地理科学  2018 , 38 (5): 691-698 https://doi.org/10.13249/j.cnki.sgs.2018.05.006

Orginal Article

宁波市港口物流企业空间格局及区位选择

王瑞, 蒋天颖, 王帅

宁波财经学院金融贸易学院,浙江 宁波 315175

Spatial Pattern and Location Selection of Port Logistics Enterprises in Ningbo

Wang Rui, Jiang Tianying, Wang Shuai

School of Finance & Trade, Ningbo University of Finance and Economics, Ningbo 315175, Zhejiang, China

中图分类号:  F129.9

文献标识码:  A

文章编号:  1000-0690(2018)05-0691-08

通讯作者:  通讯作者:王帅,讲师。E-mail: siyueqingfeng0086@163.com

收稿日期: 2017-06-20

修回日期:  2018-04-15

网络出版日期:  2018-05-10

版权声明:  2018 《地理科学》编辑部 本文是开放获取期刊文献,在以下情况下可以自由使用:学术研究、学术交流、科研教学等,但不允许用于商业目的.

基金资助:  国家自然科学基金(71372001)、教育部人文社会科学研究一般项目(17YJCZH176)、浙江省自然科学基金一般项目(LY18D010006)、浙江省哲社规划重点课题(17NDJC009Z)、宁波市软科学课题(2014A10084)资助

作者简介:

作者简介:王瑞(1978-),男,浙江宁波人,博士,教授,主要从事区域经济、国际贸易地理研究。E-mail: everrui@163.com

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摘要

以宁波市为例,基于港口物流企业空间数据,运用冷热点分析和泊松回归等方法,研究宁波市港口物流企业空间分布特征及影响因素。结果发现:① 宁波市港口物流企业呈现向心集聚性,圈层分布格局明显。中心商务区和临港工业区是集聚热点区域;②港口物流企业集聚特征明显但整体集聚程度下降,表现出在中心商务区和临港工业区持续集聚和沿主干道路向外围区域蔓延扩散两大趋势;③不同类型港口物流企业的区位选择有明显差异:货代型物流企业主要分布在中心商务区,仓储型物流企业主要临港分布,运输型企业选择临近沿交通干道布局,综合型物流企业的分布没有明显的区位倾向;④ 政策因素、土地价格、城乡差异、临近港口、经济潜力和工业结构都不同程度地影响港口物流企业的空间分布,而交通便利没有表现出相关性。

关键词: 港口物流企业 ; 空间格局 ; 区位选择 ; 宁波市

Abstract

With the rapid development of the modern logistics industry dominated by the port logistics in China's coastal cities, researches on the characteristics of spatial distribution and laws of evolution of the port logistics companies inside cities are beginning to appear. However, the existing research has not further studied the factors affecting the location selection of the port logistics companies. In this article, based on the data of location information of Ningbo port logistics companies, the characteristics of spatial distribution and factors that affect the location selection of the port logistics companies are analyzed. The differences of the locational characteristics between the different types of port logistics companies are identified. It enriches the case studies of the locational analysis of the port logistics companies in the coastal cities. Taking Ningbo as an example, this article employed the Kernel Density Estimation, Getis-ord Gi analysis and Poisson regression models to analyze data, and the results showed that: 1) The port logistics companies in Ningbo show centripetal concentration, and the spatial distribution of enterprises presents a tendency to decrease from the central city, the periphery to the suburbs, and the distribution pattern of the circle is obvious. Central business district and port industrial area are the popular areas for port logistics enterprises to gather. 2) At present, port logistics enterprises have obvious agglomeration characteristics, but the overall agglomeration degree is decreasing. At the same time, there are two trends, which are centralization and diffusion of central business district and port industrial area. 3) It is found that there are obvious differences in location choice of different types of port logistics companies: a) The freight forwarder logistics companies are mainly distributed in the central business area; b) The logistics business companies of warehouse are mainly distributed in port; c) The transportation logistics companies often choose the layout along the main road, because they mainly undertake land container transportation, the locational selection has traffic dependency; d) Compared with the other three types, the distribution of integrated logistics companies has no obvious locational tendencies. 4) Regarding the factors affecting the spatial distribution of Ningbo port logistics companies, the research shows that the policy factors, the price of land, the differences between urban and rural areas, the distance from the port, the economic potential of the region and its industrial structure all affect the spatial distribution of port logistics enterprises in varying degrees. However, there is no correlation with traffic convenience, and the main reason is that the road network construction in Ningbo is relatively perfect. At the same time, the article put forward the corresponding measures from three different aspects: first, strengthening top-level design in logistics industry; second, exploiting the regional advantages to form a park that integrates port logistics and port industry advantages;third, optimizing the infrastructure such as the traffic network deeply.

