Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education

Bibliographic Data

ID22032067
AuthorsYue Han (0000-0002-5181-9230, Shandong University of Science and Technology), Lin Liu (0000-0002-7202-3418, Shandong University of Science and Technology, corresponding author), Qiaoli Sui (Shandong University of Science and Technology), Jiaxing Zhou (0000-0002-4193-1931, Shandong University of Science and Technology)
Year2021
Volume10
Issue12
Pages837
Publication date2021-12-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi10120837
OpenAlexW4200550743
LanguageEN
Citations received1
References cited25

There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is a significant positive correlation between AEY and GDP/C in provinces of China. Furthermore, from the perspective of spatial distribution and geostatistics, this research analyzes the correlation between AEY and the distribution of poor counties, revealing the inherent connection between education and poverty. Based on the data processing of nighttime light remote sensing images, this research adopts the machine learning method of random forest to extract the distribution status of spatio-temporal sequences for poor counties. Through the analysis, it is found that poor counties are characterized by centralized distribution and spatial autocorrelation spatially, and the number of poor counties decreases year by year in temporal evolution. On this basis, we analyze the correlation between education levels and the distribution of poor counties. It is found that, on the spatial scale, AEY in poor counties is relatively low, while AEY in non-poor counties is relatively high, showing a significant negative correlation between the two. On the temporal scale, the number of poor counties gradually decreased from 2000 to 2010, and at the same time, the education levels of poor counties also gradually improved. Finally, from the perspective of improving education levels to promote poverty elimination, we analyze the main factors affecting education using Principal Component Analysis (PCA) and other methods and obtain a regression model. This research proposes the Linear and Residual Integration Model (LRIM) to more accurately predict AEY in each province in 2020 based on historical data, and identifies the regions with low AEY as key regions for targeted poverty alleviation through education (TPAE) in the future. This research provides a decision-making basis to achieve TPAE means, helping to achieve the victory of the national education poverty elimination battle

Cartography · China · Correlation · Economic growth · Economics · Geography · Geostatistics · Gross domestic product · Per capita · Poverty · Regional science · Socioeconomics · Sociology · Spatial analysis · Spatial variability · Statistics · Advanced Technologies in Various Fields · Demography · Energy, Environment, Economic Growth · Mathematics

Tools

Open DOIOpen Access