Please wait a minute...
img

官方微信

遥感技术与应用  2021, Vol. 36 Issue (2): 463-472    DOI: 10.11873/j.issn.1004-0323.2021.2.0463
遥感应用     
多源信息耦合的GDP空间化研究—以北京市为例
张爱华1(),潘耀忠2,3(),明艳芳1,王金云2
1.山东科技大学 测绘与空间信息学院,山东 青岛 266590
2.遥感科学国家重点实验室,北京师范大学地理科学学部,北京 100875
3.青海师范大学 地理科学学院,青海 西宁 810016
Research of GDP Spatialization based on Multi-source Information Coupling:A Case Study in Beijing
Aihua Zhang1(),Yaozhong Pan2,3(),Yanfang Ming1,Jinyun Wang2
1.College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China
2.State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China
3.College of Geographical Sciences,Qinghai Normal University,Xi'ning,810016,China
 全文: PDF(3995 KB)   HTML
摘要:

针对统计数据对GDP空间分布信息表达存在的局限性,耦合GDP统计数据、城市兴趣点数据、夜间灯光数据以及土地利用数据,提出了一种GDP空间化方法。以北京市为实验区,首先,利用土地利用类型与GDP的关系,实现了第一产业GDP的100 m网格空间化;其次,通过计算兴趣点密度分布情况,并耦合夜间灯光指数以及土地利用类型,建立了第二、三产业GDP空间化模型;最终,将分产业结果进行空间整合,完成北京市100 m网格的GDP分布结果。结果表明:加入兴趣点的多源耦合模型较夜间灯光指数单因素模型,R2从0.84提高至0.92,生成的结果可以更好地体现区域GDP的空间差异。表明多源信息耦合模型可以提高GDP空间化模拟精度,兴趣点数据可以有效地反映GDP空间分布,为GDP空间化提供数据支持。

关键词: GDP空间化兴趣点夜间灯光指数土地利用    
Abstract:

Aiming at the limitations of statistical data on the spatial distribution information of GDP, a method of GDP spatialization was proposed by coupling GDP statistics, urban Point Of Interest(POI), nighttime lighting data and land use data. First, taking Beijing as an experimental area, using the relationship between land use type and GDP, the 100m grid of the first industry GDP will be spatialized. Secondly, establishing the spatialized expression model of GDP in the second and third industries by determining coupling the POI kernel density, the nighttime lighting index and land use data. Spatially integrate the results of sub-industry and complete the GDP distribution results of Beijing with 100 m resolution finally. The results show that the multi-source coupling model with the POI is higher than the general nighttime light index single factor model. R2 is increased from 0.84 to 0.92. The generated results can better reflect the spatial difference of regional GDP. The results show that multi-source coupling model can improve the spatialization accuracy of GDP. POI can effectively reflect the spatial distribution of GDP and provide data support for GDP spatialization.

Key words: GDP spatialization    POI    Nighttime light    Land use data
收稿日期: 2019-11-12 出版日期: 2021-05-24
ZTFLH:  TP79  
基金资助: “十三五”国家重点研发专项(2018YFC1504603)
通讯作者: 潘耀忠     E-mail: zhangaihua55@126.com;pyz@bnu.edu.cn
作者简介: 张爱华(1995-),女,山东滨州人,硕士研究生,主要从事经济地理以及资源环境遥感方面的研究。E?mail:zhangaihua55@126.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
张爱华
潘耀忠
明艳芳
王金云

引用本文:

张爱华,潘耀忠,明艳芳,王金云. 多源信息耦合的GDP空间化研究—以北京市为例[J]. 遥感技术与应用, 2021, 36(2): 463-472.