Keywords: port logistics enterprises ; spatial pattern ; location selection ; Ningbo

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王瑞, 蒋天颖, 王帅. 宁波市港口物流企业空间格局及区位选择[J]. 地理科学, 2018, 38(5): 691-698 https://doi.org/10.13249/j.cnki.sgs.2018.05.006

Wang Rui, Jiang Tianying, Wang Shuai. Spatial Pattern and Location Selection of Port Logistics Enterprises in Ningbo[J]. Scientia Geographica Sinica, 2018, 38(5): 691-698 https://doi.org/10.13249/j.cnki.sgs.2018.05.006

近年来,物流设施空间分布及其区位选择成为交通地理学研究的热点[1,2,3]。20世纪90年代以来,国内学者在借鉴和学习国外研究的基础上开始物流地理学的研究[4],但大都集中在宏观尺度研究,如区域物流业发展与空间组织研究[5,6,7]。随着企业位置信息可获得性的提高,从中微观尺度对城市内部物流企业的空间格局及演变特征的研究开始丰富[8,9,10,11]。近年来中国沿海城市以港口物流为主导的现代物流业发展迅速,国内关于城市内部港口物流企业空间分布特征及演化规律的研究也开始出现[12,13,14]。但总体来看,目前对沿海城市内部港口物流企业空间分布特征的研究还很少,且现有研究都没有进一步探讨港口物流企业区位选择的影响因素。

宁波作为中国东南沿海著名的港口城市,2017年宁波舟山港集装箱吞吐量达2 460.7万标箱,成功蝉联全球第四大集装箱港口;货物吞吐量突破10亿t,成为全球首个“10亿t大港”[9]。2017年全市共有物流相关企业近一万家,基本形成以港口物流为龙头,制造业物流、城乡配送物流、航空物流、专业物流等为配套的发展格局,港口物流业发展具有一定的典型性和代表性。本文利用宁波市港口物流企业位置信息数据,对港口物流企业的空间分布特征及影响企业区位选择的因素进行分析,明确不同类型港口物流企业的区位特征差异,丰富沿海城市港口物流企业区位分析的案例研究。

1 研究方法与数据来源

1.1 研究方法

1) 冷热点分析。热点分析可探测事件在空间分布的非随机性,计算出事件发生高频率的热点区域。在研究宁波市港口物流企业空间特征时,采用空间热点探测分析港口物流企业在空间上的聚集区域。计算公式如下:

Gi*(d)=j=1nwij(d)xjj=1nxj(1)

式中, Gi*(d)为热点分析Getis-Ord值,用于分析属性值在局部空间水平上的集聚程度; xj表示各区域样本观测值, wij为空间权重矩阵,若第i和第j个空间单元之间的距离位于给定的临界距离d之内,认为它们是邻居,空间权重矩阵中的元素为1;否则,元素为0,n为研究区域单元总数。

对其进行标准化处理,公式(1)转化为:

Z[Gi*(d)]=Gi*d-E[Gi*d]Var[Gi*(d)](2)

式中, E[Gi*(d)]Var[Gi*(d)]表示 Gi*(d)的数学期望和方差。得到的 Z[Gi*(d)]值显著,且为正,表明在研究期内街道(乡镇)i及其周围地区港口物流企业数量多,属于高值空间集聚区域,也就是热点区;反之 Z[Gi*(d)]值显著,并为负,表明在研究期间内街道(乡镇)i及其周围地区港口物流企业数量少,属于低值空间集聚区域,也就是冷点区。

2) 泊松回归模型。通过对宁波市域进行1 km×1 km网格划分,本文以落入网格内的港口物流企业数量为因变量,以影响要素为自变量构建企业区位选择因素分析模型。对于表示数量的因变量,取值范围较小,且为非负整数值,在统计中称为计数变量,可构建基于泊松分布的回归模型。假设第i个空间单元内观测到的企业数Yi服从参数为λi的泊松分布,则某一空间单元内观测到的企业数为yi的概率(P)为:

PYi=yi|Xi=λiyiyi!e-λi(yi=0,1,2)(3)

