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

Abstract

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 km2 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.

A connectivity modeling and evaluating methodological framework in biodiversity hotspots based on naturalness and linking wilderness

“…At the same time, the consistency index of the discriminant matrix was calculated and the consistency test was carried out. The formula is as follows: Since roads of different levels and settlements of different developed levels have different influences on the wilderness, the night light index reflects the degree of development of roads or settlements to a certain extent (Liu et al, 2021). The brighter the night light is, the greater the impact of the artificial facilities is on the wilderness.…”

“…Since roads of different levels and settlements of different developed levels have different influences on the wilderness, the night light index reflects the degree of development of roads or settlements to a certain extent (Liu et al, 2021). The brighter the night light is, the greater the impact of the artificial facilities is on the wilderness.…”

Bridging carbon footprint and urban livability differences in Chinese non-core cities: a typological exploration

“…The poor livability of the cities is also re ected in that they have the worst Social vitality scores. Using nighttime lighting data, it is demonstrated that in Hunan Province, for example, non-core cities are increasingly being left behind by their provincial capitals socio-economically, and such intra-regional unevenness has become more pronounced in recent years (Liu et al, 2021;Zhu et al, 2022). On the Innovation capacity dimension, these cities have a moderate performance in Education for innovation (I2) and Innovation support (I3), indicating that their governments are to some extent emphasizing urban innovation and have a certain base of innovation facilities, including universities and research institutes.…”

Spatio-Temporal Characteristics and Influencing Factors of Urban Spatial Quality in Northeast China Based on DMSP-OLS and NPP-VIIRS Nighttime Light Data

“…The nighttime light data provided by the US Defense Meteorological Satellite have the advantages of strong data availability, small processing capacity, and brightness indicators that reflect the intensity of economic activities. Even though the data still have issues with stability and compatibility [46,47], the abovementioned characteristics cause them to be suitable for large-scale and long-term regional spatial research, and the data can still effectively show the quality of urban spatial development. On the other hand, spatial simulation based on DMSP-OLS nighttime light data is known for its small data volume and strong comprehensiveness, causing it to be widely used in simulating regional and urban spatial distribution [48].…”