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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (9): 36-44    DOI: 10.11925/infotech.2096-3467.2018.1473
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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
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[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
ZTFLH:  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.

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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
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