λi=eβXi(4)

式中, Xi是可能影响企业区位选择的因素,λi取决于解释变量 Xi;β是变量的回归系数向量,其极大似然估计量可通过如下对数似然函数得到:

Lβ=i=1n[yilnλi-λi-ln(yi!)](5)

泊松回归模型一个关键假定是因变量的条件均值和条件方差相等,且等于λi。但是这个假设在实际应用中很难满足,如果条件均值小于条件方差,即样本中出现过度分散的问题,将导致泊松回归模型结果出现偏差,需要对泊松回归模型进行修正。负二项分布模型允许因变量的方差超过平均值。通常采用负二项回归模型代替得到β的准极大似然估计[15]

1.2 数据来源

宁波港口物流企业名单及地址来自宁波港口物流信息平台,并结合中国港口网物流企业数据库进行补充调整,共收集2 135家企业数据(企业注册资本均在100万以上),企业注册资本及成立时间数据来自启信宝。经济社会发展数据均来自《宁波统计年鉴2017》[16],地价数据来源于宁波市公布的基准地价。

2 宁波市港口物流企业总体分布特征

2.1 向心集聚且圈层分布格局明显

将宁波市分为核心区、外围区和郊区3个地域层次,从表1可以看出,宁波港口物流企业主要分布在核心区,即宁波老三区,2016年港口物流企业有1 181家,占全市的55.32%,集中分布趋势明显;外围区港口物流企业数量为825家,占全市的38.64%,其中北仑区由于临港优势,企业分布较多,有587家,占比达到27.50%;郊区包括一区两市两县,有港口物流企业129家,占比6.04%,呈现从中心城区到郊区递减的趋势。

表1   宁波市港口物流企业区域分布情况

Table 1   Distribution of port logistics enterprises in Ningbo

2000年2005年2010年2016年
中心区数量(个)241966091181
占比(%)30.0047.2353.7055.32
外围区数量(个)54213496825
占比(%)67.5051.3343.7438.64
郊区数量(个)2629129
占比(%)2.501.452.566.04

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2.2 单中心集聚格局明显

以街道为单位分析宁波港口物流企业的空间分布情况,发现三江口片区企业分布密度较高,这一地区也是宁波传统商业活动集中区域(图1a)。进一步运用核密度分析对宁波港口物流企业进行空间集聚分析,得到港口物流企业聚集区分布图(图1b),发现港口物流企业布局呈现显著的单中心结构,中心商务区是物流企业集聚的主要区域,但临港工业区有成为另一个集聚中心的潜力。其中,一级集聚区位于中心区内三区交界地带,是宁波港口物流企业集聚的核心地带,也是宁波传统的城市中心区域;二级集聚区基本沿一级集聚区向外扩散,大致在宁波环城南路、环城北路、环城西路和世纪大道围绕范围,属于现在城市中心区域;除了这两个显著的集聚区外,在北仑区靠宁波港地区也形成了低等级的企业集聚区。

图1   宁波港口物流企业冷热点分布(a)及核密度分析(b)

Fig.1   Hotspot zones (a) and KED analysis (b) of port logistics enterprises in Ningbo

2.3 中心持续集聚和向外蔓延扩散趋势并存

2005年之前,宁波港口物流企业主要分布在市区范围,周边县市企业分布较少,中心商务区和临港工业区作为企业主要聚集区已经形成(图2a)。2005年之后,港口物流企业的分布表现出在中心商务区和临港工业区持续集聚和沿主干道路向外围区域蔓延扩散两大趋势(图2b)。总体上看,宁波港口物流企业空间分布集聚性增强,中心商务区和临港工业区集聚企业不断增多,但2010年以后集聚性下降(图2c、d)。结合标准椭圆面积变化,发现2016年港口物流企业标准差椭圆面积相比2010年增加,意味着港口物流企业分布重心的集聚程度减弱,整体有着偏离城市中心区扩散的趋向,出现郊区化。未来随着慈溪综合物流园区的建设,宁波北部区域会形成新的集聚热点。

图2   宁波市港口物流企业空间分布演化

Fig.2   The evolution of spatial distribution of port logistics enterprises in Ningbo

