Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

Abstract

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

Carbon-Emission Characteristics and Influencing Factors in Growing and Shrinking Cities: Evidence from 280 Chinese Cities

“…One is to consider the change in population over a certain period, where an increase in population is identified as urban growth, and a decrease is identified as urban shrinkage [ 6 , 28 , 33 ]. The other way is to consider the change in nighttime light over a certain period; when nighttime light brightens, the city is growing, and when it dims, the city is shrinking [ 34 , 35 , 36 ]. However, population division can only reflect changes in the urban population, and while nighttime light can reflect the economy and population, the data are not continuous in time and are characterized by uncertainty, which will affect the results.…”

Depopulation and associated challenges for US cities by 2100

“…Studies have defined the range of population change differently, and no universal standard exists [35][36][37] . To be consistent with the existing literature and to make our findings translatable, we classified cities based on the sign of percent change in population from 2000 to 2010, 2010 to 2020 and 2000 to 2020, along with their average annual change for 2010-2020.…”

“…Wiechmann et al 36 defined cities with a continuous population decline for more than two years as shrinking cities. Oswalt and Rieniets 37 labeled cities with over 10% loss or more than 1% average loss as shrinking cities 35 . Therefore, based on these resources, we opted to label cities with an average annual change of 5% or higher as severely depopulating, from 1% to 5% as moderately depopulating, and from 0% to 1% as slowly depopulating when the signs for all three time periods were negative, and vice versa for an increasing population.…”

Temporal—Spatial Changes in Vegetation Coverage under Climate Change and Human Activities: A Case Study of Central Yunnan Urban Agglomeration, China

“…In 2008, researchers introduced a new perspective by utilizing nighttime light data to study economic development [75,76]. Since then, these data have been employed to investigate the effects of human activities on the environment, urban areas [77], climate change [78], and ecosystems [79]. In our exploration of the impact of human activity on vegetation coverage, we have opted to use nighttime light data as a measure.…”