Shrinkage and Expansion Mechanisms of Resource-based Cities: Analysis Based on Multidimensional Typology Definition Matrix
- Xianjian Yi ,
- Lei Wang ,
- Xinyue Gu
Abstract
The urban shrinkage phenomenon is increasingly common. However, at the same time, it is accompanied by local growth within the region, and its complex impact mechanism still lacks in-depth research. This paper takes Daqing, a resource-based city, as an example and constructs a multi-dimensional urban growth/shrinkage type definition matrix. The random forest classification model is used to quantitatively analyze the relevant factors affecting the classification matrix by comparing different models. The results show that: (1) The growth/shrinkage of Daqing City leads to a concentric structure of intensive growth—expansion growth—expansion shrinkage—intensive shrinkage that gradually changes from the center to the periphery in space. (2) The random forest classification model can better explain the cause mechanism of urban growth/shrinkage. (3) The growth/shrinkage of resource-based cities in the transformation stage is mainly affected by economic factors, such as traffic accessibility and urban morphology related to the resource economy. This study provides an evaluation framework from qualitative to quantitative, providing useful references for research and planning policy formulation in related fields.
Keywords
urban shrinkage, random forest, resource-based cities
References
- A harmonized global nighttime light dataset 1992–2018 | Scientific Data. (n.d.). Retrieved October 1, 2023, from https://www.nature.com/articles/s41597-020-0510-y
- Bartholomae, F., Woon Nam, C., & Schoenberg, A. (2017). Urban shrinkage and resurgence in Germany. Urban Studies, 54(12), 2701–2718. https://doi.org/10.1177/0042098016657780
- Deng, C., & Ma, J. (2015). Viewing urban decay from the sky: A multi-scale analysis of residential vacancy in a shrinking U.S. city. Landscape and Urban Planning, 141, 88–99. https://doi.org/10.1016/j.landurbplan.2015.05.002
- Döringer, S., Uchiyama, Y., Penker, M., & Kohsaka, R. (2020). A meta-analysis of shrinking cities in Europe and Japan: Towards an integrative research agenda. European Planning Studies, 28(9), 1693–1712. https://doi.org/10.1080/09654313.2019.1604635
- Gao, Z., Wang, S., & Gu, J. (2021). Identification and Mechanisms of Regional Urban Shrinkage: A Case Study of Wuhan City in the Heart of Rapidly Growing China. Journal of Urban Planning and Development, 147(1), 05020033. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000643
- Goutte, C., & Gaussier, E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In D. E. Losada & J. M. Fernández-Luna (Eds.), Advances in Information Retrieval (pp. 345–359). Springer. https://doi.org/10.1007/978-3-540-31865-1_25
- Guan, D., He, X., & Hu, X. (2021). Quantitative identification and evolution trend simulation of shrinking cities at the county scale, China. Sustainable Cities and Society, 65, 102611. https://doi.org/10.1016/j.scs.2020.102611
- Jiang, Z., Zhai, W., Meng, X., & Long, Y. (2020). Identifying Shrinking Cities with NPP-VIIRS Nightlight Data in China. Journal of Urban Planning and Development, 146(4), 04020034. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000598
- Kim, Y. E., Lee, J. S., & Kim, S. (2022). Proposing the classification matrix for growing and shrinking cities: A case study of 228 districts in South Korea. Habitat International, 127, 102644. https://doi.org/10.1016/j.habitatint.2022.102644
- Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu, B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X., Chen, J., He, C., … Zhu, Z. (2020). Mapping global urban boundaries from the global artificial impervious area (GAIA) data. Environmental Research Letters, 15(9), 094044. https://doi.org/10.1088/1748-9326/ab9be3
- Mallach, A., Haase, A., & Hattori, K. (2017). The shrinking city in comparative perspective: Contrasting dynamics and responses to urban shrinkage. Cities, 69, 102–108. https://doi.org/10.1016/j.cities.2016.09.008
- Peng, W., Wu, Z., Duan, J., Gao, W., Wang, R., Fan, Z., & Liu, N. (2023). Identifying and quantizing the non-linear correlates of city shrinkage in Japan. Cities, 137, 104292. https://doi.org/10.1016/j.cities.2023.104292
- Shan, C., & Gu, X. (2024). Understanding urban sprawl based on open-source data: A study of three American cities. In Urban Construction and Management Engineering IV. CRC Press.
- Shan, C., Liu, Y., & Gu, X. (2025). Curbing Urban Sprawl: A Study of Three Typical American Cities from the Perspectives of the Built Environment and Socioeconomics. Journal of Urban Planning and Development, 151(1), 05024044. https://doi.org/10.1061/JUPDDM.UPENG-5316
- Shrinking cities in a rapidly urbanizing China—Ying Long, Kang Wu, 2016. (n.d.). Retrieved September 30, 2023, from https://journals.sagepub.com/doi/full/10.1177/0308518X15621631
- Wilcox, R. R. (2009). Comparing Pearson Correlations: Dealing with Heteroscedasticity and Nonnormality. Communications in Statistics - Simulation and Computation, 38(10), 2220–2234. https://doi.org/10.1080/03610910903289151
- Zhao, N., Liu, Y., Cao, G., Samson, E. L., & Zhang, J. (2017). Forecasting China’s GDP at the pixel level using nighttime lights time series and population images. GIScience & Remote Sensing, 54(3), 407–425. https://doi.org/10.1080/15481603.2016.1276705