2.4 不同类型港口物流企业空间分布特征差异明显

把宁波港口物流企业划分成货代型、仓储型、运输型和综合型港口物流企业。从不同类型企业的空间分布上可以明显看出(图3),货代型物流企业因其不需要直接从事货物运输或仓储服务,属于中介服务性质的企业,因此主要分布在中心商务区,集聚有587家企业,占全部货代型物流企业的82.56%;仓储型物流企业因为需要临时仓储,部分具有保税业务,同时又要方便转运,主要临港分布,集中在北仑区;运输型物流企业因业务主要是承担陆路集装箱运输,分布具有明显的交通指向,在一级道路500 m缓冲区内有165家企业,占全部运输型物流企业的47.14%,对道路通道性的要求较高。同时,还表现出明显的运输目的地指向性;综合型物流企业是承担两项及以上主要业务的企业,如承担国际货运代理业务的同时,还承接陆路运输业务,这类企业在市区内部明显集聚在中心商务区和临港工业区,在外围地区则沿主干道路分布趋势明显。

图3   不同类型港口物流企业的空间分布

Fig.3   Distribution of different types of port logistics enterprises in Ningbo

3 宁波港口物流企业区位选择的影响因素分析

3.1 影响因素选择

选择政策因素、土地价格、城乡差异、经济潜力、工业结构、交通便利等6个影响因素,综合分析其对宁波港口物流企业区位选择的影响。具体解释变量及定义如下。

政策因素(policy):政府政策对物流企业区位选择的影响主要是通过规划建设开发区和特色物流园区等引导港口物流企业空间布局。选择目前宁波市已建成的开发区和物流园区作为政策影响区域,设置虚拟变量,如果物流企业在政策范围区内设置为1,否则为0。土地价格(landprice):企业经营需要考虑用地成本和房屋租金,为了减少企业经营成本,企业会倾向选择地价相对较低的区域。根据宁波市城市土地定级和基准地价图把宁波市土地价格划分为3类,设置虚拟变量,分别赋值为1、2、3。城乡差异(region):城区和郊区由于经济发展程度、交通等基础设施建设方面存在差异,预期城乡差异与港口物流企业网点密度之间也存在相关关系。引入虚拟变量,设定中心区为1,外围区为2,郊区为3。考虑到城乡差异因素和土地价格因素的在影响机制上可能产生的重复性,在模型中需要进行共线性检验。港口因素(port):考虑到港口物流企业和港口的关系,引入企业是否靠近港口变量,设定靠近港口为1,否则为0;经济潜力(lnpergdp):一个地区企业的发展必然受区域经济发达程度的影响,利用地区人均GDP数据(取对数)表征区内经济发展潜力。工业结构(indu):考虑到港口物流企业主要服务工业企业的性质,区域内工业结构水平和港口物流企业的集聚水平存在相关性。以第二产业增加值占地区GDP的比重表示区域工业结构水平。交通便利(density):物流企业的分布表现明显的交通依赖性,良好的交通通达性可以降低物流企业的交通运输成本。采用网格内的路网密度表征该区域的交通便利性。

为了尽可能使一个网格中有一个企业分布,采用1 km×1 km对宁波市进行网格划分。以落入网格内的港口物流企业数量为因变量,以区位影响要素为自变量构建企业区位影响因素分析模型。

3.2 结果分析

模型有效样本共计312个,对解释变量进行相关性检验,发现土地价格(landprice)与城乡差异(region)之间具有较高的相关性,为消除多重共线性影响,将变量分别引入回归模型进行估计。首先进行泊松回归分析,因拟合优度检验中得到泊松回归模型的结果不满足变量均值与方差相等的假设,故进一步用负二项回归模型对泊松回归模型进行修正,计算结果如表2所示。

表2   港口物流企业区位选择回归模型估计结果

Table 2   Estimation results of port logistics enterprises location selection model in Ningbo

变量模型1模型2
policy0.2401**0.2248**
landprice-0.6432***
region-0.4091**
port0.1664**0.1974**
lnpergdp0.8904***0.9634***
indu0.4301**0.4360**
density0.17910.1813
常数项7.2023**8.0619**
Pseudo R20.57380.5361

