中国春运城际出行网络结构特征与城市角色识别——基于多元交通客流的测度

删除或更新信息，请邮件至freekaoyan#163.com(#换成@)

本站小编 Free考研考试/2021-12-29

张宇^,¹, 曹卫东^,¹, 梁双波², 任亚文³^,⁴

1. 安徽师范大学地理与旅游学院,芜湖 241002

2. 中国科学院南京地理与湖泊研究所,南京 210008

3. 中国科学院地理科学与资源研究所,北京 100101

4. 中国科学院大学资源与环境学院,北京 100049

Structure characteristics of intercity travel network and identification of city role during the Spring Festival travel rush in China: Based on the measurement of multiple traffic passenger flows

ZHANG Yu^,¹, CAO Weidong^,¹, LIANG Shuangbo², REN Yawen³^,⁴

1. School of Geography and Tourism, Anhui Normal University, Wuhu 241002, Anhui, China

2. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China

3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

通讯作者: 曹卫东(1973-),男,安徽寿县人,博士,教授,博士生导师,研究方向为区域发展与交通物流。E-mail: weidongwh@163.com

收稿日期:2020-11-3接受日期:2021-03-3

基金资助:

国家自然科学基金项目(41571124)
国家自然科学基金项目(41671132)

Received:2020-11-3Accepted:2021-03-3
作者简介 About authors
张宇(1988-),男,山西大同人,博士研究生,研究方向为区域发展与区域规划。E-mail: 1055006221@qq.com

摘要
作为要素流动的载体和空间重塑的主体,春运交通客流更能透视中国区域发展的新情况新问题。基于2017年春运期间交通客流数据,从整体网络、城际联系和城市节点维度对交通客流网络结构特征进行比较分析并综合识别城市地域交通类型。研究发现：① 与公路客流辐射范围和流量相比,铁路客流较大,航空客流较小;交通客流网络趋于发育为复杂网络;公路客流网络以邻省组合型城市组团为主,铁路客流网络主要为跨省组合型城市组团,航空客流网络均为“破碎”组合型城市组团。② 由短途为主的公路客流、中短途为主的铁路客流和中长途为主的航空客流分别构筑形成的区域空间结构具有显著的分化态势和极化现象,主要发生在“胡焕庸线”东南侧城市群内以及城市群之间。③ 在空间距离制约下,基于城市首位联系刻画而成的交通客流网络空间组织模式存在显著差异。公路客流形成5种区域空间组织模式,铁路客流形成多重组合的核心-边缘空间组织模式,而航空客流则形成多重组合的轴-辐空间组织运营模式。④ 城市地域交通类型分化显著,形成泾渭分明的“沉睡”区与“活跃”区,且中心城市和城市群成为集散客流的主要动力。希冀能为新形势下推动形成优势互补高质量发展的区域经济布局提供借鉴。
关键词： 城际出行网络;空间结构;城市角色;交通客流;春运

Abstract

As the carrier of the elements flow and the main body of spatial remodeling, the traffic passenger flow during the Spring Festival travel rush can better reflect the new trend of China's regional development. Based on the data of traffic passenger flow during the 2017 Spring Festival travel rush, the spatial structure of the traffic passenger flow network is compared and analyzed from the dimensions of the overall network, intercity connections and city nodes, and the regional traffic types of city are comprehensively identified. The study found that: 1) Firstly, compared with the radiation scope and flow scale of highway passenger flow, railway passenger flow is larger and air passenger flow is smaller. Secondly, the traffic passenger flow network tends to become more complicated. Specifically, highway passenger flow is dominated by city group of "neighboring-province", railway passenger flow is mainly expressed as a "cross-provincial" city group, and the "fragmented" city group is a common form of air passenger flow. 2) The regional spatial structure formed by short-distance highway passenger flow, medium-short-distance railway passenger flow and medium-long-distance air passenger flow shows a significant trend of polarization, which is mainly concentrated in the urban agglomeration and among the urban agglomerations on the southeast side of "Hu Huanyong Line". 3) Under the constraints of spatial distance, there are significant differences in the spatial organization model of the traffic passenger flow network based on the "city’s first contact". There are five spatial organization modes of the highway passenger flow, and the multi-portfolio "Core-Periphery" spatial organization mode is the general form of railway passenger flow, while the multi-portfolio "Hub-spoke" spatial organization operation mode is a typical representative of air passenger flow. 4) There are significant differences in the regional traffic types of city, in which the "sleeping" and "Active" areas of the traffic passenger flow have been formed, and central city and urban agglomeration are the main distribution centers of the traffic passenger flow. This article aims to provide reference for the formation of the regional economic pattern with complementary advantages and high quality development under the new situation in China.

Keywords：intercity travel network;space structure;city roles;traffic passenger flows;Spring Festival travel rush

PDF (6400KB)元数据多维度评价相关文章导出EndNote|Ris|Bibtex 收藏本文
本文引用格式
张宇, 曹卫东, 梁双波, 任亚文. 中国春运城际出行网络结构特征与城市角色识别——基于多元交通客流的测度[J]. 地理研究, 2021, 40(9): 2526-2541 doi:10.11821/dlyj020200998
ZHANG Yu, CAO Weidong, LIANG Shuangbo, REN Yawen. Structure characteristics of intercity travel network and identification of city role during the Spring Festival travel rush in China: Based on the measurement of multiple traffic passenger flows[J]. Geographical Research, 2021, 40(9): 2526-2541 doi:10.11821/dlyj020200998

1 引言

在全球化、信息化与快速交通共同推动与促进下,资本、信息和人员等要素与日俱增的移动特性正逐步将“社会性社会”建构成“流动性社会”^[1]。“流动性(mobility)”正从多方面、多层次深刻地影响、改变与重塑地理空间形态与结构,现已成为地理学不可忽视的空间特征和重要议题。“流动性”驱使地方要素向极化与分散化方向发展,引发区域空间结构、发展模式的革新与转变：由传统的、等级型的中心地模式向开放、流动、多中心的复杂网络化模式转变^[2,3];由传统的“地方空间”向基于网络的“流动空间”转变^[3];由早期的区域“自我发展”向强调区域“协同发展”转变。因此,基于流动性的城市网络成为新时期区域空间组织新形式。目前学术界主要通过交通流^[4,5]、人口流^[6,7]等实体(显性)媒介与信息流^[8,9]、企业组织^[10,11]、社会文化^[12,13]、科研合作^[14]、技术转移^[15]等虚拟(隐性)媒介对全球、国家、区域等尺度的城市空间结构进行刻画与诠释。

人流既是承载区域要素流动的载体,也是重塑区域空间结构的主体。作为表征“流动性”的重要媒介与载体,人流通过在空间位置上传递物质流、资本流、信息流和技术流,推动生产要素的快速流动和优化配置,促进城乡结构和社会结构的转变,进而重塑区域空间结构与形态。关于人口流迁的研究主要有：① 基于人口普查、抽样调查等统计数据对中宏观尺度人口迁移的空间特征、规律与影响因素进行分析^[16,17]。② 通过问卷调查、访谈、入户调查等方式获取个体属性特征对中微观尺度人口流动(迁)的空间特征、规律与动因进行探讨^[18,19]。③ 基于公路、铁路与航空等类型城际交通客流(客运班次近似表征)构建城市网络,进而对其城际联系程度、空间结构与空间组织模式等特征进行量化评价^[4,5,20-22]。④ 随着移动互联时代RS、GPS、LBS和LSS等技术的日益成熟与广泛应用,通过挖掘与处理庞大数量个体“人”的空间地理行为(出行路径与活动痕迹)、社会行为(个性偏好与社交方式)等信息数据,可以从宏观、中观、微观等尺度实时动态揭示人的空间活动轨迹与空间行为模式^[6-9,23]。鉴于此,通过腾讯人口迁徙大数据解析出公路、铁路与航空客流的城际出行轨迹,可以不同程度地弥补普查数据的时效性、抽样数据的客观性、问卷调查数据的典型性和城际客运班次数据的失真性等问题;相较于年际人口迁徙的时空结构及其演化进程研究,对年内各时段(如春运)人口流动的相关研究开展较晚且相对偏少;相较于单一类型交通流视角的城市网络研究,对多元交通流视角的城市网络异同性研究则相对较少;而专门针对中国春运期间多元交通客流网络的对比分析却鲜有所见。

受国土面积广阔、人口空间分异、区域经济差异和传统文化习俗等诸多因子的综合影响^[24],春运成为人类历史上规模最为壮观呈周期性流迁的地理现象,也是中国特有的社会经济现象。2017年春运期间全国旅客发送(客流)量达29.8亿人次,且公路、铁路和航空等交通客流量与出行目的却存在较大差异,通过对春运交通客流进行比较分析,不仅可以揭示交通客流网络结构特征差异,更能透视区域发展的新情况新问题。据此,通过腾讯人口迁徙数据构建交通客流的无向加权网络矩阵,并从整体网络、城际联系和城市节点3个维度对中国春运交通客流网络进行深入剖析与探讨。旨在为人口地理学与交通地理学等领域的传统议题提供新佐证,并希冀能为新形势下推动形成优势互补高质量发展的区域布局提供借鉴。

