷信息以及历史数据为基础,通过模型表征测度不同类型变量的空间变异性,将其作为空间分层的依据从而完成抽样布点方案,评价精度通过抽样方差进行。结果表明:① 综合多种因素分层可以灵活地解决调查中涉及类别数据以及数值型数据的问题,将影响抽样问题的各类型因素体现到样点空间布点方案中,扩大三明治空间抽样的应用范围;② 三明治空间抽样各层样点的分布以及容量受层变异度值(相当于方差)的影响,但其样本容量并不是简单随着区域的层变异度值的增大而增大,空间抽样样本容量同时受到多个因素的影响,其地理空间的大小也是其中一个影响因素;③ 变异度模型成功地量化了各种类型数据,通过少量的预调查得到更详细的抽样方案,其抽样精度为0.0002,样本容量35,满足了问卷调查的目标需求并将抽样样本容量控制在合理的范围之内。

关键词: 多目标抽样, 多类型数据, 变异度, 社会问卷调查, 厦门岛

Abstract:

The social survey questionnaires sometimes are multi-objective, and some objectives are attribute data or category variables. However, traditional spatial sampling theory is primarily used in single-target and non-attribute data. It is not suitable for the investigation required multi-type target objects. A new method based on variability model was proposed in this paper. The different types of variables can be measured by variability model on the spatial variability and used as the basis for spatial stratification to design sampling plan. Depending on the questionnaire about residents' daily travel energy consumption of Xiamen Island and historical data in this study, we calculated the values of variability of samples by the model and get the map of spatial distribution. Contrasted with the map of hierarchical combination of the integrated factors and the map of stratified sampling by experts,it got the value of variability through the pre-investigation, ultimately obtained program of sampling point distribution about the target settlement of Xiamen Island. The results showed that: (1) Contrast to experts layer, the main component layer and combination of factors layer from the perspective of variability values, combination of factors layered approach is more reasonable. This method reflects various factors that affect sampling in spatial distribution plan, which offers solution for the survey involving multiple data category and expands the application scope of “Sandwiches” model. (2) The number and distribution of samples are affected by variance in the sandwiches spatial Sampling. But they are not increased with the increase of variance, the number and the distribution of samples are affected by many factors, the size of the geographical space is one factor. (3) Variability model quantified various types of data about Sampling objectives successfully. In our study, we got more detail sampling plan based on pre-investigation in small range. The sampling accuracy is 0.0002while sample size is 35. It meets the practical requirements of the survey of Xiamen. The number of samples and the accuracy are controlled in the reasonable range. Not only we saved manpower and material resources, but also, we improved the accuracy of sampling.

Key words: multi-objective sampling, multi-type data, variability, social survey, Xiamen