注:*****分别表示在5%和1%的水平上显著;“–”表示未出现此项。

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在两个模型中政策因素都表现出了显著的正相关,说明政府通过划定各类政策区对港口物流企业的区位选择有明显的引导作用。一方面,2015年宁波市城市总体规划提出“一核两翼、两带三湾”的空间发展格局,其中明确“中心区”宁波市区和“北翼”包括余姚市、慈溪市和杭州湾新区等地重点发展商贸物流,建设现代物流基地。2016年宁波市区和慈溪、余姚两市共有788家港口物流企业,占全部港口物流企业的98.99%,符合规划提出的重点发展物流产业的发展定位。另一方面,“十二五”期间,宁波规划布局了9大综合物流园区,其中梅山保税港区物流园区、镇海大宗货物海铁联运物流枢纽港、宁波经济技术开发区现代国际物流园的规划建设,对引导宁波港口物流企业集聚发展具有显著影响。

分别引入土地价格和城乡差异因素,发现两者都表现显著相关性,即港口物流企业倾向于选择在地价较低的地区布局,这类地区往往就是郊区。但对比宁波港口物流企业分布发现,其在中心城区集聚分布的态势明显,这是因为大部分货代型物流企业和部分综合型物流企业因其所需办公面积较小,土地租金对其影响不太明显。特别是货代型物流企业,其在中心商务区布局可以更好的享受良好的设施资源,集聚性更明显。但具有仓储业务的物流企业,由于占地面积较大,会随着城市的拓展逐渐向城市边缘地价较低的地区迁移。港口对港口物流企业的区位选择也表现正向影响,说明港口物流企业倾向于在邻近港口布局。

经济潜力和工业结构因素都表现显著的正向影响,说明区域经济环境和工业结构都会对港口物流企业区位选择产生影响。一方面,物流业是一种典型的需求导向型产业,外部经济发展环境直接影响物流业务需求;另一方面,物流业作为融合运输、仓储、货运代理和信息等行业的生产性服务业,与制造业联系最为紧密,物流业与制造业在空间上的协同集聚趋势也日益明显。港口物流业作为一类依托港口中转运输的物流业,其发展程度也和地区经济以及工业结构水平具有明显相关性。

交通便利没有表现出显著相关性。这是由于宁波市交通基础设施相对完善,在每个1 km×1 km网格内的路网密度相差不大,导致相关性分析不显著,但这并不否认交通便利性对物流企业区位选择的影响。从一级道路500 m缓冲区与宁波港口物流企业分布关系来看,港口物流企业还是倾向于靠近主要交通干道布局。但以后随着城市交通网络的进一步完善以及交通运输费用的进一步下降,是否靠近道路、是否靠近港口,对物流企业区位选择的影响会明显降低,而地价因素会进一步促使物流企业向郊区迁移。

4 结论

本文对宁波市港口物流企业的空间分布特征进行研究,并进一步探索港口物流企业区位选择的影响因素,得到以下结论:

1) 宁波市港口物流企业呈现向心集聚性,企业分布呈现从中心城区、外围区到郊区递减的趋势,圈层分布格局明显。中心商务区和临港工业区是港口物流企业集聚的热点区域。结合时间变化来看,港口物流企业集聚特征明显但整体集聚程度下降,表现出在中心商务区和临港工业区持续集聚和沿主干道路向外围区域蔓延扩散并存的趋势。

2) 不同类型港口物流企业的区位选择有明显差异。货代型物流企业主要分布在中心商务区,享受服务业集聚产生的效益;仓储型物流企业为方便转运、仓储,主要临港分布;运输型物流企业主要是承担陆路集装箱运输,区位选择具有交通依附性;综合型物流企业兼具两项及以上主营业务,在市区内部明显集聚在中心商务区和临港工业区,在外围地区则沿主干道路分布趋势明显。

3) 从宁波港口物流企业空间分布的影响因素来看,政策因素、土地价格、城乡差异、经济潜力、工业结构都不同程度影响港口物流企业的区位分布,而交通便利没有表现出显著相关性。

根据上述研究结论,提出以下政策建议:一是加强顶层设计,制定港口物流产业发展规划,完善物流园区布局,实现功能错位发展,以园区建设推动区域物流资源整合,提升地区物流集约化水平。引导具有仓储、运输、保税、国际采购、区域配送等业务的物流企业向港口工业区域集聚,加强主营货代、船代、物流金融等国际物流企业向城市中心商务区集聚,优化港口物流产业功能布局,提升集聚功能;二是发挥宁波舟山港航运优势、宁波保税区政策优势和临港产业集群优势,深化区港一体的物流运作优势,形成港口物流与临港产业优势融合的港口综合型物流园区,在土地价格、投资政策、税收减免等方面向园区内港口物流企业倾斜,着力培育港口物流龙头企业,打造成为功能前沿、总部集聚、联动紧密的物流贸易一体化运作平台;三是继续优化交通路网等基础设施,完善功能配套服务,地方政府要重视基础设施建设中信息化和人工智能应用程度。提高行业应用智慧物流网络及物联网与互联网融合水平,引进高端港口物流人才,构建线上和线下结合的港口腹地及中转物流需求的通道网络。

The authors have declared that no competing interests exist.


