Study on the distribution pattern and influencing factors of shrinking cities in Northeast China based on the random forest model

Guanghua Yan, Xi Chen, Yun Zhang

Article ID: 1305
Vol 3, Issue 1, 2020

VIEWS - 384 (Abstract) 150 (PDF)

Abstract


Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.


Keywords


Shrinking Cities; Northeast China; Demographic Changes; Linear Regression

Full Text:

PDF


References


1. Allagst K, Wiechimann T, Martinez-Fernandez C. Shrinking cities: International perspectives and policy implications. New York: Routledge; 2014.

2. Huang H. Smart shrinkage: Planning measures for urban decay and its practice in US. Journal of Urban and Regional Planning 2011; 4(3): 157–168.

3. Oswalt P. Shrinking cities: International research. Berlin: Hage Cantz; 2006.

4. Oswalt P. Shrinking cities: Interventions. Berlin: Hage Cantz; 2006.

5. Oswalt P, Rieniets T, Schirmel H, et al. Atlas of shrinking cities. Berlin: Hage Cantz; 2006.

6. Wolff M. Understanding the role of centralization processes for cities-evidence from a spatial perspective of urban Europe 1990-2010. Cities 2018; 75(5): 20–29.

7. Zingale N, Riemann D. Coping with shrinkage in Germany and the United States: A cross-cultural comparative approach toward sustainable cities. Urban Design International 2013; 18(1): 90–98.

8. Mallach A, Haase A, Hattori K. The shrinking city in comparative perspective: Contrasting dynamics and re-sponses to urban shrinkage. Cities 2017; 69(9): 102–108.

9. Hartt D. How cities shrink: Complex pathways to population decline. Cities 2018; 75(5): 38–49.

10. Martinez-Fernandez C, Weyman T, Fol S, et al. Shrinking cities in Australia, Japan, Europe and the USA: From a global process to local policy responses. Progress in Planning 2016; 105(4): 1–48.

11. Hattori K, Kaido K, Matsuyuki M. The development of urban shrinkage discourse and policy response in Japan. Cities 2017; 69(9): 124–132.

12. Zhu X, Zhang Q, Sun P. Effects of urbanization on spatiotemporal distribution of precipitations in Beijing and its related causes. Acta Geographica Sinica 2018; 73(11): 38–56.

13. Wang L, Wu Ri, Li W. Spatial temporal patterns of population aging on China’s urban agglomerations. Acta geographica Sinica 2017; 72(6): 1001–1016.

14. Long Y, Wu K, Wang J. Shrinking cities in China. Modern Urban Research 2015; (9): 14–19.

15. Mao Q, Long Y, Wu K. Temporal and spatial evolution of population density and spatial pattern of urbanization in China from 2000 to 2010. Urban Planning 2015; 39(2): 38–43.

16. Li X, Du Zi, Li X. The spatial distribution and mechanism of city shrinkage in the Pearl River Delta. Modern Urban Research 2015; (9): 36–43.

17. Liu Z, Qi W, Wang X, et al. A literature research on population shrinking. World Regional Studies 2019; 28(1): 13–23.

18. Yang Z, Yang D. Exploring shrinking areas in China availing of city development index. Human Geography 2019; 34(4): 63–72.

19. Zhang Mi, Xiao H. Spatial pattern characteristics and mechanism of urban contraction in Northeast China. Urban Problems 2020; (1): 33–42.

20. Yang M. Identification of Chinese urban shrinkage and its causes based on night light data. Hebei Academic Journal 2020; 40(2): 130–136.

21. Zhang Y, Wang Q, Fu Y, et al. Identification of shrinking cities at precision level in China and its driving forces. Journal of Geo-mathematics 2020; 45(2): 15–19.

22. Zheng L, Xu J, Wang X. Application of random forests algorithm in researches on wetlands. Wetland Science 2019; 17(1): 16–24.

23. Guo Y, Li L. Change in the negative externality of the shrinking cities in China. Scientia Geographica Sinica 2019; 39(1): 52–60.

24. Hoekveld J. Time-space relations and the differences between shrinking regions. Built Environment 2012; 38(2): 179–195.

25. Hollander JB, Németh J. The bounds of smart decline: A foundational theory for planning shrinking cities. Housing Policy Debate 2011; 21(3): 349–367.

26. Ma Z, Li C, Zhang J, et al. Urban shrinkage in developed countries and its implications for China. Human Geography 2016; 31(2): 13–17.

27. Yu C, Hu D, Cao S, et al. The spatial characteristics and changes of ISP-LST of Beijing in recent 30 years. Geographical Research 2019; 38(9): 2346–2356.

28. Anselin L, Bera A K, Florax R, et al. Simple diagnostic tests for spatial dependence. Regional Science and Urban Economics 1996; 26(1): 77–104.

29. Lin X, Yang J, Zhang X, et al. Measuring shrinking cities and influencing factors in urban China: Perspective of population and economy. Human Geography 2017; 32(1): 82–89.

30. Chen X, Zhang W, Zhang H. The relations of urban spatial expansion and economic growth in China: A case study of 261 precision level cities. Science Geographica Sinica 2016; 36(8): 1141–1147.

31. Wang C, Kan K, Zeng Y, et al. Population distribution pattern and influencing factors in Tibet based on random forest model. Acta Geographica Sinica 2019; 74 (4): 664–680.

32. Breiman L. Random forests. Machine Learning 2001; 45(1): 5–32.

33. Cutler A, Cutler D R, Stevens JR. Random forests. Machine Learning 2004; 45(1): 157–176.




DOI: https://doi.org/10.24294/jgc.v3i1.1305

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License

This site is licensed under a Creative Commons Attribution 4.0 International License.