2013—2020年中国财政千强镇的时空变化及影响因素分析

doi:10.3969/j.issn.1004-9479.2025.01.20222437

世界地理研究 ›› 2025, Vol. 34 ›› Issue (1): 138-153.DOI: 10.3969/j.issn.1004-9479.2025.01.20222437

• 城市与产业 • 上一篇

2013—2020年中国财政千强镇的时空变化及影响因素分析

李志远¹^,²(), 马芳芳¹^,², 丁志伟¹^,²()

^1.河南大学地理科学与工程学部地理科学学院，郑州 450046
^2.黄河中下游数字地理技术教育部重点实验室/环境与规划国家级实验教学示范中心，开封 475004

收稿日期:2022-11-21 修回日期:2023-05-21 出版日期:2025-01-15 发布日期:2025-02-07
通讯作者: 丁志伟
作者简介:李志远（1999—），女，硕士，研究方向为城市-区域综合发展，E-mail：lizhiyuan20210330@163.com。
基金资助:
国家自然科学基金项目(42271213);河南省社科决策咨询项目(94);河南省研究生教育改革与质量提升工程项目(YJS2024SZ26);河南省本科高校研究性教学改革与实践项目(教高［2023］388号)

Analysis of the spatial and temporal changes and influencing factors of top 1 000 towns in finance in China from 2013 to 2020

Zhiyuan LI¹^,²(), Fangfang MA¹^,², Zhiwei DING¹^,²()

^1.College of Geographical Sciences, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou 450046, China
^2.Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of Education / National Demonstration Center for Environment and Planning, Henan University, Kaifeng 475004, China

Received:2022-11-21 Revised:2023-05-21 Online:2025-01-15 Published:2025-02-07
Contact: Zhiwei DING

摘要/Abstract

摘要：

根据2013—2020年财政千强镇榜单数据，综合运用空间分析和空间回归模型方法，分析财政千强镇的时空演化规律以及影响因素。研究表明：①财政千强镇在空间上趋向沿海城市群集聚，呈现出以江浙沪、珠三角和京津冀鲁为主的核心区和局部零散分布的总体格局特征。在频数方面，东部财政千强镇明显优于西部地区，且长三角、珠三角等高水平经济区频数高而相对稳定。②从进入退出情况看，整体稳定度不高，东部沿海一带进入和退出均表现出较大的波动性，特别是江、浙两省变动较为明显。③从标准差椭圆的范围和重心点来看，范围总体呈扩大趋势且重心点向西北移动；从影响椭圆范围变化的空间倾斜来看，主要是受沿海地区入围乡镇数量增减和西北部入围乡镇较少的综合作用影响。④从集中程度来看，R指数总体呈现下降趋势，印证了空间集聚现象的存在。从空间自相关来看，显著HH区主要分布在长三角城市群及其向北延伸至江苏南部的局部地市和京津冀地区；显著LL区主要形成了以云南为核心的团状集聚区，部分分布在重庆、河北等省份。⑤从影响因素的解释力看，工业潜力、人口支撑、经济环境的影响作用较大，城镇化、地形因子和潜力环境次之，商业活力和土地环境的解释力较小。

关键词: 财政千强镇, 时空变化, 影响因素, 中国, GWR模型

Abstract:

The top 1000 towns in finance in China from 2013 to 2020, the study analyzes spatial distribution characteristics and spatial and temporal changes of the top 1000 towns in finance by means of spatial autocorrelation analysis and average nearest neighbor analysis, and uses the geographic weighted regression and other methods to explore its influencing factors. The results show that: ① From the perspective of spatial distribution, it shows obvious characteristics of sea-trending. The top 1000 towns in finance present a spatial distribution pattern of "the core area of Jiangsu, Zhejiang, Shanghai, Pearl River Delta, Beijing, Tianjin, Hebei and Shandong and other scattered distribution areas". In terms of frequency scale of the top 1000 towns in finance, the performance of the top 1000 towns in finance in the eastern region is significantly better than that of the western region, and the high-level economic zones such as the Yangtze River Delta and the Pearl River Delta have high frequency and are relatively stable. ② From the perspective of entry and exit, it is mainly the result of the increase and decrease of villages and towns in the enclosed sea area and the relatively low comprehensive effect of the villages and towns in the northwest. ③ From the perspective of the ellipse range and the center of gravity reflecting the standard deviation, the range is generally expanding and the center of gravity moves northwestward. From the perspective of spatial tilt affecting the change of ellipse range, it is mainly the result of the increase and decrease of villages and towns in the enclosed sea area and the relatively low comprehensive effect of the villages and towns in the northwest. ④ From the perspective of spatial concentration, the R index generally shows a downward trend, showing the further enhancement of the concentration degree of towns in finance in space. From the spatial local LISA agglomeration map, significant HH area is mainly distributed in the Yangtze River Delta urban agglomeration and local cities extending north to southern Jiangsu and the Beijing-Tianjin-Hebei region. Significant LL area mainly forms a cluster area with Yunnan as the core, scattered in Chongqing and Hebei. ⑤ From the perspective of influencing factors, industrial potential, population support, and economic environment have a high degree of explanation of the top 1000 towns in finance. Urbanization, interpretation of elevation, and potential environment are the secondary important factors, while the business vitality, land environment, and have less explanatory power.

