Please wait a minute...
Advanced Search
数据分析与知识发现  2019, Vol. 3 Issue (9): 36-44     https://doi.org/10.11925/infotech.2096-3467.2018.1473
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于夜间灯光亮度的OpenStreetMap数据完整性检验 *
刘菲1,成晓强2,4(),吴华意1,3
1 武汉大学测绘遥感信息工程国家重点实验室 武汉 430079
2 湖北大学资源环境学院 武汉 430062
3 地球空间信息技术协同创新中心 武汉 430079
4 区域开发与环境响应湖北省重点实验室 武汉 430062
Assessing Data Integrity of OpenStreetMap Based on Night Lights
Fei Liu1,Xiaoqiang Cheng2,4(),Huayi Wu1,3
1 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2 Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
3 Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
4 Key Laboratory of Regional Development and Environmental Response, Wuhan 430062, China
全文: PDF (1237 KB)   HTML ( 7
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】解决OpenStreetMap (OSM)数据完整性评价中参考数据集难获取、更新慢等问题。【方法】引入夜光遥感影像作为新的参考数据集, 以综合竞争力较强的城市作为样本, 研究夜间灯光亮度与OSM数据完整性之间的相关关系, 探究中国OSM数据的质量分布规律。【结果】建立OSM建筑物密度和夜间灯光亮度的回归模型, 相关系数为0.8522。中国约84.2%的城市OSM建筑物密度实际值与预测值相近, 差异小于0.5%; 东莞、厦门 等城市的实际值偏低, 差异百分比分布在2%~7%范围内, 数据完整性差。【局限】该模型的可扩展性有待提升。【结论】两个数据集的融合实现了低成本、大规模、多尺度的OSM数据完整性评估, 反映了中国部分“空城”、“鬼城”的分布。

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
刘菲
成晓强
吴华意
关键词 夜光遥感OSM完整性相关性    
Abstract

[Objective] This paper aims to address data integrity issues facing the OpenStreetMap (OSM) datasets. [Methods] First, we retrieved the remote censor images of night-lighting brightness as an indicator for cities with strong comprehensive competitiveness. Then, we studied the correlation between night-lighting brightness and OSM completeness, which identified the distribution patterns of high quality data. [Results] We established a regression model for OSM building density and night-lighting brightness. The correlation coefficient was 0.8522. We also found that 84.2% of Chinese cities in our study had building densities closed to the predicted values (the discrepancy was less than 0.5%). The building densities in the other cities were 2% to 7% lower than the expected values. [Limitations] More research is needed to evaluate the performance of this model with other cities. [Conclusions] The remote sensing images help us assess quality of OSM data, which also identifies the “ghost or empty cities”.

Key wordsNighttime Remote Sensing    OSM    Completeness    Correlation
收稿日期: 2018-12-27      出版日期: 2019-10-23
ZTFLH:  P285.2 G35  
基金资助:*本文系国家重点研发计划项目“城市群经济区域建设与管理空间信息重点服务及应用示范”(项目编号: 2017YFB0503802);国家自然科学基金项目“基于视觉认知的网络混搭地图易读性评价与优化方法研究”(项目编号: 41501443);区域开发与环境响应湖北省重点实验室开放基金项目“资源环境数据在线搜索与整合关键技术研究”(项目编号: 2017(B)002)
引用本文:   
刘菲,成晓强,吴华意. 基于夜间灯光亮度的OpenStreetMap数据完整性检验 *[J]. 数据分析与知识发现, 2019, 3(9): 36-44.
Fei Liu,Xiaoqiang Cheng,Huayi Wu. Assessing Data Integrity of OpenStreetMap Based on Night Lights. Data Analysis and Knowledge Discovery, 2019, 3(9): 36-44.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1473      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I9/36
  技术流程
  西安建筑物分布
  h=500西安核密度分析结果
  h=1 000西安核密度分析结果
  h=1 500西安核密度分析结果
  h=2 000西安核密度分析结果
  西安夜光遥感影像
  OSM建筑物核密度随夜间灯光亮度变化趋势(横坐标并未完全显示)
  剩余样本变化趋势(横坐标并未完全显示)
  建筑物核密度与夜间灯光亮度散点图
  R2与斜率k随样本量变化折线图
  最优样本的建筑物核密度与夜间灯光散点图
差异(%) 等级描述 城市数目
[-7.0, -2.0) 实际值偏低 4
[-2.0, -1.0) 实际值略低 10
[-1.0, -0.5) 实际值略低 16
[-0.5, 0.5) 实际值与预测值近似 308
[0.5, 1.0) 实际值略高 6
[1.0, 3.0) 实际值略高 10
[3.0, 10) 实际值偏高 9
[10, 100) 实际值极高 3
  实际值与预测值的差异分布表
  建筑物核密度实际值与预测值的差异分布图
[1] Rambaldi G, Kyem P A K, McCall M , et al. Participatory Spatial Information Management and Communication in Developing Countries[J]. The Electronic Journal of Information Systems in Developing Countries, 2006,25(1):1-9.