2 数据来源与研究方法

2.1 数据来源

2018年《中国移动互联网发展报告》显示：截至2017年底,中国移动电话用户总数14.2亿户,普及率达102.5部/百人,手机网民、手机即时通信用户、手机网络视频用户、手机网络新闻用户和移动游戏用户规模分别为7.5亿、6.9亿、5.49亿、6.2亿和5.54亿。腾讯计算机系统有限公司作为中国最具影响力互联网公司(最大互联网综合提供商和服务用户最多互联网公司)之一,数字产品已涵盖即时通信、社交、新闻、购物、视频、游戏、音乐等领域,基本覆盖全部移动智能手机用户。腾讯“人口迁徙图”平台(https://heat.qq.com/qianxi.php)利用其关联App提供的定位服务功能(LBS)可实时、动态、直观、真实地呈现海量个人用户的城际迁徙轨迹。

获取2017年春运期间(1月13日—2月21日,共40天)全国339个地级及以上行政区(4个直辖市、2个特别行政区、3个盟、7个地区、30个自治州、15个副省级城市以及278个地级市)逐日人口迁徙数据,因台湾省和海南省三沙市的数据缺失,未将其纳入研究范围。

2.2 研究思路

在清洗冗余数据后,构建每日城际人口流动的339×339无向加权网络矩阵：

(1)

\begin{array}{l} city j_{1} j_{2} ? j_{n - 1} j_{n} \\ \begin{matrix} i_{1} \\ i_{2} \\ ? \\ i_{n - 1} \\ i_{n} \end{matrix} [\begin{matrix} 0 & w_{1, 2} & ? & w_{1, n - 1} & w_{1, n} \\ 0 & ? & w_{2, n - 1} & w_{2, n} \\ 0 & ? & ? \\ 0 & w_{n - 1, n} \\ 0 \end{matrix}] \end{array}

式中：w_i_,_j₍_a₎为城市i到城市j的人口迁徙(交通客流)规模,且w_i_,_j₍_a₎=w_j_,_i₍_a₎。

据春运期间全国339个城市逐日人口迁徙数据分别建构公路、铁路和航空交通客流的339×339无向加权矩阵。按照整体网络(复杂网络与城市组团)→城际联系(区域空间结构与空间组织模式)→城市节点(地域交通类型)的逻辑关系对交通客流网络进行深入剖析。首先,利用复杂网络方法与网络社团模型分别对交通客流网络的整体网络结构和内在亲疏关系进行挖掘与可视化分析;其次,分别对交通客流联系的空间结构与空间组织模式进行分析和刻画;再次,基于城市控制力与城市影响力的组合关系测度与识别城市地域交通类型;最后对研究结果进行归纳总结(图1)。

图1

新窗口打开|下载原图ZIP|生成PPT
图1研究思路与分析框架

Fig. 1Research ideas and analysis framework

经统计,春运交通客流网络之间具有显著差异。铁路客流辐射范围最广且流量最大,公路客流辐射范围和流量均次之,而航空客流辐射范围最小且流量最小(表1)。

Tab. 1
表1
表1中国春运交通客流网络的基本参数
Tab. 1Basic parameters of traffic passenger network during the Spring Festival travel rush in China

交通客流网络	城市节点 (个)	城际客流联系路径		城际客流总量
交通客流网络	城市节点 (个)	数量(条)	占比(%)	人次(万人)	占比(%)
公路	339	5711	69.02	78443.28	32.70
铁路	339	8053	97.33	122848.05	51.21
航空	339	5249	63.44	38607.15	16.09
人口(综合)	339	8274	100.00	239898.48	100.00

新窗口打开|下载CSV

2.3 研究方法

随着复杂网络科学的发展,网络评价指标体系日趋完善。测度指标如下：

(1)累积度分布。考虑到网络中度分布的非连续性,为减少误差,用累积度分布函数P₍_k₎来描述度分布情况^[25,26],其与度分布关系为：

(2)

P_{(k)} = \overset{\infty}{\sum_{k < n}} p_{(k)}, 且 p_{(k)} \propto k^{- γ}

式中：度分布可用分布函数p₍_k₎表示(网络中度值为k的节点数n_k与网络总节点数n之比);度分布具有幂函数分布且幂指数为2≤

γ

≤3时为无标度网络。累积度分布函数则为：① 符合幂指数

γ - 1

的幂律

P_{(k)} \propto \overset{\infty}{\sum_{k < n}} p_{(k)} \propto k^{- (γ - 1)}

时为幂律分布,倾向无标度网络。② 具有与度分布相同的指数

P_{(k)} \propto \overset{\infty}{\sum_{k < n}} e^{- \frac{k}{λ}} \propto e^{- \frac{k}{λ}}

,其中

λ > 0

时则为指数型,倾向随机网络^[25]24。

(2)平均路径长度。描述网络节点的离散程度,为测度小世界网络的重要指标^[25]24。其值越小,网络的连通性能与组织效率越好。

(3)

L = \frac{2}{n (n - 1)} \sum^{} d_{ij}, 且 1 \leq i < j \leq n

式中：L为网络平均路径长度;

d_{ij}

为节点

i

到节点

j

间的最短路径距离。

(3)平均集聚系数。描述网络节点间联系的整体紧密程度与集团化程度,是表征小世界网络的重要参数^[25]26。其值越大,网络的整体紧密程度与集团化程度越高。

(4)

C = \frac{1}{n} \overset{n}{\sum_{i = 1}} C_{i}

式中：

C

为网络平均集聚系数;

C_{i}

为网络节点聚类系数。

(4)城市组团结构识别：用于刻画网络的局部集聚特征。基于交通客流量(边权重),将解析度设置为1.0,利用Gephi软件模块化的社区探测算法分别测度春运交通客流联系的内在亲疏关系与集聚特征^[27],揭示交通客流网络的空间组织结构特征。其中,“模块度”是评价城市组团结构清晰程度的重要指标,其值越高,城市组团结构越清晰。

(5)城市地域交通类型测度。① 客流集散量(

S_{i}

),衡量网络中城市客流集散能力(控制力)。其值越高,表明该城市控制力越强。② 特征向量中心度(

E C_{i}

),城市重要性既取决于自身连接边数量,也取决于其邻接城市重要程度(影响力)。其值越高,表明该城市对网络的影响力越强。③ 首先,运用自然间断点分级法(基于组间方差最大、组内方差最小原则)分别将交通客流网络中的城市客流集散量与城市特征向量中心度划分为3个层级;其次,基于客流集散量与特征向量中心度的层级组合关系构建复合指标体系测度城市客流活跃性与联系紧密程度,分别确定某城市在交通客流网络中的地域类型;最终通过对某城市在不同交通客流网络中的地域类型进行定量比较(由高往下逐级递推),在全国和区域尺度上将其细化为综合型、复合型和优势型等交通类型(表2)。

(5)

S_{i} = \sum^{} w_{ij}

(6)

E C_{i} = \frac{c \sum_{j}^{\in} a_{ij} x_{j}}{E C_{h}}

式中：

S_{i}

为城市客流集散量;

w_{ij}

为节点i与节点j连接边的权重(客流量);

k_{i}

为节点

i

联系边的集合;

E C_{i}

为城市特征向量中心度;

a_{ij}

为节点

i

与节点

j

存在连接边数量;

E C_{h}

为网络中特征向量中心度最高的节点;

c

为一个比例常数;

x_{j}

为节点

i

邻接节点

j

的度值。

Tab. 2
表2
表2中国春运城市地域交通类型的复合指标体系
Tab. 2The composite index system of city regional traffic types during the Spring Festival travel rush in China

城市地域等级( $S_{i}$ 与 $E C_{i}$ 的组合关系)	城市交通类型(解析说明)
全国性枢纽城市(Ⅰ-Ⅰ)	综合型(某城市在三类交通客流网络中具有同一地域类型)
区域性中心城市(Ⅰ-Ⅱ Ⅱ-ⅡⅡ-Ⅰ)	复合型(某城市在两类交通客流网络中具有同一地域类型,且高于其在第三类交通客流网络中地域类型),细分为公铁、公空和空铁复合型
地方性节点城市(Ⅰ-Ⅲ Ⅱ-ⅢⅢ-Ⅲ Ⅲ-Ⅱ Ⅲ-Ⅰ)	优势型(某城市在一类交通网络中的地域类型高于其在另两类交通客流网络中的地域类型),细分为公路、铁路和航空优势型

新窗口打开|下载CSV

3 结果分析

3.1 客流网络整体特征

3.1.1 趋向于发育为复杂网络交通客流网络整体趋向发育为兼具无标度特性与小世界特性的复杂网络。公路客流网络和铁路客流网络趋向发育为有序结构的复杂网络,而航空客流网络则为典型的复杂网络。

(1)无标度网络。完全随机网络的度分布近似为泊松分布,函数分布呈指数或对数分布^[25]54;而无标度网络则体现为增长和优先连接特性,函数分布呈幂律分布^[25]67。① 交通客流网络累积度分布的幂律函数拟合度(R²)均在0.92以上,明显高于其指数函数拟合度,即呈幂律分布。② 公路与航空客流网络的幂指数

