Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement

ABSTRACT

The population distribution grid at fine scales better reflects the distribution of residents and plays an important role in investigating urban systems. The recent years have witnessed a growing trend of applying the nighttime light data to the estimation of population at micro levels. However, using the nighttime light data alone to estimate population may cause the overestimation problem due to excessively high light radiance in specific types of areas such as commercial zones and transportation hubs. In dealing with this issue, this study used taxi trajectory data that delineate people’s movements, and explored the utility of integrating the nighttime light and taxi trajectory data in the estimation of population in Shanghai at the spatial resolution of 500 m. First, the initial population distribution grid was generated based on the NPP-VIIRS nighttime light data. Then, a calibration grid was created with taxi trajectory data, whereby the initial population grid was optimized. The accuracy of the resultant population grid was assessed by comparing it with the refined survey data. The result indicates that the final population distribution grid performed better than the initial population grid, which reflects the effectiveness of the proposed calibration process.

KEYWORDS:

• Population
• nighttime light data
• social sensing data
• taxi trajectory data
• NPP-VIIRS

Acknowledgments

We are grateful to Prof. May Yuan, Dr. Shawn Laffan and the three anonymous referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key R&D Program of China [2017YFE0100700]; the National Natural Science Foundation of China [41871331, 41701462]; the Major Program of National Social Science Foundation of China [17ZDA068]

Notes on contributors

Bailang Yu

Bailang Yu received the B.S. and Ph.D. degrees in cartography and geographic information systems from East China Normal University, Shanghai, China, in 2002 and 2009, respectively. He is currently a Professor with the Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, where he is also with the School of Geographic Sciences. His research interests include urban remote sensing, nighttime light remote sensing, LiDAR, and object-based methods.

Ting Lian

Ting Lian is a postgraduate in cartography and geographic information systems at East China Normal University and plans to work on Internet product planning in the future. Her research interests are population estimation and ubiquitous computing.

Yixiu Huang

Yixiu Huang received a master degree in Geographic Information Systems from East China Normal University. As of now, he is working as a software development engineer in SonicWALL Shanghai R&D Center.

Shenjun Yao

Shenjun Yao is a postdoc researcher at East China Normal University. Her research interests focus on how the geographical information science and technology can be applied to the transportation and public health, as well as application of social sensing geodata.

Xinyue Ye

Xinyue Ye is a professor of Kent State University, specializing in GIS and cartography. His research interests are urban crime analysis, urban expansion, and spatio-temporal information mining.

Zuoqi Chen

Zuoqi Chen received the PhD degree from East China Normal University, Shanghai, China, in 2017. Currently, he is a postdoctoral fellow with the Key Laboratories of Geographic Information Science (Ministry of Education) and School of Geographic Sciences, East China Normal University, Shanghai, China. His interested research fields contain urban remote sensing, nighttime light remote sensing, and development of GIS.

Chengshu Yang

Chengshu Yang received the B.S. degree in marine technology from Shanghai Ocean University, China, in 2013. Currently, he is a Ph.D. candidate at East China Normal University. His research area mainly focus on the nighttime light remote sensing and its application in urban research.

Jianping Wu

Jianping Wu received the M.S. degree from Peking University, Beijing, China, in 1986, and the Ph.D. degree from East China Normal University, Shanghai, China, in 1996. He is currently a Professor with East China Normal University. His research interests include remote sensing and geographic information system.