Aihua Zhang,Yaozhong Pan,Yanfang Ming,Jinyun Wang. Research of GDP Spatialization based on Multi-source Information Coupling:A Case Study in Beijing. Remote Sensing Technology and Application, 2021, 36(2): 463-472.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.2.0463        http://www.rsta.ac.cn/CN/Y2021/V36/I2/463

图1  技术路线
类型相关系数类型相关系数类型相关系数
餐饮0.91大厦0.92服务区0.04
公安交警0.96火车站0.67机场0.09
加油站0.41金融服务0.98居民小区0.98
科研教育0.95旅游0.85汽车相关0.13
汽车站0.07收费站0.20停车场0.96
休闲娱乐0.92医疗机构0.94医院0.98
政府单位0.95住宿0.94购物0.91
表1  不同类别的POI核密度与GDP的相关系数表
图2  POI复合密度图
拟合模型调整后R2RE<30%30%≤RE<60%RE≥60%MRE (%)RMSE
灯光亮度模型0.846个6个4个12.600.56
POI核密度、灯光亮度模型0.9211个3个2个6.400.40
表2  两个模型精度比较
图3  两种模型模拟结果
图4  本研究结果
图5  资源环境科学与数据中心提供的GDP数据
图6  相同区域结果对比(单位:万元/km2)
区县