Key words: the top 1000 towns in finance, time-space change, influencing factors, China, GWR Model

李志远, 马芳芳, 丁志伟. 2013—2020年中国财政千强镇的时空变化及影响因素分析[J]. 世界地理研究, 2025, 34(1): 138-153.

Zhiyuan LI, Fangfang MA, Zhiwei DING. Analysis of the spatial and temporal changes and influencing factors of top 1 000 towns in finance in China from 2013 to 2020[J]. World Regional Studies, 2025, 34(1): 138-153.

图/表 6

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2013—2020年中国财政千强镇的时空变化及影响因素分析

Analysis of the spatial and temporal changes and influencing factors of top 1 000 towns in finance in China from 2013 to 2020

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Metrics

省级行政区	2014年		2015年		2016年		2017年		2018年		2019年		2020年
省级行政区	退	进	退	进	退	进	退	进	退	进	退	进	退	进
北京市	1	34	15	10	5	27	8	16	8	16	4	8	10	8
天津市	1	10	8	2	2	8	5	2	7	3	3	9	7	2
河北省	20	9	11	5	9	8	6	5	8	12	7	8	9	19
山西省	7	1	2	0	4	0	0	3	4	1	1	1	1	2
内蒙古自治区	2	10	9	6	10	5	5	2	6	7	4	3	5	7
辽宁省	14	3	13	0	1	0	0	1	0	6	3	1	2	3
吉林省	4	0	2	6	6	1	1	5	4	0	0	2	3	4
黑龙江省	1	1	1	0	0	1	0	0	2	1	1	1	1	0
上海市	1	7	1	4	2	4	2	4	2	2	1	9	2	3
江苏省	24	43	20	16	28	30	24	16	17	16	19	7	11	10
浙江省	72	42	15	5	25	35	23	28	28	19	29	19	24	36
安徽省	6	14	8	6	13	18	6	7	11	9	10	6	15	15
福建省	12	19	10	5	9	25	4	14	16	8	10	5	11	8
江西省	5	16	4	6	13	16	12	6	14	3	3	3	6	9
山东省	76	30	10	11	8	16	16	14	14	14	19	9	8	15
河南省	6	6	3	4	7	14	8	11	6	12	6	8	8	14
湖北省	19	11	3	3	17	6	7	4	9	8	5	7	17	5
湖南省	6	7	7	2	3	10	8	1	3	1	1	0	3	2
广东省	9	18	7	12	11	19	9	5	8	12	12	17	14	11
广西壮族自治区	2	8	5	5	8	3	4	1	5	2	1	4	4	7
海南省	1	0	3	6	2	3	5	3	2	1	1	1	2	2
重庆市	5	0	0	0	0	3	1	0	1	0	0	1	1	1
四川省	3	10	10	3	6	5	6	7	8	4	0	3	5	13
贵州省	8	3	2	5	5	5	5	2	4	0	0	2	4	4
云南省	7	2	1	2	5	6	2	1	2	2	4	1	1	1
西藏自治区	0	0	0	1	1	0	0	1	1	2	0	0	0	0
陕西省	4	7	14	2	5	3	0	8	6	0	2	1	2	2
甘肃省	1	1	1	0	0	0	0	0	0	0	0	0	0	0
青海省	0	1	2	0	0	0	0	0	0	0	0	0	0	0
宁夏回族自治区	0	1	0	0	1	0	0	0	0	1	1	1	1	0
新疆维吾尔自治区	0	4	4	64	65	0	0	0	0	34	9	19	36	10