Classification of Shrinking Cities in China Based on Self-Organizing Feature Map

调整大小 城市化 地理 经济地理学 中国 人口 经济增长 业务 人口学 经济 经济政策 社会学 考古 欧洲联盟
作者
Xinyi Wang,Zihan Li,Zhe Feng
出处
期刊:Land [MDPI AG]
卷期号:11 (9): 1525-1525 被引量:14
标识
DOI:10.3390/land11091525
摘要

Since the 1980s, China has been experiencing shrinking cities as part of a massive urbanization process. In recent years, the Matthew effect of factor accumulation has led to an increasingly substantial divergence in population concentration and sparseness in China. The pattern of shrinking Chinese cities has become increasingly severe. Accurate classification of shrinking cities is important for formulating policies and achieving the rational development of shrinking cities. In this study, the data of the 6th and 7th population censuses are used to investigate the shrinking Chinese cities, and prefecture-level cities are utilized as the scale of administrative units. The resident population in 130 cities decreased during the last decade. The population, economy, society, and space indicators are selected to cluster the shrinking cities through the self-organizing feature map neural network. Results show that China’s shrinking cities can be divided into four categories: (1) Sixty-two cities are characterized by a high degree of transfer dependence on the economy due to a chronic lack of population. (2) Twenty-eight cities are characterized by high urban expansion but with population loss. (3) Fourteen cities are characterized by obvious transportation and location advantages and with relatively slight population loss. (4) Twenty-six cities have good industrial development prospects but with serious urban pollution and “siphoning” effects from other cities. The shrinking cities are mainly concentrated in the western, central, and northeastern regions of China, which are represented by the old industrial and resource-depleted cities. The shrinking cities in the eastern region are fewer and less severe, which is mainly related to the high population concentration and developed economy in the region. This study provides solutions from different perspectives for four types of shrinking cities and serves as an empirical reference for policymakers and urban planners.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
FFFFFFG完成签到,获得积分10
刚刚
虚幻的安容完成签到,获得积分20
刚刚
个性的汲完成签到,获得积分10
2秒前
852应助斯文的傲珊采纳,获得10
2秒前
Ava应助cc哈库纳玛塔塔采纳,获得10
2秒前
延文星发布了新的文献求助10
4秒前
7秒前
时尚白凡完成签到 ,获得积分10
7秒前
Can完成签到,获得积分10
8秒前
邓洁宜完成签到,获得积分10
11秒前
11秒前
Ningxin完成签到,获得积分10
13秒前
炙热的冰萍完成签到,获得积分10
14秒前
zym完成签到 ,获得积分10
15秒前
hyx完成签到 ,获得积分10
15秒前
DYY发布了新的文献求助20
16秒前
AAAcaiwenji完成签到,获得积分10
16秒前
18秒前
春春完成签到 ,获得积分10
20秒前
captain_sir完成签到 ,获得积分10
20秒前
21秒前
ran完成签到 ,获得积分10
23秒前
zyn发布了新的文献求助10
23秒前
hd完成签到,获得积分10
25秒前
26秒前
一条鱼叫弗里登完成签到 ,获得积分10
28秒前
weihe完成签到,获得积分10
28秒前
娅娃儿完成签到 ,获得积分10
28秒前
直率的乐萱完成签到 ,获得积分10
29秒前
Tom完成签到,获得积分10
30秒前
Feijiahao完成签到,获得积分10
33秒前
小莫完成签到 ,获得积分10
34秒前
wenbinvan完成签到,获得积分0
35秒前
35秒前
monster完成签到 ,获得积分10
38秒前
38秒前
41秒前
closer完成签到 ,获得积分10
43秒前
多喝水完成签到 ,获得积分10
45秒前
知性的成完成签到 ,获得积分10
48秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kolmogorov, A. N. Qualitative study of mathematical models of populations. Problems of Cybernetics, 1972, 25, 100-106 800
Vertébrés continentaux du Crétacé supérieur de Provence (Sud-Est de la France) 600
A complete Carnosaur Skeleton From Zigong, Sichuan- Yangchuanosaurus Hepingensis 四川自贡一完整肉食龙化石-和平永川龙 600
FUNDAMENTAL STUDY OF ADAPTIVE CONTROL SYSTEMS 500
微纳米加工技术及其应用 500
Nanoelectronics and Information Technology: Advanced Electronic Materials and Novel Devices 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5304453
求助须知:如何正确求助?哪些是违规求助? 4450972
关于积分的说明 13850191
捐赠科研通 4337994
什么是DOI,文献DOI怎么找? 2381744
邀请新用户注册赠送积分活动 1376791
关于科研通互助平台的介绍 1343965