Improving Nighttime Light Imagery With Location-Based Social Media Data
| Autor: | Ying Liu, Guofeng Cao, Eric L. Samson, Wei Zhang, Yong Chen, Naizhuo Zhao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Visible Infrared Imaging Radiometer Suite
education.field_of_study Brightness Pixel Population Aggregate (data warehouse) 0211 other engineering and technologies 02 engineering and technology Upsampling General Earth and Planetary Sciences Social media Electrical and Electronic Engineering education Image resolution 021101 geological & geomatics engineering Remote sensing |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 57:2161-2172 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2018.2871788 |
| Popis: | Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors. |
| Databáze: | OpenAIRE |
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