外资主导下的汽车制造业空间分布特征及其影响因素——以广州为例
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摘要
以1 069家广州汽车及零部件制造业企业为研究对象,采用GIS和HDBSCAN空间聚类方法分析其空间布局变化及特征,并采用负二项回归模型定量分析影响汽车制造业企业分布的因素,研究发现:(1)广州汽车制造业企业空间集聚特征显著,空间布局演变由近郊区往外围区域扩张;(2)由于长期过于依赖外资整车企业,导致广州汽车制造业产业链存在过于封闭的特征,对汽车零部件企业的辐射带动效应不足;(3)外资和内资汽车零部件企业在空间布局上存在明显差异,外资企业偏向围绕外资整车企业布局,而内资企业偏向沿交通干线布局;(4)广汽传祺、广汽客车等中国品牌对广州汽车零部件企业更具有辐射带动效应,有利于推动技术创新;(5)负二项回归模型显示整车带动、区位因素、集聚经济、人口密度等因素对汽车制造业企业空间布局有重要影响,而交通因素对广州汽车制造业企业布局影响不显著。
Taking 1 069 automobile and parts manufacturing enterprises in Guangzhou as research object, this paper analyzes the spatial distribution evolution of Guangzhou automobile manufacturing industry and influencing factors by means of GIS, HDBSCAN spatial clustering and negative binomial regression model. It is concluded that: 1)Guangzhou's automobile manufacturing enterprises have significant spatial agglomeration characteristics, and their spatial layout evolves from suburban to peripheral areas. 2) Due to long-term overreliance on foreign companies, automobile manufacturing industry chain in Guangzhou is too closed, and the foreign-funded vehicle enterprises have insufficient radiation-induced effects on auto parts enterprises. 3) there are obvious differences in the spatial layout of foreign-funded and domestic-funded auto parts companies, foreign-funded enterprises tend to focus on the layout of foreign-funded vehicle companies, while domestic-funded enterprises tend to focus on the layout along the trunk line. 4) Chinese brand automobile companies including Guangzhou Automobile Group Motor and Guangzhou Automobile Group Autobus have more radiation-driving effects on Guangzhou automobile manufacturing industry and promote technological innovation.5) It, based on the negative binomial regression model, shows that influencing factors such as driving effect of vehicle companies, location factors, agglomeration economy, and population density have an important impact on spatial layout of automobile manufacturing enterprises in Guangzhou. The traffic factor has no significant impact on layout of automobile manufacturing enterprises.
Taking 1 069 automobile and parts manufacturing enterprises in Guangzhou as research object, this paper analyzes the spatial distribution evolution of Guangzhou automobile manufacturing industry and influencing factors by means of GIS, HDBSCAN spatial clustering and negative binomial regression model. It is concluded that: 1)Guangzhou's automobile manufacturing enterprises have significant spatial agglomeration characteristics, and their spatial layout evolves from suburban to peripheral areas. 2) Due to long-term overreliance on foreign companies, automobile manufacturing industry chain in Guangzhou is too closed, and the foreign-funded vehicle enterprises have insufficient radiation-induced effects on auto parts enterprises. 3) there are obvious differences in the spatial layout of foreign-funded and domestic-funded auto parts companies, foreign-funded enterprises tend to focus on the layout of foreign-funded vehicle companies, while domestic-funded enterprises tend to focus on the layout along the trunk line. 4) Chinese brand automobile companies including Guangzhou Automobile Group Motor and Guangzhou Automobile Group Autobus have more radiation-driving effects on Guangzhou automobile manufacturing industry and promote technological innovation.5) It, based on the negative binomial regression model, shows that influencing factors such as driving effect of vehicle companies, location factors, agglomeration economy, and population density have an important impact on spatial layout of automobile manufacturing enterprises in Guangzhou. The traffic factor has no significant impact on layout of automobile manufacturing enterprises.
引文
[1]何婷婷.我国汽车产业空间集聚的实证研究[J].汽车工业研究,2008(3):28-33.
[2]吴铮争,吴殿廷,袁俊,等.中国汽车产业地理集中及其影响因素研究[J].中国人口·资源与环境,2008(1):116-121.
[3]申明浩,隋广军,孙雷.中国汽车产业集群发展的影响因素分析[J].科技管理研究,2008(1):216-219.
[4]王兆宇.中国汽车制造业地理集中实证分析[J].西安建筑科技大学学报:社会科学版,2009,28(3):37-42.