γ - 1

居于适当区间(2≤

γ

≤3),为典型无标度网络;而铁路客流网络的幂指数接近适当区间,趋于无标度网络(图2)。

图2

新窗口打开|下载原图ZIP|生成PPT
图2中国春运交通客流网络的累积度分布及其函数

Fig. 2Distribution of accumulation degree and its function of traffic passenger network during the Spring Festival travel rush in China

(2)小世界网络。与同等规模节点的随机网络相比,小世界网络兼具较短的平均路径长度和较大的平均聚类系数^[25]59-60。① 在平均路径长度上,公路客流网络较长,铁路客流网络略长,而航空客流网络则略短。② 在平均聚类系数上,交通客流网络均具有较大的平均聚类系数。因此,公路客流网络趋向小世界网络,铁路客流网络则接近小世界网络,而航空网络为典型小世界网络(表3)。

(3)组织性与紧密度。对比平均路径长度和平均聚类系数可知：交通客流网络的组织性与紧密度错位明显。公路客流网络连通性最差,紧密度却最好;铁路客流网络连通性最好,紧密度相对适中;而航空客流网络连通性相对适中,紧密度却最差(表3)。

Tab. 3
表3
表3小世界网络和网络社团的基本参数
Tab. 3Basic parameters of small world networks and network societies

交通客流网络	平均路径长度(C)	平均聚类系数(L)	模块度(M)
公路	2.116(1.937)	0.533(0.100)	0.678
铁路	1.876(1.861)	0.471(0.139)	0.495
航空	1.929(1.963)	0.312(0.092)	0.245

注：括号内数值为同等规模节点的随机网络指标。

新窗口打开|下载CSV

3.1.2 城市组团结构相对清晰依据城市组团的空间覆盖范围与省级行政区划的吻合关系,可将城市组团划分为单一省域型(由同一省级行政区内的城市组成)、邻省组合型(由地理邻近的几个省级行政区内的城市共同组成)、跨省组合型(主要受地理邻近性和“超空间”特性的共同影响,由跨越多个省级行政区的城市组成)和“破碎”组合型(具有“超时空”特性,呈现“破碎化”空间格局)。公路、铁路与航空客流网络的模块度依次降低(表3),城市组团数量逐步递减,但其组团结构由清晰趋向模糊(图3)。具体可知：① 公路客流网络有12个组团,包含4个单一省域型(Ⅷ→湖北省、Ⅹ→海南省、Ⅺ→福建省与Ⅻ→山东省)和8个邻省组合型(Ⅰ、Ⅱ、Ⅲ、Ⅳ、Ⅴ、Ⅵ、Ⅶ与Ⅸ);② 铁路客流网络有8个组团,包含2个单一省域型(Ⅳ→云南省与Ⅷ→河南省)、2个邻省组合型(Ⅰ与Ⅱ)和4个跨省组合型(Ⅲ、Ⅴ、Ⅵ与Ⅶ);③ 航空客流网络有6个组团,均为“破碎”组合型。

图3

新窗口打开|下载原图ZIP|生成PPT
图3中国春运交通客流网络的城市组团结构与城市地域类型

注：此图基于国家自然资源部标准地图服务系统的标准地图(审图号：GS(2020)4619号)绘制,底图无修改。
Fig. 3City group structure and city regional types of traffic passenger network during the Spring Festival travel rush in China

3.2 城际客流联系特征

3.2.1 交通特性存在内在差异运用客流规模、客流占比、客流分配率和累计率分别测度城际交通客流联系的空间距离衰减规律^[4,22]。交通特性(空间距离衰减规律)差异决定交通客流之间的内在联系,由短途为主的公路客流、中短途为主的铁路客流和中长途为主的航空客流有机结合共同构筑形成交通客流网络。具体可知：① 从客流规模看,公路与铁路客流峰值均在100~200 km区段,而航空客流峰值则在1400~1500 km区段(图4a)。② 从客流占比看,在0~200 km区段公路客流相比铁路客流更占优势,在200~1200 km区段铁路客流始终据主导地位,在1200~4500 km区段航空客流基本据主导地位(图4b)。③ 从客流累计率看,81.31%公路客流集聚在0~400 km区段,80.56%铁路客流集聚在0~1200 km区间,而95.53%航空客流则集聚在200~1500 km区段、尤其高度集聚在900~1600 km区段(图4c)。④ 从客流分配率看,公路客流在100~200 km区段达到峰值(占其总量31.49%,后同)后呈稳定下降态势;铁路客流在100~200 km区段达到峰值(18.28%),在200~600 km区间呈快速下降态势,在600~1500 km区间呈跳跃性稳定上升态势后又迅速下降;航空客流呈先快速递增后又迅速下降的“单峰”变化态势,峰值(28.61%)在1400~1500 km区段(图4c)。

图4

新窗口打开|下载原图ZIP|生成PPT
图4中国春运交通客流网络的空间距离衰减比较

Fig. 4Comparison of spatial distance attenuation of traffic passenger network during the Spring Festival travel rush in China

3.2.2 区域空间结构趋向分化利用自然间断点分级法分别将城际交通客流划分为4个层级(图5)。由于空间依赖性、空间指向性与空间异质性在多重尺度上交互叠加^[4,20-22],春运城际交通客流呈现明显的分化态势与极化现象。在全国尺度上主要发生在“胡焕庸线”东南侧;在区域尺度上主要趋向汇聚在城市群内以及城市群中心城市之间(图5)。

图5

新窗口打开|下载原图ZIP|生成PPT
图5中国春运交通客流网络的空间格局

注：此图基于国家自然资源部标准地图服务系统的标准地图(审图号：GS(2020)4619号)绘制,底图无修改。
Fig. 5Spatial pattern of traffic passenger network during the Spring Festival travel rush in China

(1)主干流。① 公路客流的11.34%集聚发生在城市群内的29(0.51%)对城市间,区域一体化和同城化现象显著。② 铁路客流的5.84%集聚发生在城市群内部的21(0.26%)对城市间,具有明显的区域一体化与同城化特征;同时,4.64%客流集聚发生在主要城市群间的14(0.17%)对中心城市,构成全国尺度主干骨架。③ 航空客流的13.91%由京津冀(北京)、长三角(上海)、珠三角(深圳与广州)和成渝(重庆与成都)4大城市群间的7(0.13%)对中心城市承载,形成全国尺度“菱形”主干骨架。

(2)骨干流。① 公路客流的29.53%聚集在198(3.47%)对城市间,高度发生在城市群内并向外扩散。② 铁路客流的30.02%聚集在257(3.19%)对城市间,高度粘合在主要铁路干线沿途,具有显著的“沿途”效应;横向客流密集程度明显低于纵向客流(西安-兰州段“断裂”)。③ 航空客流的25.68%由44(0.84%)对城市承载,形成以“菱形”顶点为核心,省会或旅游城市为其主要链接的区域空间结构。

(3)主枝流。① 公路客流的36.70%分散在634(11.10%)对城市间,主要发生在北京-西安-成都-昆明一线以东区域,形成由京津冀、中原与长三角城市群构成的“三角形”密集区和由武汉都市圈、成渝、滇中、北部湾与珠三角城市群构成的“菱形”密集区;此外,以哈尔滨、西安、兰州和乌鲁木齐等省会城市为核心、周边城市为腹地的区域空间结构显现。② 铁路客流的35.94%分散在868(10.78%)对城市间,多数为铁路主干线的深化延伸;同时,“胡焕庸线”以西的拉萨与乌鲁木齐分别成为所在区域内外联系的枢纽。③ 航空客流的30.90%由176(3.35%)对城市承载,在北京-西安-成都-昆明一线东侧形成以大中城市为核心的区域空间结构,而西侧的客流联系则有待发育(乌鲁木齐成为新疆内外联系枢纽)。

(4)分枝流。① 公路客流的22.43%分散在4850(84.92%)对城市间,由东向西呈梯度递减空间分布格局。② 铁路客流的23.57%分散在6893(85.60%)对城市间,新增大量中小城市,形成复杂交织的网络结构。③ 航空客流的29.51%分散在5022(95.68%)对城市间,大量西部地区的中小城市融入网络,呈现高度密集的区域空间结构。

3.2.3 空间组织模式发育多样城市首位联系(某城市所有城际客流联系中居于首位的网络联系路径)可以清晰地识别网络中城市间的主导关系,进而抽象地刻画网络的空间组织模式^[22,28]。现用城市首位度(某城市拥有首位客流联系路径的数量)和城市首位联系强度(某城市自身首位客流量占其客流总量的比例)来测度与确定城市间主导关系与互惠共生城市类型,进而刻画空间组织模式。在空间距离制约下,由城市首位联系构建而成的交通客流网络空间组织模式存在明显差异。公路客流受地理邻近性制约显著,形成单核孤立发育、单核外围发育、双核孤立发育、双核共生发育与多核心网络发育5种空间组织模式;铁路客流受地理邻近性影响,发育为多重组合的核心-边缘空间组织模式;航空客流则具有“超空间”特性,形成多重组合的轴-辐空间组织运营模式。