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https://doi.org/10.3321/j.issn:0375-5444.2008.02.003      URL      [本文引用: 1]      摘要

物流企业是承担物流活动的专业化组织,是具有空间网络的企业形式。基于相关研究的评述,分析了物流企业网络的基础理论,重点解析其空间体系、职能联系与运营机制。通过研究,认为物流企业由企业属性要素和物流属性要素组成,其共同作用形成企业部门的职能分化和区位分离,由此形成物流企业网络,包括企业组分网络和物流运营网络。物流企业在城市和区域尺度构筑企业组分网络,城市网络包括总公司、配送中心和营业点,总公司布局于市中心区,配送中心倾向于近郊区,营业点集中在物流密集区;区域网络包括总部、区域分公司、地方分公司、办事处和受理点,总部布局于国内大城市,区域分公司倾向于各区政治经济中心,地方分公司集中在省会、经济中心和交通枢纽,办事处与受理点在企业网络外部或内部空白处布局。物流运营网络由运输专线、配送体系和物流网络组成,运输专线形成支线和干线两类,是物流运营的基本途径;配送体系形成区域、城市区域和城市三层,是物流企业提升竞争力的保障:运输专线和配送体系的交错、融合和优化,最终形成轴辐物流网络。
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[J]. 地理研究,2011, 30(11): 1997-2007.

https://doi.org/10.11821/yj2011110006      URL      [本文引用: 1]      摘要

This paper studies spatial pattern and evolution location of urban logistics enterprises and reveals its general laws. Through establishing logistics space and attribute database of Suzhou during 1990-2007, with GIS spatial correlation analysis module and visualization, this paper applies quadrat, spatial autocorrelation and hotspot analysis to study urban logistics geographic distribution and evolution. The research shows the following results. (1) In recent 20 years, generally the logistics enterprise distribution has been in a state of agglomeration, but as time goes by, the decline of BPI marks a weakening tendency, for the logistics enterprise distribution expands from few intensive centers to other aggregation centers, lacking global higher and lower value centers. Thus, the distribution displays evolution and migration. (2) Globally, the hotspot of logistics enterprise distribution shows apparent expansion and migration. First, the area of hotspot evolves from singleness to multiplicity, and expands from downtown to sub-center towns. Second, the spatial expansion of hotspots shows the striking characteristics of being along the roads (G312 National Road and Shanghai-Nanjing Road) and river (Yangtze River). (3) Asset and non-asset logistics enterprises have significant differences in spatial distribution and pattern evolution: asset logistics hotspots have weakened in the downtown, further expansion and centrifugation, while non-asset logistics hotspots are mainly in the downtown, with obvious centralization and distribution along G312 National Road.

[Cao Weidong.

Spatial pattern and location evolution of urban logistics enterprises: taking Suzhou as an example

. Geographical Research, 2011, 30(11):1997-2007.]

https://doi.org/10.11821/yj2011110006      URL      [本文引用: 1]      摘要

This paper studies spatial pattern and evolution location of urban logistics enterprises and reveals its general laws. Through establishing logistics space and attribute database of Suzhou during 1990-2007, with GIS spatial correlation analysis module and visualization, this paper applies quadrat, spatial autocorrelation and hotspot analysis to study urban logistics geographic distribution and evolution. The research shows the following results. (1) In recent 20 years, generally the logistics enterprise distribution has been in a state of agglomeration, but as time goes by, the decline of BPI marks a weakening tendency, for the logistics enterprise distribution expands from few intensive centers to other aggregation centers, lacking global higher and lower value centers. Thus, the distribution displays evolution and migration. (2) Globally, the hotspot of logistics enterprise distribution shows apparent expansion and migration. First, the area of hotspot evolves from singleness to multiplicity, and expands from downtown to sub-center towns. Second, the spatial expansion of hotspots shows the striking characteristics of being along the roads (G312 National Road and Shanghai-Nanjing Road) and river (Yangtze River). (3) Asset and non-asset logistics enterprises have significant differences in spatial distribution and pattern evolution: asset logistics hotspots have weakened in the downtown, further expansion and centrifugation, while non-asset logistics hotspots are mainly in the downtown, with obvious centralization and distribution along G312 National Road.
[9] 蒋天颖, 伍婵提, 陈改改.

