Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from...

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Autores principales:	Yuxin Liu, Tian He, Yi Wang, Changhui Peng, Hui Du, Shuai Yuan, Peng Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	night light remote sensing Hunan province BP artificial neural network urban spatial expansion built-up region Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ebb5bdee33b048dfabc2498b21a2e0ca
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spelling	oai:doaj.org-article:ebb5bdee33b048dfabc2498b21a2e0ca2021-11-11T19:38:58ZAnalysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China10.3390/su1321119822071-1050https://doaj.org/article/ebb5bdee33b048dfabc2498b21a2e0ca2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11982https://doaj.org/toc/2071-1050Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km<sup>2</sup> by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.Yuxin LiuTian HeYi WangChanghui PengHui DuShuai YuanPeng LiMDPI AGarticlenight light remote sensingHunan provinceBP artificial neural networkurban spatial expansionbuilt-up regionEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11982, p 11982 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	night light remote sensing Hunan province BP artificial neural network urban spatial expansion built-up region Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	night light remote sensing Hunan province BP artificial neural network urban spatial expansion built-up region Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Yuxin Liu Tian He Yi Wang Changhui Peng Hui Du Shuai Yuan Peng Li Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
description	Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km<sup>2</sup> by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.
format	article
author	Yuxin Liu Tian He Yi Wang Changhui Peng Hui Du Shuai Yuan Peng Li
author_facet	Yuxin Liu Tian He Yi Wang Changhui Peng Hui Du Shuai Yuan Peng Li
author_sort	Yuxin Liu
title	Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
title_short	Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
title_full	Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
title_fullStr	Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
title_full_unstemmed	Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
title_sort	analysis and prediction of expansion of central cities based on nighttime light data in hunan province, china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ebb5bdee33b048dfabc2498b21a2e0ca
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_version_	1718431500619218944

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

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