Construction of regional economic development model based on remote sensing data

2019 2nd International Conference of Green Buildings and Environmental Management

生态环境科学

Gu, Hailing^1 ; Chen, Chao^1 ; Lu, Ying^1 ; Chu, Yanli^2 ; Ma, Yuxiang^1

Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, China^1

School of Port and Transportation Engineering, Zhejiang Ocean University, Zhoushan, China^2

关键词: Coefficient of determination;    Correlation coefficient;    Economic activities;    Morphological changes;    Regional economic development;    Remote sensing data;    Remote sensing technology;    Satellite remote sensing data;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/310/5/052060/pdf
DOI  :  10.1088/1755-1315/310/5/052060

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

In order to break through the time-consuming and laborious limitations of traditional regional economic development surveys, this study will build some regional economic development models by virtue of the advantages of remote sensing technology. First, based on multi-source and multi-temporal satellite remote sensing data, obtain surface morphological changes and land use information; second, analyze the correlation between land use types and regional economic indicators, and optimize sensitive factors; third, combine the social survey data to build a regional economic development model; finally, an accuracy evaluation is performed to verify the validity and applicability of the model. Zhoushan City, China, is selected as the research area to carry out verification experiments. The experimental results show that the construction land area is the most sensitive factor relating to various economic indicators, and the correlation coefficients with GDP, PPI, PSI and PTI are respectively 0.9591, 0.9390, 0.9546 and 0.9573. The coefficient of determination R2 of the regional economic development model built with the survey data is 0.9884. This study provides a new way of thinking for regional economic development prediction and economic data correction as well as a possibility for humans to observe economic activities and their impact. The model built in this study is simple and clear yet with high precision. It is of great significance for understanding regional economic development, adjusting and correcting statistical data.