(1)在首位联系城市上,公路、铁路与航空客流网络中首位联系城市数量逐渐递减,但辐射范围与整合能力却在依次增强(图6A)。① 公路客流网络有163个首位联系城市,辐射范围具有显著地理邻近性与属地特征(平均空间距离149.84 km,后同),在省级行政边界效应制约下呈现核心-边缘空间结构,中心城市和省会城市成为首位客流主要汇集地。② 铁路客流网络有82个首位联系城市,辐射范围具有明显的地理邻近性(359.91 km)与空间指向性,突破省级行政边界效应制约,呈现多重组合的核心-边缘空间结构。其中,北京(27)成为连接南北两大区域枢纽,成都(23)辐射范围主要局限在省内;此外,多数省会城市只承担省域门户职能。③ 航空客流网络有36个首位联系城市,辐射范围具有“超空间”特性(1276.61 km)且具有明显空间指向性,形成以首位联系城市为中心的放射状空间结构。其中,上海(70)、北京(63)、深圳(52)三大城市汇集54.57%的城市首位联系路径,空间范围基本覆盖“胡焕庸线”以东区域;西部地区的城市首位联系路径主要汇集于成都(35)、重庆(27)、乌鲁木齐(10)、昆明(9)、咸阳(6)5个首位联系城市。两者有机联结形成全国尺度的核心-边缘层级结构。

图6

新窗口打开|下载原图ZIP|生成PPT
图6城市首位联系视角下交通客流网络的空间组织结构与空间组织模式

注：图6A基于国家自然资源部标准地图服务系统的标准地图(审图号：GS(2020)4619号)绘制,底图无修改。
Fig. 6The spatial organization structure and the spatial organization model of traffic passenger network from the perspective of the "city’s first contact"

(2)城市首位联系强度可以反映属地城市对其首位联系城市的依赖程度,分别将交通客流网络的城市首位联系划分为3个等级(图6A和表4)。铁路、公路与航空客流网络的平均城市首位联系强度依次递增,且客流方向趋于高度汇集。① 公路客流网络以弱联系为主,较强联系次之,强联系数量较少且多发生在核心城市与其卫星(或邻近)城市;属地城市对其首位联系城市依赖程度相对适中,客流方向无明显汇集。② 铁路客流网络中弱联系占据绝对地位,强联系仅有2对(资阳→成都、南阳→郑州);首位联系城市对其属地城市的控制力较弱,客流方向趋于分散。③ 航空客流网络中较强联系占据主导地位;首位联系城市对其属地城市具有较强控制力,客流方向趋向高度汇集。其中,北京强联系最多(18),主要向东北与中部地区呈扇状扩散;上海(11)次之,主要向东北与华南地区呈放射状扩散;此外,乌鲁木齐(3)、成都(2)、重庆(2)、咸阳(1)、沈阳(1)和郑州(1)则成为沟通南北、承接东西的首位联系城市。

Tab. 4
表4
表4城市首位联系强度等级与互惠共生城市类型
Tab. 4The "city’s first contact" strength grade and reciprocal symbiotic city type

客流类型	强联系(个) (≥0.6)	较强联系(个) (>0.3,≤0.6)	弱联系(个) (≤0.3)	平均城市首位联系强度	互惠共生城市(1对=2个城市首位联系路径)
客流类型	强联系(个) (≥0.6)	较强联系(个) (>0.3,≤0.6)	弱联系(个) (≤0.3)	平均城市首位联系强度	总(对)	依附型(对)	伙伴型(对)
公路	18(5.31%)	134(39.53%)	187(55.16%)	0.32	55	35	20
铁路	2(0.59%)	105(30.97%)	232(68.44%)	0.27	15	9	6
航空	42(12.39%)	236(69.62%)	61(17.99%)	0.43	6	3	3

注：括号内百分数为各类城市首位联系强度个数占其总量的比例。

新窗口打开|下载CSV

(3)互惠共生城市为城际客流联系彼此互为首位的城市对,有依附与伙伴两种类型(见表4)。当城市间首位联系强度相差>0.1时为依附关系,主要体现为属地城市严重依赖首位联系城市;当首位联系强度相差≤0.1时为伙伴关系,体现为两个规模相等城市间相互依赖、共同发展。① 公路客流网络互惠共生城市数量最多,有北京←廊坊、广州←佛山与合肥←六安等35对依附型城市;有上海-苏州与深圳-东莞等20对伙伴型城市。② 铁路客流网络次之,有广州←佛山、西安←咸阳与昆明←曲靖等9对依附型城市;有北京-武汉与济南-青岛等6对伙伴型城市。③ 航空客流网络最少,依附型城市为海口←合肥、乌鲁木齐←喀什与宁德←百色;伙伴型城市有深圳-成都、北京-重庆与贵阳-温州。

基于上述分析：① 公路客流主要形成5种空间组织模式(图6b1)：Ⅰ.单核孤立发育(区域只有一个核心城市且周边城市具有向心指向,垂直规模等级结构显著);Ⅱ.单核外围发育(区域多数城市指向单核心城市,但外围个别城市形成伙伴型或依附型互惠共生关系,规模等级结构明显);Ⅲ.双核孤立发育(区域两个核心城市均已形成各自专属领地,具有规模等级结构);Ⅳ.双核共生发育(区域两个核心城市间成为伙伴型互惠共生关系,兼顾规模等级结构与网络组织结构);Ⅴ.多核心网络发育(区域多个核心城市的属地交互重叠,网络组织结构显著)。② 铁路客流虽已突破省级行政边界效应制约,但仍受地理邻近性影响,在公路客流空间组织模式的基础上形成多重组合的核心-边缘空间组织模式(图6b2)。③ 航空客流虽具有“超时空”特性,但存在基础设施分布与等级限制(多数机场的飞行区等级较低、开通航线偏少且多指向核心城市),使得中小城市客流需就近选择机场进行较大空间跨度出行,因而形成多重组合的轴-辐空间组织运营模式(图6b3)。

3.3 城市角色识别

3.3.1 层级空间结构差异显著城市客流集散量(控制力)和城市特征向量中心度(影响力)是判别城市在交通网络中地位高低的重要依据。交通客流网络中城市控制力和城市影响力均处在Ⅰ级的只有京津冀(北京)、长三角(上海)、珠三角(深圳)和成渝(重庆)四大城市群中心城市,而其余城市的控制力和影响力在空间层级结构中具有显著差异(图7)。

(1)从城市控制力看：Ⅰ级城市具有较高一致性,而其他城市却有明显层级分异(图7A)。①Ⅰ级：公路客流网络有深圳、广州、北京、上海、东莞、重庆、成都和苏州8个城市,铁路客流网络有北京、上海、广州、深圳、重庆和成都6个城市,航空客流网络有上海、重庆、北京、深圳和成都5个城市,均为京津冀、长三角、珠三角和成渝4大城市群中心城市或重要城市。②Ⅱ级：公路客流网络有佛山、郑州和南宁等99个城市,全部集中在“胡焕庸线”东侧;铁路客流网络有武汉、西安和苏州等25个城市,主要为省会城市,兼有部分制造业城市;航空客流网络有广州、杭州和宁波等20个城市,以省会城市为主,兼有部分贸易和旅游城市。③Ⅲ级：该类城市数量在公路、铁路和航空客流网络中均占据绝对主导地位,分别占其总量的75.08%、90.86%和92.63%。

(2)从城市影响力看：城市在空间层级结构中具有显著分异(图7A)。①Ⅰ级：公路客流网络有重庆、上海和广州等10个城市,均为5大城市群中心城市或重要城市;铁路客流网络有重庆、上海和北京等15个城市,均为各大城市群中心城市或重要城市;航空客流网络有重庆、上海和北京等14个城市(地区),除阿里为边远地区外,其他城市均为各大城市群中心城市或重要城市。②Ⅱ级：公路客流网络有西安、成都和昆明等113个城市,主要集聚在北京-西安-成都-昆明一线东侧;铁路客流网络有宁波、贵阳和阿里等124个城市(地区),主要集聚分布在“胡焕庸线”东侧的铁路干线沿途,在其西侧呈“破碎化”的空间格局;航空客流网络有苏州、长沙和和田等46个城市(地区),在全国尺度上整体呈“碎片化”空间格局。③Ⅲ级：该类城市数量在公路、铁路与航空客流网络中均占据绝对主导地位,分别占其总量的63.72%、59.00%和82.30%。

图7

新窗口打开|下载原图ZIP|生成PPT
图7城市控制力和城市影响力的空间层次结构及其组合类型

注：图7A基于国家自然资源部标准地图服务系统的标准地图(审图号：GS(2020)4619号)绘制,底图无修改。
Fig. 7Spatial hierarchical structure and combination types of city control and city influence

3.3.2 城市地域交通类型分化准确测度与识别春运交通客流网络中城市地域交通类型对于清晰解析与充分理解区际发展差异至关重要(表2、图3和图7B)。城市地域交通类型分化显著。在全国尺度上形成泾渭分明的“沉睡”区与“活力”区,北京-石家庄-郑州-成都-昆明一线西侧的东北、西北、青藏等地区的客流集散程度较低,陷入“沉睡”,而其东侧的大部分地区则成为客流集散“活跃”区;在区域尺度上中心城市和城市群已成为客流的主要集散地(见图8)。