[5]白景锋,周旗,王海洋.基于GIS的我国汽车产业空间布局与市场耦合分析[J].世界地理研究,2011,20(3):112-118.
[6]赵浚竹,孙铁山,李国平,等.中国汽车制造业集聚与企业区位选择[J].地理学报,2014(6):850-862.
[7]虞虎,陆林,朱冬芳.近10年中国汽车工业空间分布格局[J].安徽师范大学学报:自然科学版,2012,35(1):67-72.
[8]丁娟,李俊峰.基于Web地理图片的中国入境游客POI空间格局[J].经济地理,2015,35(6):24-31.
[9]吴芬芳,熊卿.大数据时代地理信息获取与服务方式的嬗变[J].测绘工程,2015,24(10):15-18.
[10]焦汉科,黄悦,曹凯滨.开源GIS研究及应用初探[J].测绘通报,2016(S2):44-48.
[11]张捷.进化算法及智能数据挖掘若干问题研究[D].西安:西安电子科技大学,2013.
[12]韩丽苹.基于数据挖掘的我国区域矿产资源与能源开发利用现状分析研究[D].呼和浩特:内蒙古科技大学,2015.
[13]林冬云.基于空间聚类法的广州市海淀区朝阳区企业分布研究[D].北京:北京师范大学,2005.
[14]夏云.基于聚类方法的产业分布空间数据挖掘研究[D].南京:东南大学,2012.
[15] He Y,Tan H,Luo W,et al. MR-DBSCAN:a scalable Map Reduce-based DBSCAN algorithm for heavily skewed data[J].Frontiers of Computer Science,2014,8(1):83-99.
[16]杨帆,徐建刚,周亮.基于DBSCAN空间聚类的广州市区餐饮集群识别及空间特征分析[J].经济地理,2016,36(10):110-116.
[17] R Campello,D Moulavi and J Sander. Density-Based Clustering Based on Hierarchical Density Estimates[C]//Advances in Knowledge Discovery and Data Mining,Springer,2013:160-172.
[18] L McInnes,J Healy,S Astels,Hdbscan:Hierarchical density based clustering[C]//Journal of Open Source Software,The Open Journal,volume 2,number 11. 2017.
[19] He Y,Tan H,Luo W,et al. MR-DBSCAN:a scalable Map Reduce-based DBSCAN algorithm for heavily skewed data[J].Frontiers of Computer Science,2014,8(1):83-99.
[20] Ankerst M,Breunig MM,Kriegel HP,et al. OPTICS:ordering points to identify the clustering structure[J]. ACM SIGMOD Record,1999,28(2):49-60.
[21] Justin Eldridge,Mikhail Belkin. Beyond Hartigan Consistency:Merge Distortion Metric for Hierarchical Clustering[EB/OL]. https://arxiv.org/pdf/1506.06422v2.pdf,2015.
[22] The hdbscan Clustering Library,Documentation of hdbscan Clustering Library 0.8.12[EB/OL]. http://hdbscan.readthedocs.io/en/latest,2017-11-10.
[23]胡良平.过离散计数资料负二项分布模型回归分析[J].四川精神卫生,2018,31(5):399-404.
[24]郭志刚,巫锡炜.泊松回归在生育率研究中的应用[J].中国人口科学,2006(4):2-15,95.
[25]张倩媛,林晓,朱惠红,等.基于零膨胀模型的江苏省AIDS发病研究[J].数理医药学杂志,2018,31(6):791-793.
[26] QGIS Project,QGIS User Guide[EB/OL]. https://docs.qgis.org/2.18/pdf/en/QGIS-2.18-UserGuide-en.pdf,2017-10-23.
[27]张永凯,徐伟.演化经济地理学视角下的产业空间演化及其影响因素分析[J].世界地理研究,2014,23(2):1-13.
[28]巫细波.新一轮汽车产业开放政策对我国汽车产业的影响及对策建议[J].对外经贸,2018(8):57-60.
(1)“三大三小”指我国汽车工业发展初期重点发展的基地。其中,“三大”指一汽、东风、上汽三大轿车基地,“三小”指北京吉普、天津夏利、广州标致三个小型轿车基地。
[2]吴铮争,吴殿廷,袁俊,等.中国汽车产业地理集中及其影响因素研究[J].中国人口·资源与环境,2008(1):116-121.
