Abstract
Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of ) and SVMR (mean of ) are superior to MLR (mean of ) and LWR (mean of ) for model calibration; only PLSR (mean of , mean of ) holds a strong transferability among different years; the frequency of three DN sections of (0–1), (4–16), and (57–63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years.
Highlights
Regional and global economic data are important indicators of the assessment of human societal development, and most countries conduct economic censuses every year for the evaluation of national economy strength [1]
The objective of this study was to explore the possibility of establishing a transferable economy estimation model across multiple years with different data-mining methods, based on the DMSP/OLS
R2 cv for different data mining methods: (a) Multiple Linear Regression (MLR); (b) Local Weighted Regression (LWR); LWR; (c)
Summary
Regional and global economic data are important indicators of the assessment of human societal development, and most countries conduct economic censuses every year for the evaluation of national economy strength [1]. The economic census always requires a long period of time with low efficiency and high costs, which is especially serious in developing countries with weak government statistical infrastructure [2] These problems have hindered the understanding of the real status of the economy. With the rapid development of science and technology, remote sensing has gradually gained attention, which is an efficient approach for the observation of earth on a global scale based on the optical images from the satellite sensors in the outer space It can provide us with a real-time mirror of the human activities and the socioeconomic status, making it relatively cheaper and far more efficient than the traditional economic census.
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