图8

新窗口打开|下载原图ZIP|生成PPT
图8中国春运城市地域交通类型

注：此图基于国家自然资源部标准地图服务系统的标准地图(审图号：GS(2020)4619号)绘制,底图无修改。
Fig. 8Spatial pattern of city regional traffic types during the Spring Festival travel rush in China

(1)全国性枢纽城市(8个)。综合型有北京、上海、深圳和重庆4个城市,具备综合职能或突出的经济职能;成都为空铁复合型,虽为省会城市但公路客流的影响力的主要范围在省内;广州为公铁复合型,虽为全国重要经济中心但航空客流集散量的位序相对较低;而苏州与东莞则为公路优势型,制造业尤其劳动密集型产业的高度集聚,对邻近省市劳动力具有强烈吸附力,以公路出行和运输为主。

(2)区域性中心城市(66个)。综合型有贵阳、杭州和郑州等13个城市,主要以省会城市为主,兼有部分商贸城市;公铁复合型有佛山、合肥和石家庄等6个城市,主要以省会城市为主,兼有部分制造业和商贸城市;空铁复合型有哈尔滨、济南和长春3市,均为省会城市;公路优势型有保定、阜阳和遵义等40个城市,主要为城市群的属地城市;只有兰州市为铁路优势型,主要承担西部地区铁路客流中转的职能;航空优势型有咸阳、三亚和福州3个城市,主要承担航空交通、旅游和商贸等职能。

(3)地方性节点城市(265个)。主要在北京-石家庄-郑州-成都-昆明一线西侧呈“集中连片化”的分布格局,在其东侧呈“破碎化”的分布格局。

春运期间城市交通客流主要受地理位置(区位优势与地理邻近性)、人口空间分异(常住人口总量与年龄结构)、经济发展差异(产业结构、工作岗位与收入水平)、交通基础设施(高速公路、高铁线路与机场的布局)、迁移成本(心理成本与经济成本)、自然地理环境(地形与季节变化)、传统文化习俗(春节“返乡”阖家团圆)以及路径依赖等因素的综合影响。

4 结论与讨论

4.1 结论

利用“腾讯迁徙”平台提供的城际交通客流数据,对2017年春运交通客流网络空间结构进行比较分析并综合测度城市地域交通类型,既可以揭示交通客流网络的结构特征差异,更能透视区域发展的新情况新问题。主要结论有：

(1)相较公路客流辐射范围和流量而言,铁路客流较大,而航空客流较小;公路客流网络和铁路客流网络趋向发育为有序结构的复杂网络,航空客流网络则为典型的复杂网络;且交通客流网络的组织性与紧密度错位明显。

(2)公路、铁路与航空客流网络的城市组团数量逐步递减,其组团结构由清晰趋向模糊。公路客流网络共有单一省域型和邻省组合型2种组团,以邻省组合型为主;铁路客流网络共有单一省域型、邻省组合型和跨省组合型3种组团,主要为跨省组合型;而航空客流网络则均为“破碎”组合型组团。

(3)在交通特性与空间特性共同作用下,由短途为主的公路客流、中短途为主的铁路客流和中长途为主的航空客流分别构筑形成的区域空间结构具有明显的分化态势与极化现象。在全国尺度上主要发生在“胡焕庸线”东南侧,在区域尺度上主要趋向汇聚在城市群内以及城市群中心城市之间。

(4)在空间距离制约下,基于城市首位联系刻画而成的交通客流网络空间组织模式存在明显差异。公路客流受地理邻近性制约显著,形成单核孤立发育、单核外围发育、双核孤立发育、双核共生发育与多核心网络5种区域空间组织模式;铁路客流虽已突破省级行政边界效应制约,但仍受地理邻近性影响,发育为多重组合的核心-边缘空间组织模式;而航空客流具有“超空间”特性,形成多重组合的轴-辐空间组织运营模式。

(5)城市地域交通类型分化显著。在全国尺度上形成泾渭分明的“沉睡”区与“活力”区,北京-石家庄-郑州-成都-昆明一线西侧的东北、西北、青藏等广大地区的客流集散程度较低,陷入“沉睡”,而其东侧的大部分地区则成为客流集散“活跃”区;在区域尺度上中心城市和城市群现已成为客流的主要集散地。

4.2 讨论

(1)数据限制。“腾讯迁徙”数据虽拥有多样性、真实性、代表性、动态性等特点,但仍存在限制：① 人群偏好。大量未使用关联App用户的城际出行轨迹未被计录(如老年人)。② 属性偏好。只有公路、铁路、航空3类交通方式的城际出行轨迹属性,而无性别、年龄与目的地等属性。③ 时空限制。只记录用户固定时间内空间位移,虽然当前高铁、飞机等交通方式快速便捷,使得大部分出行耗时较少,但仍有相当规模空间位移数据未被完整记录,而被拆分成2次及以上空间位移。④ 非全数据。只呈现地级及以上城市(盟、地区、州)每日前10位流入/出地数据。边远地区(如阿里)自身流入/出地经常变换,致使其影响力(特征向量中心度)虚高;行政或经济等级较高的城市(如北京)自身流入/出地基本固定不变,但其他城市趋向其联系(趋优联系),更能反映出真实的影响力和控制力。

(2)研究仅对2017年春运期间多元交通客流网络的空间结构进行了多维度分析,缺少从耦合性与差异性等视角对多元交通客流进行年内多时段(如小长假、日常)的对比分析,据此揭示年内不同时段多元客流网络空间结构的异同性,进而折射区际(城际)发展的新情况;限于篇幅有限,仅简单探讨了春运期间城市交通客流的影响因素,而未能对其内在动力机制进行深入剖析;与此同时,面对新形势下春运期间城市交通客流趋于显著的分化态势与极化趋势,那么其他时段或未来城市交通客流的空间布局趋势如何,是趋向均衡化,还是趋于固化?进而如何推动形成优势互补高质量协调发展的区域布局?这将是未来需要研究的重大议题。

致谢:

真诚感谢两位匿名评审专家在论文评审中所付出的宝贵时间和精力,评审专家对本文研究方法、结果分析以及专业词汇严谨性等方面所提出的意见和建议,使本文获益匪浅。

参考文献原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子

[1]

孙九霞, 周尚意, 王宁, 等. 跨学科聚焦的新领域: 流动的时间、空间与社会
地理研究, 2016, 35(10):1801-1818.

DOI:10.11821/dlyj201610001 [本文引用: 1]

在全球化对社会产生深刻影响的当下,流动获得了内涵和外延的立体化与多面性,各种类型的流动正表征并重塑地方、社会结构和价值观念。如何认识流动、分析流动对理解当前世界多重空间尺度的各类社会文化现象、理解空间及附加其上的意义具有重要影响。一方面,流动性是流动实践空前增强的过程,这个过程既发生在全球与区域的尺度上,也发生在城市内部日常生活与通勤的尺度上。另一方面,流动性对于人文社会科学中基本概念的阐释具有革命性的作用,例如地方,认同这些话题都需要从流动性的视角进行重新思考。以时间和空间作为刻画流动性的基础尺标,在新流动性范式下,从地方、城市、移民、旅游、身份认同、政策与管制等方面探讨流动性多层次的社会文化意义,期于在流动性这一场域进行跨学科的对话,并促使国内学界对流动现象的关注上升为对流动性理论本身的解读。

[ Sun

Jiuxia

, Zhou

Shangyi

, Wang

Ning

, et al. Mobility in geographical research: Time, space and society
Geographical Research, 2016, 35(10):1801-1818.]. DOI: 10.11821/dlyj201610001.

[本文引用: 1]

With the profound impact of globalization on society, the connotation and denotation of mobility have become three-dimensional and multi-faceted today. Various types of mobilities are representing and reshaping our social structure and values. How to analyze and understand mobility has important implications on our cognition of social culture, local structure and contextual significance, as well as all kinds of socio-cultural phenomena which are currently measured by multiple spatial scales. On the one hand, mobility is an unprecedented enhancement of movement. This process not only occurs in the global and regional scales, but also in the scale of daily life and commuting within the urban areas. On the other hand, mobility overturns our understanding of the basic conceptions in humanities and social sciences in a revolutionary way. For example we need to rethink these crucial issues like space and identity with a perspective of mobility. Under the new paradigm—taking time and space as a basic judgement of mobility, the multi-layered and three-dimensional sociocultural meaning of mobility is thus to be discussed in terms of regions, cities, immigration, tourism, identity, policy, regulation and so on. This practice offers a field for interdisciplinary dialogue and communication which carries the expectation of providing references and prospects for lifting the discussion on movement to mobility.

[2]

Castells

. Globalisation, networking, urbanisation: Reflections on the spatial dynamics of the information age
Urban Studies, 2010, 47(13):2737-2745. DOI: 10.1177/0042098010377365.

URL [本文引用: 1]

[3]

王士君, 廉超, 赵梓渝. 从中心地到城市网络: 中国城镇体系研究的理论转变
地理研究, 2019, 38(1):64-74.

