Identification of urban shrinkage using NPP-VIIRS nighttime light data at the county level in China

Cities ◽  
2021 ◽  
Vol 118 ◽  
pp. 103373
Author(s):  
Ying Zhou ◽  
Chenggu Li ◽  
Wensheng Zheng ◽  
Yuefang Rong ◽  
Wei Liu
Keyword(s):  
County Level ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate

2019 ◽  
Vol 8 (9) ◽  
pp. 419 ◽  
Author(s):  
Xiaole Ji ◽  
Xinze Li ◽  
Yaqian He ◽  
Xiaolong Liu
Keyword(s):  
Growth Rate ◽  
Remotely Sensed ◽  
Gdp Growth ◽  
County Level ◽  
Remotely Sensed Data ◽  
Simple Method ◽  
Gdp Growth Rate ◽  
Nighttime Light Data ◽  
Economic Statistics ◽  
Nighttime Light

County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions.

scholarly journals Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China

2021 ◽  
Vol 13 (5) ◽  
pp. 2930
Author(s):  
Pengfei Ban ◽  
Wei Zhan ◽  
Qifeng Yuan ◽  
Xiaojian Li
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
City Size ◽  
Border City ◽  
Point Of Interest ◽  
Nighttime Light Data ◽  
Profile Line ◽  
Point Analysis ◽  
Nighttime Light ◽  
Single Data

Cities defined mainly from the administrative aspect can create impact and problems especially in the case of China. However, only a few researchers from China have attempted to identify urban areas from the morphology dimension. In addition, previous studies have been mostly based on the national and regional scales or a single prefecture city and have completely ignored cross-boundary cities. Defining urban areas on the basis of a single data type also has limitations. To address these problems, this study integrates point of interest and nighttime light data, applies the breaking point analysis method to determine the physical geographic scope of the Guangzhou–Foshan cross-border city, and then compares this city with Beijing and Shanghai. Results show that Guangzhou–Foshan comprises one core urban area and six suburban counties, among which the core urban area extends across the administrative boundaries of Guangzhou and Foshan. The urban area and average urban radius of Guangzhou–Foshan are larger than those of Beijing and Shanghai, and this finding contradicts the city size measurements based on the administrative division system of China and those published on traditional official statistical yearbooks. In terms of urban density value, Shanghai has the steepest profile followed by Guangzhou–Foshan and Beijing, and the profile line of Guangzhou–Foshan has a bimodal shape.

DEVELOPMENT OF A METHOD OF ESTIMATION OF TOTAL FLOOR AREA USING DEEP LEARNING BASED ON NIGHTTIME-LIGHT DATA

2020 ◽  
Vol 76 (6) ◽  
pp. II_1-II_7
Author(s):  
Ryusei SAITO ◽  
Chizuko HIRAI ◽  
Chihiro HAGA ◽  
Takanori MATSUI ◽  
Hiroaki SHIRAKAWA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Floor Area ◽  
Nighttime Light Data ◽  
Nighttime Light
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