浙江省A级物流企业时空格局特征研究

[J]. 地理科学, 2017, 37(11):1720-1727.

URL      [本文引用: 2]      摘要

基于浙江省2005-2015年A级物流企业时空数据,运用Arc GIS10.0和Crime Stats 3.3软件从时间和空间视角分析了浙江省A级物流企业空间格局特征及形成机制。研究表明:浙江省A级物流企业在时间序列上分为起步阶段、成长阶段和成熟阶段,各等级企业发展不平衡;从空间上来看,A级物流企业呈现明显的东北部密集、西南部稀疏的分布趋势,最近邻指数表明全省A级物流企业的空间分布集聚强度依次为:总体〉3A〉4A〉2A〉5A〉1A;多尺度研究表明不同等级的物流企业在不同空间尺度上的分布格局以集聚分布形态为主,随机分布为辅,集聚程度趋势随尺度变化而变化;热点探测分析表明A级物流企业区位导向为长三角南翼经济发达区、沿海港口城市、内陆交通枢纽三大地带。

[Jiang Tianying, Wu Chanti, Chen Gaigai.

Spatio-temporal pattern of Zhejiang A-class logistics enterprise

. Scientia Geographica Sinica, 2017, 37(11): 1720-1727.]

URL      [本文引用: 2]      摘要

基于浙江省2005-2015年A级物流企业时空数据,运用Arc GIS10.0和Crime Stats 3.3软件从时间和空间视角分析了浙江省A级物流企业空间格局特征及形成机制。研究表明:浙江省A级物流企业在时间序列上分为起步阶段、成长阶段和成熟阶段,各等级企业发展不平衡;从空间上来看,A级物流企业呈现明显的东北部密集、西南部稀疏的分布趋势,最近邻指数表明全省A级物流企业的空间分布集聚强度依次为:总体〉3A〉4A〉2A〉5A〉1A;多尺度研究表明不同等级的物流企业在不同空间尺度上的分布格局以集聚分布形态为主,随机分布为辅,集聚程度趋势随尺度变化而变化;热点探测分析表明A级物流企业区位导向为长三角南翼经济发达区、沿海港口城市、内陆交通枢纽三大地带。
[10] 蒋天颖, 史亚男.

宁波市物流企业空间格局演化及影响因素

[J]. 经济地理,2015, 35(10): 130-138.

[本文引用: 1]     

[Jiang Tianying, Shi Yanan.

The spatial pattern evolution and influencing factors of logistics enterprises in Ningbo

. Economic Geography, 2015, 35(10): 130-138.]

[本文引用: 1]     

[11] 千庆兰, 陈颖彪, 李雁, .

广州市物流企业空间布局特征及其影响因素

[J]. 地理研究, 2011, 30(7): 1254-1261.

https://doi.org/10.11821/yj2011070011      URL      [本文引用: 1]      摘要

The article is presented based on the material of inspecting 3771 logistics enterprises in Guangzhou at first hand. It uses spatial analysis method of GIS to investigate the spatial distribution of logistics enterprises in Guangzhou City, and reveals the main factor affecting its spatial structure. The result shows that the spatial distribution of logistics enterprises in Guangzhou has the centripetal characteristic that the number of logistics enterprises obviously gradually decreases from the downtown to the suburban area and the peripheral county level cities; logistics enterprises present axial distribution along the major and minor trunk roads. And the selection of logistics enterprises zone has service dependence; urban industrial areas and business districts are important gathering areas of logistics enterprises. The spatial structure of logistics enterprises is the result of comprehensive function of location, transportation, policy, regional economic strength and land price.

[Qian Qinglan, Chen Yingbiao, Li Yan et al.