DOI:10.11821/dlyj020180468 [本文引用: 2]

城镇体系核心特征的演变,推动其研究视角由等级制的传统中心地理论向关系制的现代城市网络转变。以已有相关研究成果为基础,简要回顾克氏中心地理论在中国城镇体系研究中的理论和实证进展,通过对中心地理论固有缺陷,以及城镇体系研究逻辑变化的分析,阐明传统中心地理论在解释和解决现代城镇体系问题时的局限性;指出城市网络理论兴起的背景：交通革命带来的时空压缩导致区位论的解释失效、流空间理论的产生、全球化与生产网络的产生、制度与体制演变——中心地组织原则的转化;从企业组织、交通流、信息流三个研究视角,揭示中国城镇体系研究的网络思维端倪;通过研究主体特征的关注由中心性到节点属性、主体关系由等级从属到功能互补、时空尺度由地方到全球三个方面的对比,指出中心地与城市网络应用于城镇体系研究中的理论差异。

[ Wang

Shijun

, Lian

Chao

, Zhao

Ziyu

. From central place to city network: A theoretical change in China's urban system study
Geographical Research, 2019, 38(1):64-74.]. DOI: 10.11821/dlyj020180468.

[本文引用: 2]

The systematic and organizational nature of the urban system is not naturally formed and immutable, but it is actively reconstructed and evolved along with the process of social spatial production. The evolution of the characteristics of the research object has promoted the theoretical development of the urban system research. The research perspective has changed from the hierarchical central traditional theory to the modern urban network of the relational system. Based on the integration of relevant research results, the paper first briefly reviews the theoretical and empirical progress of the Christaller center theory in China's urban system research. By analyzing the inherent defects of the central geography theory and the logical changes in the research of the urban system, the limitations of the traditional central theories in interpreting and solving the problems of the modern urban system are expounded. That is, the main deficiencies of the central theories are owing to closed systems. The hypothesis of spatial homogenization created by theory is too ideal and the perfect structural picture which is devoted to the condition of strict hypothesis is unlikely to exist in reality which leads to the lack of universality in its application and the influence of practical value. Secondly, it is pointed out that the theoretical failure of central land theory and the rise of urban network theory are reflected in the following aspects: the time and space compression brought by the traffic revolution leads to the failure of the interpretation of location theory, the generation of space of flow theory, the generation of globalization and production network, the evolution of system and the transformation of the central organization principle. Thirdly, from the three research perspectives of enterprise organization, infrastructure, and information flow, and the evaluation index system of degree-centrality and cluster structure, etc., the network thinking of China's urban system research is revealed. Finally, it is pointed out that from the level to the network, the differences between the traditional and modern urban system research and the theoretical transformation of China's urban system research are reflected in the main characteristics changing from the central to the node attributes, and the main relationship, from hierarchical subordinate to functional complementary. The analysis logic of the subject relationship is transformed from vertical, monopoly and control of research objects, scale relation to vertical and horizontal, complementary and cooperative, and complex relationship network relations; spatial scales change from the locality to the global; the foothold of the research is from the result of closed and static to the process of open and dynamic.

[4]

王姣娥, 杜德林, 金凤君. 多元交通流视角下的空间级联系统比较与地理空间约束
地理学报, 2019, 74(12):2482-2494.

DOI:10.11821/dlxb201912005 [本文引用: 4]

交通运输联系是区域空间级联系统与城市体系结构研究的重要视角之一,而不同交通运输方式表达的级联体系结构亦存在一定的差异。为综合研究交通运输体系刻画的空间级联系统及克服单一交通方式的局限性,基于长途汽车、高铁和航班时刻表数据,比较分析了多元交通网络的空间级联体系结构及其表达的城市网络组织体系,并进一步揭示了地理空间的约束作用。研究发现：① 每种交通运输方式适合在一定的空间尺度和行政范围内刻画和表达城市网络体系结构与城市联系,公路客运受省域行政范围约束,高铁联系具有廊道影响效应,航空运输体现全国和区域尺度较高层次的社会经济联系。② 从旅客直达视角分析,长途汽车与高铁的城际运输市场重叠最大,近年来长途汽车的运输市场受高铁影响明显。③ 地理空间是影响陆路交通运输和组织的重要约束因子,距离衰减效应明显;结合设施空间、行政空间和管理体制的作用,长途汽车和高铁运输在空间上形成分异的社区结构;航空运输由于具有超空间连接特性,既不遵循距离衰减规律,社区结构也并不明显。

[ Wang

Jiao'e

, Du

Delin

, Jin

Fengjun

. Comparison of spatial structure and linkage systems and geographic constraints: A perspective of multiple traffic flows
Acta Geographica Sinica, 2019, 74(12):2482-2494.]. DOI: 10.11821/dlxb201912005.

[本文引用: 4]

Transportation connection has always been one of the important perspectives of studying spatial cascading systems and urban systems. Based on the timetable data in 2018 of inter-city coach, high-speed train and aviation, this paper builds networks of the three modes of transportation in China. Through the methods of the city-pair connectivity and community detection, this paper compares the spatial structure and linkage systems of multi-traffic flow network and reveals the geospatial constraints. The research results show that: (1) Different modes of transportation are suitable for portraying urban systems on different spatial and administrative scales. Inter-city coaches are constrained by the provincial administrative boundaries. High-speed train network shows the effect of corridors especially along the main trunks. The aviation network reflects the spatial relationship at the national scale. (2) From the perspective of the direct accessibility, there is a large spatial overlap between inter-city coaches and high-speed trains, and the market of inter-city coaches is obviously squeezed in recent years. For air transport, its frequency advantage mainly concentrates on the city-pairs with a long distance. The competition and complementarity of the three modes of transportation have a great impact on the urban system and are useful for the understanding of the spatial cascading system. (3) Geographical space, infrastructure space and administrative space constraint and management system are important factors affecting the transportation networks. Inter-city coach network and high-speed train network are obviously affected by distance attenuation effects, and they present significant community structures in the two networks, but their communities have different spatial characteristics. However, air transport does not follow the constraint of distance attenuation, and there is not an obvious community structure in the network. Factors related to the passenger transport market, such as social and economic links and tourism resources, play the important roles in the aviation network structure.

[5]

Mahutga M

, Ma

, Smith D

, et al. Economic globalisation and the structure of the world city system: The case of airline passenger data
Urban Studies, 2010, 47(9):1925-1947. DOI: 10.1177/0042098010372684.

URL [本文引用: 2]

[6]

刘望保, 石恩名. 基于ICT 的中国城市间人口日常流动空间格局: 以百度迁徙为例
地理学报, 2016, 71(10):1667-1679.

DOI:10.11821/dlxb201610001 [本文引用: 2]

随着互联网时代的来临,网络数据已越来越成为表征居民地理行为的重要载体,用户迁移、社交网络、移动通信等地理行为大数据成为城市联系研究的重要数据来源。“百度迁徙”大数据通过LBS技术,全程、动态、即时和直观地记录了城市之间的人口日常流动轨迹。通过采集“百度迁徙”数据库中2015年一季度（2月7日至5月16日）国内369个城市之间的逐日的人口流动数据,分“季度平均、春运期间（春节前）、春运期间（春节后）、劳动节、周末和工作日”6个时间段,从人流集散层级、人流集散网络体系的分层集聚、人口日常流动空间格局及其与“胡焕庸线”之间的关系等角度分析各时间段的城市之间的人口日常流动相关特征与空间格局。研究发现,“百度迁徙”大数据清晰地显示了春运期间中部和沿海地区之间的人口流动格局。人流集散中心主要分布在京津冀、长三角、珠三角和成渝4大城市群中,并与其城市等级有较强的一致性。人口日常流动集散体系呈明显的分层集聚,京津冀、长三角、珠三角、成渝和乌鲁木齐5大集散体系在各时间段基本得到体现,而华中、东北、西南和福建沿海等地区并未出现高层级集散城市和高等级集散体系,与这些区域在国家区域发展战略中的地位在一定程度上不相匹配。“胡焕庸线”能较好地反映国家层面的城市之间人口日常流动格局,反映了地理环境对城市间人口日常流动的深刻影响。城市之间的人口流动强度是体现区域经济联系强度、城市等级和网络结构等的重要指标,此项研究可为形成国家区域经济发展新格局和促进区域平衡发展提供参考。

[ Liu

Wangbao

, Shi

Enming

. Spatial pattern of population daily flow among cities based on ICT: A case study of "Baidu Migration"
Acta Geographica Sinica, 2016, 71(10):1667-1679.]. DOI: 10.11821/dlxb201610001.