[2] Goodchild M F . Citizens as Sensors: The World of Volunteered Geography[J]. GeoJournal, 2007,69(4):211-221.
doi: 10.1007/s10708-007-9111-y
[3] Goodchild M F . Geographic Information Systems and Science: Today and Tomorrow[J]. Annals of GIS, 2009,15(1):3-9.
[4] Fritz S, McCallum I, Schill C , et al. Geo-Wiki. Org: The Use of Crowdsourcing to Improve Global Land Cover[J]. Remote Sensing, 2009,1(3):345-354.
[5] Goodchild M F, Glennon J A . Crowdsourcing Geographic Information for Disaster Response: A Research Frontier[J]. International Journal of Digital Earth, 2010,3(3):231-241.
[6] 罗路长, 刘波, 刘雪朝 . OpenStreetMap路网数据质量评价及应用分析[J]. 江西科学, 2017,35(1):151-157.
[6] ( Luo Luchang, Liu Bo, Liu Xuechao . Data Quality Assessment and Application Analysis for OpenStreetMap Road Network[J]. Jiangxi Science, 2017,35(1):151-157.)
[7] Gröchenig S, Brunauer R, Rehrl K . Estimating Completeness of VGI Datasets by Analyzing Community Activity over Time Periods[A]// Huerta J, Schade S, Granell C. Connecting a Digital Europe Through Location and Place[M]. Springer, 2014: 3-18.
[8] 马超, 孙群, 徐青 . 一种基于参考数据的志愿者地理信息质量评价方法[J]. 测绘与空间地理信息, 2017,40(3):1-5.
[8] ( Ma Chao, Sun Qun, Xu Qing . An Approach for Volunteered Geographic Information Quality Evaluation Based on Reference Data[J]. Geomatics & Spatial Information Technology, 2017,40(3):1-5.)
[9] 朱富晓, 王艳慧 . 多层次多粒度 OSM 路网目标数据质量综合评估方法研究[J]. 地球信息科学学报, 2017,19(11):1422-1432.
[9] ( Zhu Fuxiao, Wang Yanhui . On the Comprehensive Evaluation of the Data Quality for OSM Road Network from the Perspectives of Multi-level and Multi-granularity[J]. Journal of Geo-Information Science, 2017,19(11):1422-1432.)
[10] 王明, 李清泉, 胡庆武 , 等. 面向众源开放街道地图空间数据的质量评价方法[J]. 武汉大学学报: 信息科学版, 2013,38(12):1490-1494.
[10] ( Wang Ming, Li Qingquan, Hu Qingwu , et al. Quality Analysis on Crowd Sourcing Geographic Data with Open Street Map Data[J]. Geomatics and Information Science of Wuhan University, 2013,38(12):1490-1494.)
[11] Ludwig I, Voss A, Krause-Traudes M . A Comparison of the Street Networks of Navteq and OSM in Germany[A]// Geertman S, Reinhardt W, Toppen F. Advancing Geoinformation Science for a Changing World[M]. Springer, 2011: 65-84.
[12] Haklay M . How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets[J]. Environment and Planning B: Planning and Design, 2010,37(4):682-703.
doi: 10.1016/j.amjcard.2004.02.006
[13] Kounadi O . Assessing the Quality of OpenStreetMap Data [D]. London: University College of London Department of Civil, Environmental and Geomatic Engineering. 2009.
[14] Zheng S, Zheng J . Assessing the Completeness and Positional Accuracy of OpenStreetMap in China[A]// Bandrova T, Konecny M, Zlatanova S. Thematic Cartography for the Society[M]. Springer, 2014: 171-189.
[15] 李德仁, 李熙 . 夜光遥感技术在评估经济社会发展中的应用——兼论其对“一带一路”建设质量的保障[J]. 宏观质量研究, 2015,3(4):1-8.
[15] ( Li Deren, Li Xi . Applications of Night-time Light Remote Sensing in Evaluating of Socioeconomic Development[J]. Journal of Macro-quality Research, 2015,3(4):1-8.)
[16] Elvidge C D, Tuttle B T, Sutton P C , et al. Global Distribution and Density of Constructed Impervious Surfaces[J]. Sensors, 2007,7(9):1962-1979.