[3]申明浩,隋广军,孙雷.中国汽车产业集群发展的影响因素分析[J].科技管理研究,2008(1):216-219.
[4]王兆宇.中国汽车制造业地理集中实证分析[J].西安建筑科技大学学报:社会科学版,2009,28(3):37-42.
[5]白景锋,周旗,王海洋.基于GIS的我国汽车产业空间布局与市场耦合分析[J].世界地理研究,2011,20(3):112-118.
[6]赵浚竹,孙铁山,李国平,等.中国汽车制造业集聚与企业区位选择[J].地理学报,2014(6):850-862.
[7]虞虎,陆林,朱冬芳.近10年中国汽车工业空间分布格局[J].安徽师范大学学报:自然科学版,2012,35(1):67-72.
[8]丁娟,李俊峰.基于Web地理图片的中国入境游客POI空间格局[J].经济地理,2015,35(6):24-31.
[9]吴芬芳,熊卿.大数据时代地理信息获取与服务方式的嬗变[J].测绘工程,2015,24(10):15-18.
[10]焦汉科,黄悦,曹凯滨.开源GIS研究及应用初探[J].测绘通报,2016(S2):44-48.
[11]张捷.进化算法及智能数据挖掘若干问题研究[D].西安:西安电子科技大学,2013.
[12]韩丽苹.基于数据挖掘的我国区域矿产资源与能源开发利用现状分析研究[D].呼和浩特:内蒙古科技大学,2015.
[13]林冬云.基于空间聚类法的广州市海淀区朝阳区企业分布研究[D].北京:北京师范大学,2005.
[14]夏云.基于聚类方法的产业分布空间数据挖掘研究[D].南京:东南大学,2012.
[15] He Y,Tan H,Luo W,et al. MR-DBSCAN:a scalable Map Reduce-based DBSCAN algorithm for heavily skewed data[J].Frontiers of Computer Science,2014,8(1):83-99.
[16]杨帆,徐建刚,周亮.基于DBSCAN空间聚类的广州市区餐饮集群识别及空间特征分析[J].经济地理,2016,36(10):110-116.
[17] R Campello,D Moulavi and J Sander. Density-Based Clustering Based on Hierarchical Density Estimates[C]//Advances in Knowledge Discovery and Data Mining,Springer,2013:160-172.
[18] L McInnes,J Healy,S Astels,Hdbscan:Hierarchical density based clustering[C]//Journal of Open Source Software,The Open Journal,volume 2,number 11. 2017.
[19] He Y,Tan H,Luo W,et al. MR-DBSCAN:a scalable Map Reduce-based DBSCAN algorithm for heavily skewed data[J].Frontiers of Computer Science,2014,8(1):83-99.
[20] Ankerst M,Breunig MM,Kriegel HP,et al. OPTICS:ordering points to identify the clustering structure[J]. ACM SIGMOD Record,1999,28(2):49-60.
[21] Justin Eldridge,Mikhail Belkin. Beyond Hartigan Consistency:Merge Distortion Metric for Hierarchical Clustering[EB/OL]. https://arxiv.org/pdf/1506.06422v2.pdf,2015.
[22] The hdbscan Clustering Library,Documentation of hdbscan Clustering Library 0.8.12[EB/OL]. http://hdbscan.readthedocs.io/en/latest,2017-11-10.
[23]胡良平.过离散计数资料负二项分布模型回归分析[J].四川精神卫生,2018,31(5):399-404.
[24]郭志刚,巫锡炜.泊松回归在生育率研究中的应用[J].中国人口科学,2006(4):2-15,95.
[25]张倩媛,林晓,朱惠红,等.基于零膨胀模型的江苏省AIDS发病研究[J].数理医药学杂志,2018,31(6):791-793.
[26] QGIS Project,QGIS User Guide[EB/OL]. https://docs.qgis.org/2.18/pdf/en/QGIS-2.18-UserGuide-en.pdf,2017-10-23.
[27]张永凯,徐伟.演化经济地理学视角下的产业空间演化及其影响因素分析[J].世界地理研究,2014,23(2):1-13.
[28]巫细波.新一轮汽车产业开放政策对我国汽车产业的影响及对策建议[J].对外经贸,2018(8):57-60.
(1)“三大三小”指我国汽车工业发展初期重点发展的基地。其中,“三大”指一汽、东风、上汽三大轿车基地,“三小”指北京吉普、天津夏利、广州标致三个小型轿车基地。