[本文引用: 2]

With the advent of the Internet era, network data has become an important carrier characterizing residents' geography behavior. The residents' migration, social network, mobile communications and other geographic behavior big data have become an important data source for urban interactive relationship research. "Baidu Migration" big data can fully, dynamically, immediately and visually record population migration trajectory with LBS technology. Through collecting population daily flow among 369 cities in China during the period from February 7 to May 16 in 2015 in "Baidu Migration" and extracting six periods with "Quarter average, Spring Festival Transportation (before Festival), Spring Festival Transportation (after Festival), Labor Day, weekends, workdays", this paper tries to analyze and compare the characteristics and spatial patterns of daily flow among cities from the aspects of "population daily flow distribution levels, flow distribution layers network aggregation, spatial patterns and its relationship with the 'Hu Huanyong Line' for population flow". This paper analyzes the characteristics and spatial pattern of population daily flow among cities in each period. The result shows that "Baidu migration" big data clearly shows the pattern of population flow between the central and coastal areas in China during the period of Spring Festival Transportation. Main flow assembling centers are distributed in the urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing, and those centers have strong coherence with those urban hierarchies in each period. Clear hierarchical structure and level distinction can be identified in the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei, Chengdu-Chongqing and Urumqi assembling systems in each period. But Central China, Northeast China, Southwest China and coastal Fujian do not present a pattern of higher level of urban assembling centers and high hierarchical assembling systems and those conditions do not match the status in national regional development strategy of those areas. The "Hu Huanyong Line" can reflect the spatial patterns of population daily flow at national level, and the profound influence of geographical environment on the population daily flow among cities. The intensity of population flow among cities is an important indicator of intensity of regional economic relationship, urban hierarchy and network structure. Spatial patterns of population flow showed in this paper can provide reference for the formation of the new pattern of regional economic development and the promotion of regional balanced development.

[7]

魏冶, 修春亮, 刘志敏, 等. 春运人口流动透视的转型期中国城市网络结构
地理科学, 2016, 36(11):1654-1660.

DOI:10.13249/j.cnki.sgs.2016.11.007 [本文引用: 1]

基于春运人口流动大数据,选取对外联系度、优势流、城市位序-规模分析等方法对转型期中国城市网络特征进行分析。结果显示：① 城市网络层级结构中蕴藏着位序-规模规律,但与理想的帕累托分布有所区别,城市规模彼此差异相对较小;② 空间距离与城市等级在城市网络联系中发挥支配性作用,保证了城市网络的层级性与有序性;③ 中国城市网络核心联系呈现“两横三纵”特征,该特征与铁路大动脉的空间分布高度吻合;④ 东部地区城市网络联系更加密切,而西北、西南地区则相对稀疏,基本上以“胡焕庸线”为界,而“兰新线”是突破这一限制的潜在力量;⑤ 中国东北地区未形成明显的区域性中心,城市联系形成带状网络;⑥ 华北与华南地区的“灯下黑”现象值得警惕,缓解这一问题的可行办法是核心城市功能的对外疏散,加强核心城市与周边城市之间的联系;⑦ 带状区域发展或许将成为未来中国区域经济发展的流行模式和中坚力量。总体上看,针对于揭示转型期中国城市网络结构特征,春运人口流动数据具有一定的研究价值,是城市与人口研究领域一个值得深入挖掘的重要数据源。

[ Wei

, Xiu

Chunliang

, Liu

Zhimin

, et al. Spatial pattern of city network in transitional China based on the population flows in “Chunyun” period
Scientia Geographica Sinica, 2016, 36(11):1654-1660.]. DOI: 10.13249/j.cnki.sgs.2016.11.007.

[本文引用: 1]

<p> The space of flows theory proposed by Manuel Castells has given birth to the network perspective of city network. In contrast with the traditional perspective of urban system which is based on the central place theory, city network perspective pays more attention to the interactions and linkages between cities and regions. Thus, the city network is becoming one of the new and hot topics in the field of urban geography. “Chunyun” is a well-known socio-economic phenomenon unique to transitional China, which refers to a blooming of population flows during the Spring Festival, or refers to the period when the blooming happens. Previously, the real population flows data in “Chunyun” were difficult to obtain. The situation had not changed until the “Big data on human migration during the spring festival from Baidu map” (“Baidu migration data” in short) came up. The big data were gathered from the locations provided by hundreds of millions smart phone users through Location Based Service (LBS) Baidu map data source, and was published in the form of interactive heat map that displays people’s travel routes in China during the Chunyun period. Based on Baidu migration data, using degree of external linkages, dominant flows and network-based rank-size analysis, the spatial pattern of city network in transitional China was studied in this article. The study process certificates that Baidu migration data is indeed a high quality data sources for the study of city network, and has turned up some interesting results: 1) The distribution of external linkage degree of cities in China follows Zipf’s law, but differs from the ideal Pareto distribution. 2) The factor of spatial distance and city level play key roles in the formation of urban network of China, and ensure the hierarchy and regularity of the network. 3) The spatial distribution of core linkages in the city network could be summarized as “Three-horizontal & Two-longitudinal”, which almost coincides with the rail arteries in China. The “Three-horizontal & Two-longitudinal” linkages are skeletons of the city network, which matter a great deal in building the interregional contact and coordinating the interregional relationship. 4) In overall, the strength of interactions within the city network differs between East and West China, and Hu's line is apparently the dividing line. As potential powers, the linkages along the Lanzhou-Xinjiang railway line have the opportunity to break the Hu's line. 5) Northeast China lacks regional centers and the linkages between cities forms a bunchy network. 6) There is a phenomenon of “near field deprivation” in North China and South China, that is, the core city have strong interactions with outer regions, but the smaller cities in its near field have poor external linkages with the core city and outer regions. 7) Belt-shaped region may become a popular mode and the future backbone of regional economic development in China.</p>

[8]

甄峰, 王波, 陈映雪. 基于网络社会空间的中国城市网络特征: 以新浪微博为例
地理学报, 2012, 67(8):1031-1043.

DOI:10.11821/xb201208003 [本文引用: 1]

信息技术影响下的城市区域空间结构变化得到了国内外****的关注。本文以新浪微博为例, 从网络社会空间的角度入手, 对中国城市网络发展特征进行了研究。研究表明:微博社会空间视角下的中国城市网络存在着明显的等级关系与层级区分, 城市的网络连接度与城市等级表现出了相对一致性。根据城市网络层级与网络联系强度, 东部、中部、西部3 大区域板块的网络联系差异明显, 东部地区内部的联系, 以及东部与中部地区和西部地区的联系几乎构成当前网络体系中的全部。城市网络呈现出分层集聚现象, 具体表现为“三大四小”发展格局, 即京津冀区域、珠三角区域、长三角区域、成渝地区、海西地区、武汉地区、东北地区。高等级城市在整个城市网络中处于绝对支配地位, 北京以突出的优势成为全国性的网络联系中心, 而上海、广州、深圳则成为全国性的网络联系副中心。

[ Zhen

Feng

, Wang

, Chen

Yingxue

. China's city network characteristics based on social network space: An empiricalanalysis of sina micro-blog
Acta Geographica Sinica, 2012, 67(8):1031-1043.]. DOI: 10.11821/xb201208003.

[本文引用: 1]

[9]

董超, 修春亮, 魏冶. 基于通信流的吉林省流空间网络格局
地理学报, 2014, 69(4):510-519.

DOI:10.11821/dlxb201404007 [本文引用: 2]

基于实际发生的信息流研究流空间网络格局是一种新的尝试。以吉林省县级以上地方为研究单元,以各地间固定电话通话时长为原始数据,采用主成分分析法、C-Value 和D-Value层级分析法、优势流分析法、最小生成树法对吉林省流空间格局进行了分析。研究表明：吉林省流空间是以长春市为中心,长春市、延吉市、通化市、公主岭市为主导型城市,吉林市、白城市、白山市、辽源市、松原市、四平市为次级主导型城市,其他城市为从属型城市的层级化网络结构；行政区划在流空间格局中发挥基本的影响作用；以长春市为“单中心”的流空间特征明显,吉林省并无明显的次中心作用,长春市与吉林市流空间联系并不紧密,与传统认识和意愿有所不同；公主岭市和敦化市在吉林省流空间格局中占有重要地位,公主岭市倾向于融入长春城市圈,敦化市在吉林省东部空间网络格局中发挥了重要作用,两市的区域联通功能亟待挖掘；四平市和梨树县流空间联系紧密,适宜同城化发展。

[ Dong

Chao

, Xiu

Chunliang

, Wei

. Network structure of 'space of flows' in Jilin province based on telecommunication flows
Acta Geographica Sinica, 2014, 69(4):510-519.]. DOI: 10.11821/dlxb201404007.

[本文引用: 2]

[10]

Liang

Shuangbo

, Cao

Youhui

, Wu

Wei

, et al. International freight forwarding services network in the Yangtze River Delta, 2005-2015: Patterns and mechanisms
Chinese Geographical Science, 2019, 29(1):112-126. DOI: 10.1007/s11769-019-1018-2.

[本文引用: 1]

This study examined the spatio-temporal trajectories of the international freight forwarding service (IFFS) in the Yangtze River Delta (YRD) and explored the driving mechanisms of the service. Based on a bipartite network projection from an IFFS firm-city data source, we mapped three IFFS networks in the YRD in 2005, 2010, and 2015. A range of statistical indicators were used to explore changes in the spatial patterns of the three networks. The underlying influence of marketization, globalization, decentralization, and integration was then explored. It was found that the connections between Shanghai and other nodal cities formed the backbones of these networks. The effects of a city's administrative level and provincial administrative borders were generally obvious. We found several specific spatial patterns associated with IFFS. For example, the four non-administrative centers of Ningbo, Suzhou, Lianyungang, and Nantong were the most connected cities and played the role of gateway cities. Furthermore, remarkable regional equalities were found regarding a city's IFFS network provision, with notable examples in the weakly connected areas of northern Jiangsu and southwestern Zhejiang. Finally, an analysis of the driving mechanisms demonstrated that IFFS network changes were highly sensitive to the influences of marketization and globalization, while regional integration played a lesser role in driving changes in IFFS networks.