[17] 郑辉, 曾燕, 王勇 , 等. 基于VIIRS夜间灯光数据的城市建筑密度估算——以南京主城区为例[J]. 科学技术与工程, 2014,14(18):68-75.
[17] ( Zheng Hui, Zeng Yan, Wang Yong , et al. Urban Building Density Estimation Based on the VIIRS Night-time Satellite Data—A Case of Nanjing[J]. Science Technology and Engineering, 2014,14(18):68-75.)
[18] Zhou Q . Exploring the Relationship Between Density and Completeness of Urban Building Data in OpenStreetMap for Quality Estimation[J]. International Journal of Geographical Information Science, 2018,32(2):257-281.
[19] Silverman B W . Density Estimation for Statistics and Data Analysis[M]. New York: Routledge, 1986.
[20] Jalobeanu A, Blanc-Féraud L, Zerubia J . Satellite Image Deblurring Using Complex Wavelet Packets[J]. International Journal of Computer Vision, 2003,51(3):205-217.
doi: 10.1023/A:1021801918603
[21] Jalobeanu A, Zerubia J, Blanc-Féraud L. Bayesian Estimation of Blur and Noise in Remote Sensing Imaging[A]// Campisi P, Egiazarian K. Blind Image Deconvolution: Theory and Applications[M]. CRC Press, 2007: 239-275.
[22] Zhang Y, Man Y . Satellite Image Adaptive Restoration Using Periodic Plus Smooth Image Decomposition and Complex Wavelet Packet Transforms[J]. Tsinghua Science and Technology, 2012,17(3):337-343.
[23] 李亚平, 蔡忠亮, 谢彩云 , 等. 一种开放式地理空间数据可用性评价方法的研究[J]. 测绘地理信息, 2017,42(1):83-87.
[23] ( Li Yaping, Cai Zhongliang, Xie Caiyun , et al. A Case Study in Usability Evaluation Method of Open Geospatial Data[J]. Journal of Geomatics, 2017,42(1):83-87.)
[1] 桂思思,张晓娟,王鑫. 查询歧义性程度自动标注指标的替代性 验证研究*[J]. 数据分析与知识发现, 2019, 3(2): 79-89.
[2] 王欣瑞,何跃. 社交媒体用户交互行为与股票市场的关联分析研究: 基于新浪财经博客的实证[J]. 数据分析与知识发现, 2019, 3(11): 108-119.
[3] 吴朋民, 陈挺, 王小梅. Altmetrics与引文指标相关性研究[J]. 数据分析与知识发现, 2018, 2(6): 58-69.
[4] 张肃. 中国城镇居民信息消费的空间相关性与影响因素分析*——基于动态空间杜宾面板模型的实证研究[J]. 数据分析与知识发现, 2017, 1(5): 52-61.
[5] 孙赫,李淑琴,吕学强,刘克会. 微博城市投诉文本中地理位置实体的完整性研究*[J]. 现代图书情报技术, 2016, 32(3): 58-66.
[6] 余凡, 楼雯. 领域概念的三层递进筛选方法研究[J]. 现代图书情报技术, 2015, 31(4): 26-33.
[7] 乔建忠. 一种基于统计特征面向“类型”主题抓取的网页相关性判断策略研究[J]. 现代图书情报技术, 2012, 28(6): 9-16.
[8] 徐树维. 同步协作检索结果的相关性判断策略[J]. 现代图书情报技术, 2012, 28(4): 41-47.
[9] 成颖. 基于相关性判据的学术信息检索系统成功模型建构[J]. 现代图书情报技术, 2011, 27(9): 46-53.
[10] 成颖. 基于相关性判据的学术信息检索系统成功模型实证分析[J]. 现代图书情报技术, 2011, 27(10): 45-53.
[11] 王军辉, 胡铁军, 李丹亚. 相关文献检索研究综述[J]. 现代图书情报技术, 2011, 27(1): 39-45.
[12] 冯平, 黄名选. 特征词抽取和相关性融合的伪相关反馈查询扩展[J]. 现代图书情报技术, 2011, 27(1): 52-56.
[13] 刘兰,吴振新,张智雄,徐麒. Web Archive的采集策略研究*[J]. 现代图书情报技术, 2009, 3(1): 10-15.
[14] 余希田,万莉莉,胡铁军,李丹亚. 基于向量空间模型的文献相关性数据库的研究与实现*[J]. 现代图书情报技术, 2008, 24(6): 61-66.
[15] 徐德智,王庆涛,王斌 . 基于本体的Web信息采集*[J]. 现代图书情报技术, 2007, 2(2): 53-55.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn