基于地理探测器和GWR模型的中国重点镇布局定量归因

doi:10.18306/dlkxjz.2020.10.008

摘要/Abstract

摘要：

重点镇是小城镇发展的龙头,形成科学合理的重点镇布局对优化中国城市化战略格局有重要意义。论文以2004年和2014年分别公布的1887个和3675个全国重点镇为样本,对其分布及效应的变动特征进行探究,进而在地级尺度对重点镇布局的影响因子及其作用进行地理探测和局部空间回归。结果表明：① 经增补调整,中国重点镇布局及建设效应的均衡性增强,主要集聚区西移北扩,冷热点的分布突破“胡焕庸线”,经济辐射效应的分化程度减弱,体现出政策因素的有力影响。除县际均衡和区域倾斜政策外,重点镇的分布还受到海拔高度、公路网密度、常住人口城镇化率等因子的显著作用。② 因子探测器、GWR模型和交互作用探测器的结合能更精准地刻画影响因子的作用方式、方向、路径和强度。中国重点镇的布局不是5个显著性因子均匀、独立、直接作用的结果,而是影响均具空间异质性的各因子两两交互作用后增效的产物。③ 县际均衡政策与其他因子的协同作用是形成现有重点镇分布格局的主导力量;区域倾斜政策的效果总体较好,但目标区域还需更准确。

关键词: 重点镇, 定量归因, 地理探测器, 地理加权回归, 中国

Abstract:

National key towns are the leader in the development of small towns. The formation of scientific and reasonable national key town layouts is of great significance for optimizing China's urbanization strategy. Taking the 1887 and 3675 national key towns published in 2004 and 2014 respectively as samples, this study used geodector and the geographically weighted regression (GWR) model to explore the changing characteristics of their distribution and analyze the influencing factors of national key town layouts and effects at the prefecture level. The results show that: 1) After the adjustment by the government, the balance of the layout and construction effect of national key towns is enhanced. The main gathering areas moved westward and northward, the distribution of cold spots and hotspots break through the "Hu Huanyong Line", and the degree of differentiation of economic radiation effect is weakened, reflecting the powerful influence of policy factors. In addition to the inter-county balance and regional preferential policies, the distribution of national key towns is also significantly affected by factors such as altitude, road network density, and resident population urbanization rate. 2) The combination of the factor detector, the GWR model, and the interaction detector can more accurately characterize the mode of action, direction, path, and intensity of the influencing factors. The layout of national key towns is not the result of the uniform, independent, and direct effect of the five significant factors, but the product of the synergistic effect of the interaction of the factors with spatial heterogeneity. 3) The synergy between the inter-county balance policy and other factors is the dominant force in the formation of the existing layout of the key towns; the effect of the regional preferential policy is overall positive, but the target areas need to be more accurately identified.

Key words: national key towns, quantitative attribution, Geodetector, geographically weighted regression, China

韩静, 芮旸, 杨坤, 刘薇, 马滕. 基于地理探测器和GWR模型的中国重点镇布局定量归因[J]. 地理科学进展, 2020, 39(10): 1687-1697.

HAN Jing, RUI Yang, YANG Kun, LIU Wei, MA Teng. Quantitative attribution of national key town layout based on geodetector and the geographically weighted regression model[J]. PROGRESS IN GEOGRAPHY, 2020, 39(10): 1687-1697.

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参考文献 30

[1]	费孝通. 小城镇四记 [M]. 北京: 新华出版社, 1985.
	[ Fei Xiaotong. Four records of small towns. Beijing, China: Xinhua Publishing House, 1985. ]
[2]	石楠. 小城镇仍然是个大问题[J]. 小城镇建设, 2017,35(11):108-109.
	[ Shi Nan. Small towns are still a big problem. Development of Small Cities & Towns, 2017,35(11):108-109. ]
[3]	陈秉钊. “小城镇、大战略”这个命题是不会变的[J]. 小城镇建设, 2017,35(5):102-104.
	[ Chen Bingzhao. The proposition of "small town, big strategy" will not change. Development of Small Cities & Towns, 2017,35(5):102-104. ]
[4]	崔功豪. 中国小城镇发展之路求索[J]. 小城镇建设, 2018,36(9):5-6.
	[ Cui Gonghao. Exploring the way to develop small towns in China. Development of Small Cities & Towns, 2018,36(9):5-6. ]
[5]	方创琳. 中国城市发展方针的演变调整与城市规模新格局[J]. 地理研究, 2014,33(4):674-686. doi: 10.11821/dlyj201404008
	[ Fang Chuanglin. A review of Chinese urban development policy, emerging patterns and future adjustments. Geographical Research, 2014,33(4):674-686. ]
[6]	范建双, 虞晓芬, 周琳. 城镇化、城乡差距与中国经济的包容性增长[J]. 数量经济技术经济研究, 2018(4):41-60.
	[ Fan Jianshuang, Yu Xiaofen, Zhou Lin. Urbanization, urban-rural gap and inclusive growth in China. The Journal of Quantitative & Technical Economics , 2018(4):41-60. ]
[7]	赵辉, 陈玲. 全国重点镇规划编制实施存在的问题、原因及对策建议[J]. 小城镇建设, 2013,31(5):52-53.
	[ Zhao Hui, Chen Ling. Problems, causes and suggestions on the implementation of national key towns planning. Development of Small Cities & Towns, 2013,31(5):52-53. ]
[8]	王路生, 梁立东. 大城市周边重点镇总体规划策略: 以南宁市金陵镇为例[J]. 规划师, 2014,30(S2):187-193.
	[ Wang Lusheng, Liang Lidong. The master planning strategy of the key towns surrounding metropolis: Nanning example. Planners, 2014,30(S2):187-193. ]
[9]	李继东. 区域竞争力与重点镇产业规划研究[J]. 小城镇建设, 2011,29(11):26-30.
	[ Li Jidong. Research on regional competitiveness and industrial planning of key towns. Development of Small Cities & Towns, 2011,29(11):26-30. ]
[10]	周鑫根. 浙江省重点镇供水建设规划要点[J]. 给水排水, 2007,33(7):27-29.
	[ Zhou Xingen. Key points of water supply construction planning in key towns of Zhejiang Province. Water and Wastewater Engineering, 2007,33(7):27-29. ]
[11]	李加林, 许继琴, 叶持跃. 重点镇建设指标体系研究: 以宁波市为例[J]. 地域研究与开发, 2001,20(1):41-45.
	[ Li Jialin, Xu Jiqin, Ye Chiyue. A system of indicators for developing major towns: A case study of Ningbo City. Areal Research and Development, 2001,20(1):41-45. ]
[12]	柳思维, 陈英子. 建设农村重点镇区域性综合商业服务业中心的探讨: 洞庭湖区沅江市草尾镇的调查启示[J]. 商业经济与管理, 2011(11):11-16.
	[ Liu Siwei, Chen Yingzi. How to build regional integrated business service center in Chinese rural areas: The case of Caowei Town. Journal of Business Economics, 2011(11):11-16. ]
[13]	赵之枫, 张建, 郭玉梅. 郊区化和城镇化背景下北京郊区小城镇住宅建设的博弈: 北京郊区重点镇住宅建设调研与分析[J]. 城市发展研究, 2007,14(6):58-61.
	[ Zhao Zhifeng, Zhang Jian, Guo Yumei. The dilemma of the residence construction of small town in Beijing under the urbanization and suburbanization. Urban Studies, 2007,14(6):58-61. ]
[14]	张春燕. 我国重点镇发展区域比较的初步研究: 基于全国1861个重点镇经验数据的分析[D]. 上海: 华东师范大学, 2008.
	[ Zhang Chunyan. A comparative study of the Chinese key towns: On the data of 1861 key towns. Shanghai, China: East China Normal University, 2008. ]
[15]	胡诗文, 陈金华. 中国历史文化名村名镇空间分异及影响因素研究[J]. 小城镇建设, 2016,34(7):62-67.
	[ Hu Shiwen, Chen Jinhua. Research on spatial differentiation and influence factors of the historic cultural towns and villages in China. Development of Small Cities & Towns, 2016,34(7):62-67. ]
[16]	邵留长, 乔家君, 乔谷阳. 中国专业村镇空间格局及其影响因素[J]. 经济地理, 2016,36(3):131-138.
	[ Shao Liuchang, Qiao Jiajun, Qiao Guyang. The spatial pattern and influence factors of specialized villages and towns in China. Economic Geography, 2016,36(3):131-138. ]