统计值

/万元

预测值

/万元

差值

/万元

RE

/%

区县

统计值

/万元

预测值

/万元

差值

/万元

RE

/%

昌平7 010 0007 079 66969 6690.99门头沟1 569 0001 579 21710 2170.65
朝阳50 005 00050 035 49430 4940.06密云2 274 0002 437 676163 6767.20
大兴17 777 00017 965 033188 0331.06平谷2 005 4742 184 136178 6628.91
东城20 617 98820 625 3977 4090.04石景山4 821 4124 819 0242 3880.05
房山5 807 2005 950 979143 7792.48顺义15 716 49815 914 216197 7181.26
丰台12 620 00012 636 30316 3030.13通州6 583 0006 746 343163 3432.48
海淀50 351 00050 368 56017 5600.03西城35 336 00035 328 3387 6620.02
怀柔2 420 0002 594 943174 9437.23延庆1 101 2291 175 31274 0836.73
表3  模拟结果和统计值的误差
1 Hu Yunfeng, Wang Qianqian, Liu Yue, et al. Index System and Transferring Methods to Build the National Society and Economy Grid Database[J]. Journal of Geo-information Science,2011,13(5):573-578.
1 胡云锋,王倩倩,刘越,等.国家尺度社会经济数据格网化原理和方法[J].地球信息科学学报,2011,13(5):573-578.
2 Lu Xiu, Li Jia, Duan Ping, et al. Spatial Difference of GDP in Yunnan Border Area based on Nighttime Light and Land Use Data[J]. Journal of Geo-information Science,2019,21(3):455-466.
2 卢秀,李佳,段平,等.基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析[J].地球信息科学学报,2019,21(3):455-466.
3 Yi Ling, Xiong Liya, Yang Xiaohuan. Method of Pixelizing GDP Data based on the GIS[J]. Journal of Gansu Sciences,2006(2):54-58.
3 易玲,熊利亚,杨小唤.基于GIS技术的GDP空间化处理方法[J].甘肃科学学报,2006(2):54-58.
4 Ma Jing, Jiao Wenxian. A Review on Pixelizing of Social Statistical Data[J]. Future and Development,2008,29(3):25-28.
4 马静,焦文献.我国社会统计数据空间化研究综述[J].未来与发展,2008,29(3):25-28.
5 Wilson S J, Steenhuisen F, Pacyna J M, et al. Mapping the Spatial Distribution of Global Anthropogenic Mercury Atmospheric Emission Inventories[J]. Atmospheric Environment,2006,40(24):4621-4632.
6 Elvidge C D, Baugh K E, Kihn E A, et al. Relation between Satel-lite Observed Visible-near Infrared Emissions, Population, Economic Activity and Electric Power Consumption[J]. International Journal of Remote Sensing,1997 18(6):1373-1379. doi:10.1080/014311697218485.
doi: 10.1080/014311697218485
7 Fu H Y, Shao Z F, Fu P, et al. The Dynamic Analysis between Urban Nighttime Economy and Urbanization Using the DMSP/OLS Nighttime Light Data in China from 1992 to 2012[J]. Remote Sensing,2017,9 (5). doi: 10.3390/rs9050416.
doi: 10.3390/rs9050416
8 Ma T, Zhou C H, Tao P, et al. Responses of Suomi-NPP VIIRS-derived Nighttime Lights to Socioeconomic Activity in China’s Cities[J]. Remote Sensing Letters,2014,5:165–174. doi: 10.1080/2150704X.2014.890758.
doi: 10.1080/2150704X.2014.890758
9 Bustos M F A, Hall O, Andersson M. Nighttime Lights and Population Changes in Europe 1992~2012[J]. Ambio, 2015, 7(44):653-665. doi: 10.1007/s13280-015-0646-8.
doi: 10.1007/s13280-015-0646-8
10 Zhou Yuke, Gao Xizhang, Ni Xiliang. Analyzing Regional Inequality of Socioeconomic Development in China with Nighttime Light[J].Remote Sensing Technology and Application,2017,32(6):1107-1113.
10 周玉科,高锡章,倪希亮.利用夜间灯光数据分析我国社会经济发展的区域不均衡特征[J].遥感技术与应用,2017,32(6):1107-1113.
11 Sutton P C, Costanza R. Global Estimates of Market and Non-market Values Derived from Nighttime Satellite Imagery, Land Cover, and Ecosystem Service Valuation[J]. Ecological Economics,2002,41(3): 509-527.
12 Doll C N H, Muller J P, Morley J G. Mapping Regional Economic Activity from Night Time Light Satellite Imagery[J]. Ecological Economics,2006,57:75–92.doi: 10.1016/j.ecolecon.2005.03.007.