Spatial distribution of logistics enterprises in Guangzhou and its influencing factors

. Geographical Research, 2011, 30(7):1254-1261.]

https://doi.org/10.11821/yj2011070011      URL      [本文引用: 1]      摘要

The article is presented based on the material of inspecting 3771 logistics enterprises in Guangzhou at first hand. It uses spatial analysis method of GIS to investigate the spatial distribution of logistics enterprises in Guangzhou City, and reveals the main factor affecting its spatial structure. The result shows that the spatial distribution of logistics enterprises in Guangzhou has the centripetal characteristic that the number of logistics enterprises obviously gradually decreases from the downtown to the suburban area and the peripheral county level cities; logistics enterprises present axial distribution along the major and minor trunk roads. And the selection of logistics enterprises zone has service dependence; urban industrial areas and business districts are important gathering areas of logistics enterprises. The spatial structure of logistics enterprises is the result of comprehensive function of location, transportation, policy, regional economic strength and land price.
[12] 陈再齐, 闫小培, 曹小曙.

广州市港口服务业空间特征及其形成机制研究

[J]. 地理科学, 2010, 30(1):30-38.

URL      Magsci      [本文引用: 1]      摘要

The roles of a port are of importance in the local economy, which has drawn attentions in the many studies. Yet there are relatively few studies on the port service industries. The spatial characters and associated mechanism of the port service industries in Guangzhou were focused on. The pot service industries in Guangzhou wase highly spatial concentrated and Zhujiang-dependence. The spatial concentrating was steady while the shifting of enterprises was very frequent. The spatial distribution of the port service industries varied greatly with the different type. In general, it was apt to be concentrated to the trading port and the CBD. The spatial character is an synthesized outcome of the location, the nature of the industries, the evolution of the port spatial system, the urban planning influences and innovation of ICT.

[Chen Zaiqi, Yan Xiaopei, Cao Xiaoshu.

The spatial characters and associated mechanism of port service industries in Guangzhou

. Scientia Geographica Sinica, 2010, 30(1):30-38.]

URL      Magsci      [本文引用: 1]      摘要

The roles of a port are of importance in the local economy, which has drawn attentions in the many studies. Yet there are relatively few studies on the port service industries. The spatial characters and associated mechanism of the port service industries in Guangzhou were focused on. The pot service industries in Guangzhou wase highly spatial concentrated and Zhujiang-dependence. The spatial concentrating was steady while the shifting of enterprises was very frequent. The spatial distribution of the port service industries varied greatly with the different type. In general, it was apt to be concentrated to the trading port and the CBD. The spatial character is an synthesized outcome of the location, the nature of the industries, the evolution of the port spatial system, the urban planning influences and innovation of ICT.
[13] 曹卫东.

港航企业区位特征及其空间关联——以上海港口后勤区为例

[J]. 地理研究, 2012, 31(6):1079-1088.

https://doi.org/10.11821/yj2012060011      URL      [本文引用: 1]      摘要

港航企业的行为空间研究正成为现代港口地理学新的研究热点。以上海港口后勤区为例,建立港航企业空间属性数据库,收集各区县制造业、仓储业、商业、商务办公业的企业数据以及对应的建筑面积数据,综合运用空间基尼系数、GIS空间统计与插值以及地理联系率三种研究方法,从中微观尺度刻画上海港口后勤区主要港航企业的区位特征以及空间关联。研究表明:现阶段上海港口后勤区港航企业以广域(市域)分布为主,总体呈现明显的空间聚集特征;不同类型港航企业的区位选择具有明显的差异性,港航企业多以中心城区为核心、沿黄浦江空间集聚明显,而无船承运企业则呈现大分散小集聚布局;港航企业区位选择与制造业具有空间可分性,而国际集装箱运输企业地域依赖性明显。

[Cao Weidong.

Study on location features and spatial correlation of port enterprises: taking Shanghai Port Backup Area as an example

. Geographical Research, 2012, 31(6):1079-1088.]

https://doi.org/10.11821/yj2012060011      URL      [本文引用: 1]      摘要

港航企业的行为空间研究正成为现代港口地理学新的研究热点。以上海港口后勤区为例,建立港航企业空间属性数据库,收集各区县制造业、仓储业、商业、商务办公业的企业数据以及对应的建筑面积数据,综合运用空间基尼系数、GIS空间统计与插值以及地理联系率三种研究方法,从中微观尺度刻画上海港口后勤区主要港航企业的区位特征以及空间关联。研究表明:现阶段上海港口后勤区港航企业以广域(市域)分布为主,总体呈现明显的空间聚集特征;不同类型港航企业的区位选择具有明显的差异性,港航企业多以中心城区为核心、沿黄浦江空间集聚明显,而无船承运企业则呈现大分散小集聚布局;港航企业区位选择与制造业具有空间可分性,而国际集装箱运输企业地域依赖性明显。

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