[17]	孙枫, 汪德根. 全国特色景观旅游名镇名村空间分布及发展模式[J]. 旅游学刊, 2017,32(5):80-93.
	[ Sun Feng, Wang Degen. The spatial distribution and development pattern of tourism towns and villages with characteristic landscape. Tourism Tribune, 2017,32(5):80-93. ]
[18]	马海涛, 李强, 刘静玉, 等. 中国淘宝镇的空间格局特征及其影响因素[J]. 经济地理, 2017,37(9):118-124.
	[ Ma Haitao, Li Qiang, Liu Jingyu, et al. Spatial pattern characteristics and influencing factors of Taobao towns in China. Economic Geography, 2017,37(9):118-124. ]
[19]	张思蒙, 李宪宝. 我国特色小镇空间分布特征及其区域影响因素[J]. 资源开发与市场, 2018,34(8):1074-1079.
	[ Zhang Simeng, Li Xianbao. Spatial distribution characteristics and regional influence factors of characteristics towns in China. Resource Development & Market, 2018,34(8):1074-1079. ]
[20]	王新越, 候娟娟, 韩霞霞. 中国特色小镇空间分布特征及影响因素研究[J]. 规划师, 2018,34(1):12-15.
	[ Wang Xinyue, Hou Juanjuan, Han Xiaxia. Spatial distribution and influencing factors of China's characteristic towns. Planners, 2018,34(1):12-15. ]
[21]	芮旸, 唐蓓佩, 王兴, 等. 国家园林城市时空演变特征及其影响机理[J]. 地理研究, 2018,37(1):20-36. doi: 10.11821/dlyj201801002
	[ Rui Yang, Tang Beipei, Wang Xing, et al. The spatio-temporal evolutionary characteristics and the impact mechanism of national garden cities in China. Geography Research, 2018,37(1):20-36. ]
[22]	李钢, 陈未雨, 杨兰, 等. 武汉市快递自提点的空间格局与集聚模式研究[J]. 地理科学进展, 2019,38(3):407-416. doi: 10.18306/dlkxjz.2019.03.010
	[ Li Gang, Chen Weiyu, Yang Lan, et al. Spatial pattern and agglomeration mode of parcel collection and delivery points in Wuhan City. Progress in Geography, 2019,38(3):407-416. ]
[23]	Getis A, Ord J K. The analysis of spatial association by use of distance statistics[J]. Geographical Analysis, 1992,24(3):189-206. doi: 10.1111/gean.1992.24.issue-3
[24]	王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017,72(1):116-134. doi: 10.11821/dlxb201701010
	[ Wang Jinfeng, Xu Chengdong. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017,72(1):116-134. ]
[25]	杨忍, 罗秀丽, 陈燕纯. 中国县域乡村地域多功能格局及影响因素识别[J]. 地理科学进展, 2019,38(9):1316-1328. doi: 10.18306/dlkxjz.2019.09.005
	[ Yang Ren, Luo Xiuli, Chen Yanchun. Spatial pattern and influencing factors of rural multifunctionality at county level in China. Progress in Geography, 2019,38(9):1316-1328. ]
[26]	Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression[J]. Journal of the Royal Statistical Society: Series D (The Statistician), 2017,47(3):431-443. doi: 10.1111/rssd.1998.47.issue-3
[27]	王珂靖, 蔡红艳, 杨小唤. 多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用[J]. 地理科学进展, 2016,35(12):1494-1505. doi: 10.18306/dlkxjz.2016.12.006
	[ Wang Kejing, Cai Hongyan, Yang Xiaohuan. Multiple scale spatialization of demographic data with multi-factor linear regression and geographically weighted regression models. Progress in Geography, 2016,35(12):1494-1505. ]
[28]	韩静, 芮旸, 马滕, 等. 国家园林县城省际分布格局演化及影响因素[J]. 长江流域资源与环境, 2019,28(2):829-838.
	[ Han Jing, Rui Yang, Ma Teng, et al. Evolution and influencing factors of inter provincial distribution pattern of national garden counties. Resources and Environment in the Yangtze Basin, 2019,28(2):829-838. ]
[29]	逯进, 王恩泽. 新能源示范城市建设对区域环境污染治理的影响[J]. 资源科学, 2019,41(11):2107-2118.
	[ Lu Jing, Wang Enze. Impact of new energy demonstration city construction on regional environmental pollution control. Resources Science, 2019,41(11):2107-2118. ]
[30]	刘飞. 货币政策如何影响中国的信贷资源再分配: 基于双重差分模型的实证检验[J]. 财经论丛, 2013(2):50-56.
	[ Liu Fei. How monetary policy influences credit resource reallocation in China: Empirical test based on difference-in-difference model. Collected Essays on Finance and Economics, 2013(2):50-56. ]

