基于卫星遥感和POI数据的人口空间化研究——以广州市为例

doi:10.13284/j.cnki.rddl.003179

热带地理 ›› 2020, Vol. 40 ›› Issue (1): 101-109.doi: 10.13284/j.cnki.rddl.003179

基于卫星遥感和POI数据的人口空间化研究——以广州市为例

赵鑫¹, 宋英强^1a, 刘轶伦^1a, 陈飞香^1a, 胡月明^1,^2,³()

^1. a. 华南农业大学资源环境学院；b. 国土资源部建设用地再开发重点实验室；c. 广东省土地利用与整治重点实验室；d. 广东省土地信息工程技术研究中心，广州 510642
^2. 青海大学农牧学院，西宁 810016
^3. 电子科技大学资源与环境学院，成都 610054

收稿日期:2019-04-15 修回日期:2019-09-04 出版日期:2020-01-10 发布日期:2020-02-24
通讯作者: 胡月明 E-mail:yueminghugis@163.com
作者简介:赵鑫（1994—）,女,硕士研究生,主要研究方向为土地利用与地理信息系统,（E-mail）582625232@qq.com。
基金资助:
国家重点研发计划(2016YFC0501801);广州市科技计划项目(201807010048);国家自然科学基金(41601404)

Population Spatialization Based on Satellite Remote Sensing and POI Data: Guangzhou as an Example

Zhao xin¹, Song Yingqiang^1a, Liu Yilun^1a, Chen Feixiang^1a, Hu Yueming^1,^2,³()

^1. a. College of natural resources and environment, South China Agricultural University; b. Key Laboratory of Construction Land improvement, Ministry of Land and Resources; c. Guangdong Province Key Laboratory for Land Use and Consolidation; d. Guangdong Province EngineeringResearch Center for Land Information Technology, Guangzhou 510642, China
^2. College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
^3. College of natural resources and environment, University of Electronic Science and Technology of China, Chengdu 610054, China

Received:2019-04-15 Revised:2019-09-04 Online:2020-01-10 Published:2020-02-24
Contact: Hu Yueming E-mail:yueminghugis@163.com

摘要/Abstract

摘要：

以广州市为例,基于NPP/VIIRS夜间灯光、土地利用、POI（Points of Interest）等自然地理和社会经济因素,构建了人口空间化指标体系,采用主成分赋权法确定人口分布权重,利用GIS技术对人口统计数据进行了空间化处理。结果显示：综合考虑了自然地理和社会经济因素的人口空间化结果与真实的人口空间格局相吻合,空间分辨率为30 m,且相对误差绝对值＜25%的乡镇有62个,所占比例为36.47%;而不加POI数据得到的人口空间化结果,相对绝对值＜25%的乡镇有33个,所占比例约为20%,精度明显降低。结果表明：1）综合考虑NPP/VIIRS夜间灯光、土地利用、POI等自然地理和社会经济因素,有助于实现精度较高的人口空间化结果;2）将能够反映微观细节信息的POI数据引入人口空间化研究,扩展了人口空间化的数据源,并且可以提高人口空间化结果的精度。

关键词: NPP/VIIRS, DMSP/OLS, 人口空间化, POI, 广州

Abstract:

The fine-scale spatial distribution of a population can provide assistance for decision-making regarding research, such as urban planning, economic decision-making, disaster prevention, and crime management, which is of considerable importance to the study of human activities. Considering Guangzhou as an example, this study constructed a population spatialization index system based on NPP/VIIRS night light intensity, land use, points of interest (POI), and other natural geographical and socio-economic factors. Further, the principal component weighting method was used to calculate the weight of the population distribution. According to which, geographic information system (GIS) technology was used to assign census data to each pixel. Finally, an accuracy test was performed by comparing the result of the population spatialization with the real population of townships of Guangzhou. The result of the population spatialization, considering natural geography and socio-economic factors, is consistent with the real population spatial pattern, and the spatial resolution is 30 m. The accuracy test result showed that the absolute relative error of 62 townships was less than 25%, which accounted for 36.47% of all townships in Guangzhou, indicating that the result of the population spatialization is highly accurate. The population spatialization result without POI data showed that the absolute relative error of 33 townships was less than 25%, which accounted for 20% of all townships in Guangzhou, showing that the accuracy of population of the spatialization result was significantly reduced. Compared with other studies on population spatialization, this study included the following characteristics: 1) a comprehensive consideration of NPP/VIIRS night light intensity, land use, POI, and other natural geography and socio-economic factors, which are helpful for achieving high-precision results of population spatialization and 2) POI data that can reflect surface features, such as urban land use types and population activity preferences. Additionally, POI data are easy to acquire, rich in data volume, have a high positioning accuracy, and have a better resolution regarding micro details. Introducing POI data into population spatialization research can expand the data source of population spatialization and improve the accuracy of population spatialization results.

Key words: NPP/VIIRS, DMSP/OLS, Spatial distribution of population, POI, Principal Component Analysis, Guangzhou

中图分类号:

P208

赵鑫, 宋英强, 刘轶伦, 陈飞香, 胡月明. 基于卫星遥感和POI数据的人口空间化研究——以广州市为例[J]. 热带地理, 2020, 40(1): 101-109.

Zhao xin, Song Yingqiang, Liu Yilun, Chen Feixiang, Hu Yueming. Population Spatialization Based on Satellite Remote Sensing and POI Data: Guangzhou as an Example[J]. Tropical Geography, 2020, 40(1): 101-109.

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数据名称	数据来源	属性
2015年土地利用数据（包括河流、道路）	遥感解译Landsat image（2015-10-08）	30 m×30 m栅格
2015年POI	网络爬虫	矢量点数据
2015年DEM数据	地理空间数据云http://www.gscloud.cn/	30 m×30 m栅格
2015年NPP/VIIRS	https://ngdc.noaa.gov	500 m×500 m栅格
2015年DMSP/OLS	https://ngdc.noaa.gov	1 000 m×1 000 m栅格
2015年人口户籍统计数据	各区统计年鉴http://cyfd.cnki.com.cn/N2016010323.htm	区县级、乡镇（街道）级
2015年广州市行政区划	广州市行政图数字化	矢量面数据

A目标层	B准则层	C指标层
A人口空间化评价指标体系	B1社会经济	C1土地利用类型
		C2夜间灯光强度
		C3教育医疗核密度
		C4餐饮核密度
		C5政府机关及社会团体核密度
		C6交通设施核密度
		C7文化体育核密度
	B2自然地理	C8距最近道路距离
		C9距最近河流距离
		C10坡度

基于卫星遥感和POI数据的人口空间化研究——以广州市为例

Population Spatialization Based on Satellite Remote Sensing and POI Data: Guangzhou as an Example

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