doi: 10.1016/j.ecolecon.2005.03.007
13 Han Xiangdi, Zhou Yi, Wang Shixin, et al. GDP Spatialization in China based on DMSP/OLS Data and Land Use Data[J].Remote Sensing Technology and Application,2012,27(3):396-405.
13 韩向娣,周艺,王世新,等.基于夜间灯光和土地利用数据的GDP空间化[J].遥感技术与应用,2012,27(3):396-405.
14 Chen Q, Hou X Y, Zhang X C, et al. Improved GDP Spatialization Approach by Combining Land-use Data and Night-time Light Data: A Case Study in China’s Continental Coastal Area[J]. International Journal of Remote Sensing,2016,37(19):4610-4622.doi:10.1080/01431161.2016. 1217440.
doi: 10.1080/01431161.2016. 1217440
15 Xiao Guofeng, Zhu Xiufang, Cai Yi, et al. GDP Spatialization in Henan Province based on Multi-source Data[J]. Journal of Beijing Normal University (Natural Science),2018,54(2):232-238.
15 肖国峰,朱秀芳,蔡毅,等.基于多源数据的河南省GDP空间化[J].北京师范大学学报(自然科学版),2018,54(2):232-238.
16 Li X, Xu H M, Chen X L, et al. Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China[J]. Remote Sensing, 2013, 5(6):3057-3081. doi: 10.3390/rs5063057.
doi: 10.3390/rs5063057
17 Zhao M, Cheng W M, Zhou C H, et al. GDP Spatialization and Economic Differences in South China based on NPP-VIIRS Nighttime Light Imagery[J]. Remote Sensing, 2017.9.(7). doi: 10.3390/rs9070673.
doi: 10.3390/rs9070673
18 Zan Xiaoyu,Tan Xiaoyue,Li Qiang,et al. Recognition Model of Poverty Areas Combining Light Inten⁃sity and Patch Spatial Distribution Characteristics:A Case Study of Shanxi Province[J]. Remote Sensing Technology and Application,2020,35(6):1368-1376.
18 昝骁毓,谭晓悦,李强,等.融合灯光强度和斑块空间分布特征的贫困区域识别模型构建——以山西省为例[J].遥感技术与应用,2020,35(6):1368-1376.
19 Chen Shili, Chen Haohui, Li Xun. The Ability of Nighttime Imagery in Monitoring Economic Activity in Different Scales[J]. Scientia Geographica Sinica,2020,40(9):1476-1483.
19 陈世莉,陈浩辉,李郇.夜间灯光数据在不同尺度对社会经济活动的预测[J].地理科学,2020,40(9):1476-1483.
20 Xiao Min. Research on Correlation between Tourism POI and Regional Economy based on GWR[D]. Chengdu: Southwest Jiaotong University,2019.
20 肖敏.基于地理加权回归的旅游POI与区域经济相关性研究[D].成都:西南交通大学,2019.
21 Hu Y F, Han Y Q. Identification of Urban Functional Areas based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone[J]. Sustainability, 2019, 11(5). doi: 10.3390/su11051385.
doi: 10.3390/su11051385
22 Lou G, Chen Q X, He K, et al. Using Nighttime Light Data and POI Big Data to Detect the Urban Centers of Hangzhou[J]. Remote Sensing,2019,11(15). doi:10.3390/rs11151821.
doi: 10.3390/rs11151821
23 Shi X Y, Lü F S, Seng D W, et al. Visual Exploration of Mobility Dynamics based on Multi-source Mobility Datasets and POI Information[J]. Journal of Visualization, 2019, 22(6): 1209-1223.doi: 10.1007/s12650-019-00594-1.
doi: 10.1007/s12650-019-00594-1
24 Li K N, Chen Y H, Li Y. The Random Forest-based Method of Fine-resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data[J]. Remote Sensing.2018,10(10). doi: 10.3390/rs10101650.
doi: 10.3390/rs10101650
25 Wang L Y, Fan H, Wang Y K. Fine-resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data based on Similarity Matching[J]. Remote Sensing, 2019, 11(16). doi: 10.3390/rs11161900.
doi: 10.3390/rs11161900
26 Zhao Xin, Song Yingqiang, Liu Yilun, et al. Population Spatialization based on Satellite Remote Sensing and POI Data: Taking Guangzhou as an Example[J]. Tropical Geography,2020,40(1):101-109.
