Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (<i>N...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Debao Yuan, Huinan Jiang, Wei Guo, Ximin Cui, Ling Wu, Ziruo Wu, Hongsen Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	VIIRS-DNB night lights conditional constraint <i>VHNI</i> linear regression Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ca83341781c14346b80e320c8bf5757b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ca83341781c14346b80e320c8bf5757b
record_format	dspace
spelling	oai:doaj.org-article:ca83341781c14346b80e320c8bf5757b2021-11-25T18:57:27ZRegression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints10.3390/s212275611424-8220https://doaj.org/article/ca83341781c14346b80e320c8bf5757b2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7561https://doaj.org/toc/1424-8220Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (<i>NTL</i>) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original <i>NTL</i>, improved impervious surface index (<i>IISI</i>) and vegetation highlights nighttime-light index (<i>VHNI</i>). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, <i>VHNI</i> performed best with the value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> at 0.8632. For the employed population and power consumption regression with these three indices, the maximum <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> of <i>VHNI</i> are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the <i>VHNI</i> perform better than <i>NTL</i> and <i>IISI</i> in GDP regression, too. When taking employment population as the regression object, <i>VHNI</i> performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of <i>VHNI</i> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> is better than <i>NTL</i> and <i>IISI</i> in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between <i>VHNI</i> and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, <i>VHNI</i> index can be used for fitting analysis and prediction of economy and power consumption in the future.Debao YuanHuinan JiangWei GuoXimin CuiLing WuZiruo WuHongsen WangMDPI AGarticleVIIRS-DNBnight lightsconditional constraint<i>VHNI</i>linear regressionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7561, p 7561 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	VIIRS-DNB night lights conditional constraint <i>VHNI</i> linear regression Chemical technology TP1-1185
spellingShingle	VIIRS-DNB night lights conditional constraint <i>VHNI</i> linear regression Chemical technology TP1-1185 Debao Yuan Huinan Jiang Wei Guo Ximin Cui Ling Wu Ziruo Wu Hongsen Wang Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints
description	Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (<i>NTL</i>) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original <i>NTL</i>, improved impervious surface index (<i>IISI</i>) and vegetation highlights nighttime-light index (<i>VHNI</i>). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, <i>VHNI</i> performed best with the value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> at 0.8632. For the employed population and power consumption regression with these three indices, the maximum <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> of <i>VHNI</i> are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the <i>VHNI</i> perform better than <i>NTL</i> and <i>IISI</i> in GDP regression, too. When taking employment population as the regression object, <i>VHNI</i> performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of <i>VHNI</i> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> is better than <i>NTL</i> and <i>IISI</i> in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between <i>VHNI</i> and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, <i>VHNI</i> index can be used for fitting analysis and prediction of economy and power consumption in the future.
format	article
author	Debao Yuan Huinan Jiang Wei Guo Ximin Cui Ling Wu Ziruo Wu Hongsen Wang
author_facet	Debao Yuan Huinan Jiang Wei Guo Ximin Cui Ling Wu Ziruo Wu Hongsen Wang
author_sort	Debao Yuan
title	Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints
title_short	Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints
title_full	Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints
title_fullStr	Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints
title_full_unstemmed	Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints
title_sort	regression analysis and comparison of economic parameters with different light index models under various constraints
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ca83341781c14346b80e320c8bf5757b
work_keys_str_mv	AT debaoyuan regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints AT huinanjiang regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints AT weiguo regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints AT ximincui regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints AT lingwu regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints AT ziruowu regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints AT hongsenwang regressionanalysisandcomparisonofeconomicparameterswithdifferentlightindexmodelsundervariousconstraints
_version_	1718410489667518464

Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints

Ejemplares similares