[1]韩用顺,王文娟,张东水,等.一种基于分区和识别真实居住区的人口空间化方法——以雅砻江流域为例[J].山地学报,2022,(2):303-316.[doi:10.16089/j.cnki.1008-2786.000673]
　HAN Yongshun,WANG Wenjuan,ZHANG Dongshui,et al.A Population Spatialization Method Based on Regional Differentiations in Population and Actual Housing Vacancy — Taking the Yalong River Basin of China as an Example[J].Mountain Research,2022,(2):303-316.[doi:10.16089/j.cnki.1008-2786.000673]
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一种基于分区和识别真实居住区的人口空间化方法——以雅砻江流域为例
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2022年第2期
页码:
303-316
栏目:
山地技术
出版日期:
2022-03-25

文章信息/Info

Title:
A Population Spatialization Method Based on Regional Differentiations in Population and Actual Housing Vacancy — Taking the Yalong River Basin of China as an Example
文章编号:
1008-2786-(2022)2-303-14
作者:
韩用顺1; 2;  王文娟1;  张东水2;  李爱国1;  王仁超3*;  常禹龙2
1. 河南理工大学 测绘与国土信息工程学院,河南 焦作 454003; 2. 湖南科技大学 地球科学与空间信息工程学院,湖南 湘潭 411201; 3. 电子科技大学, 成都 611731
Author(s):
HAN Yongshun1; 2;  WANG Wenjuan1;  ZHANG Dongshui2;  LI Aiguo1;  WANG Renchao3*;  CHANG Yulong2
1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, Henan, China; 2. School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China; 3. University of Electronic Science and Technology of China, Chengdu 611731, China
关键词:
人口空间化方法;  真实居住区;  人口差异性分区;  雅砻江流域
Keywords:
population spatialization method;  housing vacancy;  zoning of population differentiation;  the Yalong River basin
分类号:
P237
DOI:
10.16089/j.cnki.1008-2786.000673
文献标志码:
A
摘要:
人口空间化可直观反映人口分布的细节特征,是科学开展区域规划、资源分配、灾害评估等的重要技术。建筑物空置和人口分布区域差异是影响人口精细化空间估算的两个难点。本研究以雅砻江流域为例,提出了一种基于分区和识别真实居住区的人口空间化方法,解决了建筑数据不能反映真实人口居住情况的遥感估计技术难点。根据研究区人口分布和经济发展特征的空间分异性,将雅砻江流域分为高经济高人口密度区、高人口密度区和低人口密度区三个类型区,将建筑数据分别与夜间灯光和植被指数结合,提取真实居住区,建立顾及真实居住和区域差异性的人口空间化模型,生成精细化的雅砻江流域200 m×200 m的人口分布图(YLJ2018)。研究结果表明:(1)以建筑数据为基础,结合珞珈一号夜间灯光数据和植被指数,对真实居住区进行识别与提取,解决了房屋空置问题,避免了大面积相同人口的错分现象;(2)模型的平均相对误差(MRE)为4.51%,各分区的MRE分别为2.23%、4.35%和4%,均优于WorldPop和LandScan数据集,表明该方法能有效提高人口空间化的精度;(3)YLJ2018 与WorldPop和LandScan数据集整体上具有相似的人口分布趋势,但在局部上WorldPop和LandScan数据集均存在一定程度的错分现象,而YLJ2018在低人口密度区人口主要分布在道路周边,在高人口密度区和高经济高人口密度区人口集中分布在河流附近,在人口密集区呈现从中心向外围逐渐降低的趋势,其空间化结果更加符合人口分布规律和研究区实际。因此,顾及真实居住和区域差异性的精细人口空间化方法得到的YLJ2018能够更精确地反映雅砻江流域各县(市、区)的真实人口分布情况,可以为该区经济社会发展规划和防灾减灾管理提供依据与参考。
Abstract:
Population spatialization can directly reveal the detailed features of population distribution and it is an essential for scientific regional planning, resource allocation, disaster assessment, etc. There were two factors, housing vacancy and regional differences in population distribution, which exert significant influences on the refined estimation of population spatialization. In this study, the Yalong River basin was targeted as case study. Based on geographical zoning and actual housing vacancy, a method to estimate population spatialization was proposed, aiming at the problem of large spatial differences in population distribution and the incomplete building data which cannot stand for actual residency in building blocks. According to the spatial differentiation of population distribution and economic development in the study area, the Yalong River basin was divided into three zones: high economy and high population density area, high population density area, and low population density area; Building data were utilized by integrating with night lights in residences as well as vegetation index for extracting the data of housing vacancies, then a population spatial model was established; A refined population distribution map indexed by YLJ2018 was generated in the resolution of 200 m × 200 m for the Yalong River basin. It achieves the following results:(1)it was justified that our proposed method was suitable for estimation of population spatialization, concisely localizing vacant houses and avoiding misclassification of population with same sizes. Based on the building data, combined with the night light data of Luojia-1 and vegetation index, it successfully extracted and recognized residency in building complex.(2)The mean relative error(MRE)of the model was 4.51%, and they were 2.23%, 4.35% and 4% in three zones, respectively, with higher resolution than those from WorldPop and LandScan datasets, suggesting that this method can effectively improve the accuracy of population spatialization.(3)In YLJ2018 it revealed a similar population distribution trend as compared with WorldPop and LandScan datasets as a whole, but there was yet a certain degree of misclassification phenomenon in both WorldPop and LandScan datasets in part. To be specific, in the low population density area, the population presented in YLJ2018 was mainly distributed along roads; Comparably, in high population density area or high economy with high population density area, the population was concentrated near rivers, describing a pattern of gradually decreasing from the center to the periphery of the study area. This is to justify that depending on our proposed method, population distribution in the Yalong River basin obtained was quite consistent with factual observations and common knowledge. Therefore, by the refined population spatialization method with inclusive of housing vacancy and regional differentiation of population as the model coefficients, it could more accurately describe actual population distribution on a county basis(cities and districts)in the Yalong River basin, and it would provide basis and reference for the economic and social development planning and disaster prevention and reduction management of the area.

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备注/Memo

备注/Memo:
收稿日期(Received date):2022-02-19; 改回日期(Accepted date):2022-04-21
基金项目(Foundation item):国家重点研发计划(2021YFB3901203); 云南省高校高烈度地震山区交通走廊工程地质病害早期快速判识与防控重点实验室资助项目(2022-ZD-02); 湖南省自然科学基金(2020JJ4295). [National Basic Research Program of China(2021YFB3901203); Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province(2022-ZD-02); Natural Science Foundation of Hunan Province(2020JJ4295)]
作者简介(Biography):韩用顺(1974-),男,博士、教授,主要研究方向:灾害监测评估与3S技术及应用。[HAN Yongshun(1974-), male, Ph.D., professor, research on disaster monitoring assessment, 3S technologies and applications] E-mail: yongshunhan@126.com
*通讯作者(Corresponding author):王仁超(1989-),男,博士,主要研究方向:地质灾害防治及3S应用。 [WANG Renchao, male, Ph.D., research on geohazards control and 3S application] E-mail: renchao1225@sina.com
更新日期/Last Update: 2022-03-30