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

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