样本	变量	重点镇认定前				重点镇认定后			DIFF(FU)-DIFF(BL)
样本	变量	处理组	控制组	差分值		处理组	控制组	差分值	DIFF(FU)-DIFF(BL)
2004年公布的全国重点镇	GDP	76.10	38.24	37.86^**		290.45	149.31	141.14^***	103.28
2004年公布的全国重点镇	非农就业率	47.21	25.80	21.41^*		84.61	41.14	43.47^**	22.06
2014年增补调整后的全国重点镇	GDP	39.82	2.09	37.73^*		30.5	26.07	4.43^*	-33.30
2014年增补调整后的全国重点镇	非农就业率	71.06	53.78	17.27^**	70.03		51.23	18.80^**	1.53

影响因子维度	自变量名称	计算方法	单位	预期作用方向
政策	县际均衡政策	统计年鉴获取	个	+
政策	区域倾斜政策	否=0;是=1	—	+
地理	海拔高度	ArcGIS栅格统计	m	-
	地形起伏度	ArcGIS栅格统计	m	-
	到省会城市中心距离	ArcGIS邻域分析	km	-
	公路网密度	ArcGIS密度分析	km/km²	+
经济	人均GDP	统计年鉴获取	万元	+
	二三产业产值比重	二三产业产值/GDP	%	+
	地方一般预算收入	统计年鉴获取	万元	+
	全社会固定资产投资	统计年鉴获取	万元	+
社会	人口密度	常住人口/行政区面积	人/km²	+
	非农化率	从事二三产业人口/总人口	%	+
	常住人口城镇化率	常住人口/总人口	%	+

影响因子维度	自变量名称	P值	显著性水平	q值	解释力排序
政策	县际均衡政策	<0.001	0.01	0.506	1
政策	区域倾斜政策	<0.001	0.01	0.038	4
地理	海拔高度	<0.001	0.01	0.082	3
	地形起伏度	0.101	—	0.061
	到省会城市中心距离	0.502	—	0.015
	公路网密度	0.004	0.01	0.139	2
经济	人均GDP	0.945	—	0.006
	二三产业产值比重	0.325	—	0.027
	地方一般预算收入	0.993	—	0.004
	全社会固定资产投资	0.992	—	0.003
社会	人口密度	0.382	—	0.001
	非农化率	0.464	—	0.058
	常住人口城镇化率	0.093	0.1	0.028	5

q=A∩B	A+B	比较结果	交互作用产生效应的类型	交互作用后的解释力排序
S₁∩S₂=0.536	S₁(0.506)+S₂(0.038)=0.544	A+B>q>A, B	双因子增强	3
S₁∩S₃=0.629	S₁(0.506)+S₃(0.082)=0.588	q>A+B	非线性增强	2
S₁∩S₄=0.646	S₁(0.506)+S₄(0.139)=0.645	q>A+B	非线性增强	1
S₁∩S₅=0.527	S₁(0.506)+S₅(0.028)=0.534	A+B>q>A, B	双因子增强	4
S₂∩S₃=0.096	S₂(0.038)+S₃(0.082)=0.120	A+B>q>A, B	双因子增强	9
S₂∩S₄=0.184	S₂(0.038)+S₄(0.139)=0.177	q>A+B	非线性增强	8
S₂∩S₅=0.060	S₂(0.038)+S₅(0.028)=0.066	A+B>q>A, B	双因子增强	10
S₃∩S₄=0.278	S₃(0.082)+S₄(0.139)=0.221	q>A+B	非线性增强	5
S₃∩S₅=0.197	S₃(0.082)+S₅(0.028)=0.110	q>A+B	非线性增强	7
S₄∩S₅=0.252	S₄(0.139)+S₅(0.028)=0.167	q>A+B	非线性增强	6