26 赵鑫,宋英强,刘轶伦,等.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.]
27 Zhai Jin, Zhang Xinchang, Huang Jianfeng, et al. A Gridding Method of Redistributing Population bases on POIs[J].Geography and Geo-Information Science, 2018,34(4): 83-89+124+2.
27 淳锦,张新长,黄健锋,等.基于POI数据的人口分布格网化方法研究[J].地理与地理信息科学,2018,34(4): 83-89,124,2.
28 Liu Zhenglian, Gui Zhipeng, Wu Huayi, et al. Fine-Scale Population Spatialization by Synthesizing Building Survey Data and Point of Interest Data[J]. Journal of Geomatics,2021,46:1-5.
doi: 10.14188/j.2095-6045.2019182
28 doi:10.14188/j.2095-6045.2019182.[刘正廉,桂志鹏,吴华意,等.融合建筑物与兴趣点数据的精细人口空间化研究[J].测绘地理信息,2021,46:1-5. doi:10.14188/j.2095-6045.2019182.]
doi: 10.14188/j.2095-6045.2019182
29 Yang Ni. Spatialization of Provincial GDP Statistics based on Land Use Data and DEM:Taking Guangxi Zhuang Autonomous Region as an Example[J]. Journal of Guangxi Teachers Education University: (Natural Science Edition), 2018, 54(2): 232- 238.
29 杨妮.基于土地利用数据及DEM的省域GDP统计数据空间化——以广西壮族自治区为[J].广西师范学院学报(自然科学版),2018,54(2):232-238.
30 Wang Shuang, Li Jiong. Analysis and Visualization of POI Distribution Density based on Urban Network Space[J].Urban Geotechnical Investigation & Surveying, 2015(1): 21-25.
30 王爽,李炯.基于城市网络空间的POI分布密度分析及可视化[J].城市勘测,2015(1):21-25.
31 Mohaymany A S, Matin M. GIS-based Method for Detecting High-crash-risk Road Segments Using Network Kernel Density Estimation[J]. Geo-spatial Information Science, 2013(6):113-119. doi: 10.1080/10095020.2013.766396.
doi: 10.1080/10095020.2013.766396
32 Okabe A, Satoh T, Sugihara K. A Kernel Density Estimation Method for Network, Its Computational Method and a GIS-Based Tool[J]. International Journal of Geographical Information Science.2009,23(1):7–32. doi:10.1080/1365881080 2475491.
doi: 10.1080/1365881080 2475491
33 Zhao Lu,Zhao Zuoquan.Optimization of Beijing Industrial Spa-ce Circle Structure and Layout[J]. Research on Development,2017(2):46-52,174-177.
33 赵璐,赵作权.北京市产业空间圈层结构与布局优化[J].开发研究,2017(2):46-52,174-177.
[1] 陈妮,应丰,王静,李健. 基于U-Net的高分辨率遥感图像土地利用信息提取[J]. 遥感技术与应用, 2021, 36(2): 285-292.
[2] 付甜梦,张丽,陈博伟,闫敏. 基于GEE平台的海岛地表覆盖提取及变化监测—以苏拉威西岛为例[J]. 遥感技术与应用, 2021, 36(1): 55-64.
[3] 郑琪,邸苏闯,潘兴瑶,刘洪禄,朱永华,张岑,周星. 基于Rapid Eye数据的北京生态涵养区土地利用分类及变化研究[J]. 遥感技术与应用, 2020, 35(5): 1118-1126.
[4] 程雨婷,刘昭华,鹿琳琳,刘士彪,李庆亭. 一带一路沿海超大城市热岛时空特征遥感分析[J]. 遥感技术与应用, 2020, 35(5): 1197-1205.
[5] 郭梦辉,季亚南,柯樱海,陈少辉. 土地利用变化下北京市热通量的时空演变[J]. 遥感技术与应用, 2020, 35(5): 1218-1225.
[6] 李智礼,匡文慧,张澍. 近70 a天津主城区城市土地利用/覆盖变化遥感监测与时空分析[J]. 遥感技术与应用, 2020, 35(3): 527-536.
[7] 陈馨,匡文慧. 基于云平台的中哈干旱区典型城市地表覆盖变化遥感监测与比较[J]. 遥感技术与应用, 2020, 35(3): 548-557.
[8] 赵婷,白红英,邓晨晖,孟清,郭少壮,齐贵增. 基于DEM的秦岭陕西段地表面积与垂直投影面积差异性分析[J]. 遥感技术与应用, 2020, 35(2): 399-405.
[9] 刘亲亲, 崔耀平, 刘素洁, 李楠. 中国不同土地利用类型分光辐射地表反照率研究[J]. 遥感技术与应用, 2019, 34(1): 46-56.
[10] 谷晓天, 高小红, 马慧娟, 史飞飞, 刘雪梅, 曹晓敏. 复杂地形区土地利用/土地覆被分类机器学习方法比较研究[J]. 遥感技术与应用, 2019, 34(1): 57-67.
[11] 李洁, 王福红, 宋晓谕, 石培基, 赵锐峰. 干旱区流域土地利用覆被空间转型模拟及热点探测—以黑河流域中游为例[J]. 遥感技术与应用, 2019, 34(1): 187-196.
[12] 白贺庭, 马明国, 阎然, 刘康甯, 隽楚涵. 基于夜间灯光数据的重庆市城市扩张研究[J]. 遥感技术与应用, 2019, 34(1): 216-224.
[13] 胡云锋,商令杰,张千力,王召海. 基于GEE平台的1990年以来北京市土地变化格局及驱动机制分析[J]. 遥感技术与应用, 2018, 33(4): 573-583.
[14] 赵梦雨,薛亮. 咸阳市生境质量变化遥感监测研究[J]. 遥感技术与应用, 2017, 32(6): 1171-1180.
[15] 陈洋波,张涛,窦鹏,董礼明,陈华. 基于SVM的东莞市土地利用/覆被自动分类误差来源与后处理[J]. 遥感技术与应用, 2017, 32(5): 893-903.