Construction of regional economic development model based on satellite remote sensing technology
Hailing GU1, Chao CHEN1(), Ying LU1, Yanli CHU2
1. College of Marine Science and Technology, Zhejiang Ocean University, Zhoushan 316022, China 2. School of Economics and Management, Zhejiang Ocean University, Zhoushan 316022, China
In order to break through the time-consuming and laborious limitations of traditional regional economic development surveys, the authors built 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, the authors obtained surface morphological changes and land use information, analyzed the correlation between land use types and regional economic indicators, optimized sensitive factors, then combined the social survey data to build a regional economic development model and finally performed an accuracy evaluation to verify the validity and applicability of the model. Zhoushan Islands were selected as the research area to carry out verification experiments. The experimental results show that the construction land area is the most sensitive factor related to various economic indicators, and the correlation coefficients with GDP, PIP, SIP and TIP are respectively 0.959 1, 0.939 0, 0.954 6 and 0.957 3. The average determination coefficient R2 of the regional economic development model built with the survey data is 0.979 5. The results obtained by the authors provide a new way of thinking for regional economic development prediction and economic data correction and also provide 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, and thus is of great significance for understanding regional economic development as well as adjusting and correcting statistical data.
古海玲, 陈超, 芦莹, 褚衍丽. 基于卫星遥感技术的区域经济发展模型构建[J]. 国土资源遥感, 2020, 32(2): 226-232.
Hailing GU, Chao CHEN, Ying LU, Yanli CHU. Construction of regional economic development model based on satellite remote sensing technology. Remote Sensing for Land & Resources, 2020, 32(2): 226-232.
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