Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (9): 36-44    DOI: 10.11925/infotech.2096-3467.2018.1473
Current Issue | Archive | Adv Search |
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
Download: PDF(1237 KB)   HTML ( 6
Export: BibTeX | EndNote (RIS)      

[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     
Received: 27 December 2018      Published: 23 October 2019
:  P285.2 G35  

Cite this article:

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.

URL:     OR

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
[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] Ming Yi,Tingting Zhang. Ranking Answer Quality of Popular Q&A Community[J]. 数据分析与知识发现, 2019, 3(6): 12-20.
[2] Jiang Wu,Yinghui Zhao,Jiahui Gao. Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents[J]. 数据分析与知识发现, 2019, 3(4): 53-62.
[3] Sisi Gui,Xiaojuan Zhang,Xin Wang. Automatically Rating Query Ambiguity with Alt-Metrics[J]. 数据分析与知识发现, 2019, 3(2): 79-89.
[4] Daoping Wang,Zhongyang Jiang,Boqing Zhang. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[5] Pengmin Wu,Ting Chen,Xiaomei Wang. The Correlation Between Altmetrics and Citations[J]. 数据分析与知识发现, 2018, 2(6): 58-69.
[6] Jingqi Wang,Rui Li,Huayi Wu. The Evolution of Online Public Opinion Based on Spatial Autocorrelation[J]. 数据分析与知识发现, 2018, 2(2): 64-73.
[7] Dong Li,Shouchuan Tong,Jiang Li. Analyzing Interdisciplinarity and Scientists’ Academic Impacts[J]. 数据分析与知识发现, 2018, 2(12): 1-11.
[8] Yue He,Can Zhu. Sentiment Analysis of Weibo Opinion Leaders——Case Study of “Illegal Vaccine” Event[J]. 数据分析与知识发现, 2017, 1(9): 65-73.
[9] Su Zhang. Information Consumption of Urban Chinese Residents: An Empirical Study Based on Dynamic Spatial Durbin Panel Model[J]. 数据分析与知识发现, 2017, 1(5): 52-61.
[10] Xing Wei,Dehua Hu,Minhan Yi,Qizhen Zhu,Wenjie Zhu. Extracting Disease-Gene-Drug Correlations Based on Data Cube[J]. 数据分析与知识发现, 2017, 1(10): 94-104.
[11] Yu Liping. New Method to Evaluate Academic Journals: Case Study of Mathematics Journals[J]. 现代图书情报技术, 2016, 32(7-8): 94-100.
[12] Xiao Xuebin,Chai Yanju. Properties of Scholarly Papers and Number of Citations[J]. 现代图书情报技术, 2016, 32(6): 46-53.
[13] Zhu Ling,Xue Chunxiang,Zhang Chengzhi,Fu Zhu. User Tags and Microblog Posts: Case Study of Sina Weibo[J]. 现代图书情报技术, 2016, 32(3): 18-24.
[14] Duan Yufeng, Zhu Wenjing, Chen Qiao, Liu Wei, Liu Fenghong. A Domain Concepts Triple-layer Filter Method[J]. 现代图书情报技术, 2015, 31(4): 26-33.
[15] Li Junfeng, Lv Xueqiang, Zhou Shaojun. Patent Keyword Indexing Based on Weighted Complex Graph Model[J]. 现代图书情报技术, 2015, 31(3): 26-32.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938