[11]

张宇, 梁双波, 曹卫东, 等. 长江三角洲船舶代理服务业空间组织网络时空演化: 基于两省一市分析
人文地理, 2018, 33(2):92-99.

[本文引用: 1]

[ Zhang

, Liang

Shuangbo

, Cao

Weidong

, et al. Space-time evolution of spatial organization network of shipping agency service industry in the Yangtze Riwer Delta: An analysis based on two provinces and one city
Human Geography, 2018, 33(2):92-99.]. DOI: 10.13959/j.issn.1003-2398.2018.02.012.

[本文引用: 1]

[12]

Scott

. Transnational exchanges amongst skilled British migrants in Paris. Population,
Space and Place, 2004, 10(5):391-410. DOI: 10.1002/psp.345.

URL [本文引用: 1]

[13]

Castells

. The new public sphere: Global civil society, communication networks and global governance
Annals of the American Academy of Political and Social Science, 2008, 616(1):78-93.

DOI:10.1177/0002716207311877 URL [本文引用: 1]

[14]

Haitao

, Fang

Chuanglin

, Lin

Sainan

, et al. Hierarchy, clusters, and spatial differences in Chinese inter-city networks constructed by scientific collaborators
Journal of Geographical Sciences, 2018, 28(12):1793-1809. DOI: 10.1007/s11442-018-1579-5.

[本文引用: 1]

The Chinese urban system is currently experiencing a fundamental shift, as it moves from a size-based hierarchy to a network-based system. Contemporary studies of city networks have tended to focus on economic interactions without paying sufficient attention to the issue of knowledge flow. Using data on co-authored papers obtained from China Academic Journal Network Publishing Database (CAJNPD) during 2014-2016, this study explores several features of the scientific collaboration network between Chinese mainland cities. The study concludes that: (1) the spatial organization of scientific cooperation amongst Chinese cities is shifting from a jurisdiction-based hierarchical system to a networked system; and (2) several highly intra-connected city regions were found to exist in the network of knowledge, and such regions had more average internal linkages (14.21) than external linkages (8.69), and higher average internal linkage degrees (14.43) than external linkage degrees (10.43); and (3) differences existed in terms of inter-region connectivity between the Western, Eastern, and Central China regional networks (the average INCD of the three regional networks were 109.65, 95.81, and 71.88). We suggest that China should engage in the development of regional and sub-regional scientific centers to achieve the goal of building an innovative country. Whilst findings reveal a high degree of concentration in those networks - a characteristic which reflects the hierarchical nature of China’s urban economic structure - the actual spatial distribution of city networks of knowledge flow was found to be different from that of city networks based on economic outputs or population.

[15]

刘承良, 牛彩澄. 东北三省城际技术转移网络的空间演化及影响因素
地理学报, 2019, 74(10):2092-2107.

DOI:10.11821/dlxb201910010 [本文引用: 1]

从全国—本地视角,以东北三省为研究区,基于2005-2015年的专利权转移数据,融合社会网络、GIS空间分析和计量方法,定量刻画东北三省技术转移网络的空间演化规律。结果显示：① 全国视角下东北三省城际技术转移网络呈现“核心—边缘”等级层次性结构,形成了专利技术由东北辐散向全国沿海辐合的空间格局。② 本地视角下东北三省技术转移网络呈现出向心收缩结网态势,“哈长沈大”四大核心城市在本地网络中扮演“技术守门者”角色。技术转移表现出“强全国化,弱本地化”特征。③ 东北三省城际技术流动既存在路径依赖,也不断涌现路径创造。全国视角下,技术转移以东北三省核心城市为流源,基本流向以北京、上海和深圳分别为枢纽的京津冀、长三角和珠三角城市群。本地城际技术转移以哈尔滨、长春、沈阳、大连为集散中心,集中于省内转移,呈现等级、接触和跳跃式混合扩散空间模式。④ 地理距离接近度、产业结构相似度、经济水平差异度、创新能力相似度、技术吸收能力、外商直接投资对东北三省城际技术转移存在一定影响。

[ Liu

Chengliang

, Niu

Caicheng

. Spatial evolution and factors of interurban technology transfer network in Northeast China from national to local perspectives
Acta Geographica Sinica, 2019, 74(10):2092-2107.]. DOI: 10.11821/dlxb201910010.

[本文引用: 1]

Interurban technology transfer becomes an essential channel for regions or cities to obtain external knowledge. Based on patent transaction data among cities during 2005-2015, this study investigates the interurban technology transfer network of Northeast China, aiming to explore spatial evolution of technology transfer network in this region from national to local perspectives based on social network analysis (SNA). A negative binomial regression analysis further reveals the factors of interurban technology transfer network. The results of the study are as follows: (1) From the national perspective, the interurban technology transfer network of Northeast China presents a core-periphery structure. The spatial pattern of "divergence in the northeast region" and "convergence in the coastal areas" has been formed. (2) From the local perspective, the technology transfer network of Northeast China shows a centripetal contraction situation, and its four hubs, namely, Harbin, Changchun, Shenyang and Dalian, play the role of technology gatekeeper. The interurban technology transfer flows present the characteristic of strengthening nationalization and weakening localization, which are more likely to emerge between the Northeast-Southeast China rather than among the Northeast China. (3) Both path-dependence and path-creation exist in the spatial dynamics of intercity technology flows in Northeast China. From the national perspective, technology flows from Northeast China to the Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta urban agglomerations with Beijing, Shanghai and Shenzhen as the core respectively, while the local intercity technology transfer in Northeast China presents a mixed diffusing mode including hierarchical, contagious and jump diffusions. In addition, the local network mainly focuses on intra-provincial technology flows which centered on Haibin, Changchun, Shenyang and Dalian. (4) Some drivers, such as geographical proximity, the similarity of industrial structure, economic differences, the similarity of innovation capability, technology absorptive capacity, foreign direct investment, are evidenced to play a significant or determining role in interurban technology transfer of Northeast China.

[16]

蒲英霞, 韩洪凌, 葛莹, 等. 中国省际人口迁移的多边效应机制分析
地理学报, 2016, 71(2):205-216.

DOI:10.11821/dlxb201602003 [本文引用: 1]

区际人口迁移不仅与迁出地和目的地的要素特征以及距离有关,而且还受到周边迁移流的影响.基于网络自相关理论,利用"六普"省际人口迁移数据和相关统计资料,在重力模型的基础上考虑迁移流之间可能存在的几种空间依赖形式,构建中国省际迁移流的空间OD模型,初步揭示区域经济社会等因素及其空间溢出效应对省际人口迁移的影响,并就区域要素变化对整个省际人口迁移系统产生的"连锁反应"进行了模拟.结果表明:① 中国省际迁移流之间存在显著的网络自相关效应.目的地和迁出地的自相关效应皆为正,导致迁入和迁出流的空间效仿行为;迁出地和目的地周边则出现负的自相关效应,导致迁移流的空间竞争行为;② 区域经济社会等因素通过网络空间关系对周边地区产生的多边溢出效应导致迁移流在空间上集聚.其中,距离衰减效应位居各要素之首,其溢出效应进一步加剧距离的摩擦作用;对目的地而言,区域工资水平和迁移存量超过GDP的影响并产生正的溢出效应,促进周边地区吸引更多的外来人口;对迁出地而言,人口规模和迁移存量产生正的溢出效应,推动周边地区人口外迁;③ 区域要素变化潜在地对整个省际人口迁移系统产生一系列"连锁反应",震荡中心及其周边区域的迁移流波动较大.江苏省GDP增长5%的模拟结果表明,江苏迁往全国其他省份的人口数量都有不同程度地减少,而其他省份入迁人口均有所增加.相对而言,江苏周边省份的迁入或迁出流受到的波动较大,偏远省份波及较小,这是传统的重力模型所无法解释的.

[ Pu

Yingxia

, Han

Hongling

, Ge

Ying

, et al. Multilateral mechanism analysis of interprovincial migration flows in China
Acta Geographica Sinica, 2016, 71(2):205-216.]. DOI: 10.11821/dlxb201602003.

[本文引用: 1]

Population migration flows between different regions are related to not only the origin- and destination-specific characteristics, but also to the migration flows to and from neighborhoods. Intuitively, changes in the characteristics of a single region will impact both inflows and outflows to and from other regions. In order to explore the spatial interaction mechanism driving the increasing population migration in China, this paper builds the spatial OD model of interprovincial migration flows based on the sixth national population census data and related social-economic data. The findings are as follows: (1) Migration flows show significant autocorrelation effects among origin and destination regions, which means that the migration behavior of migrants in some region is influenced by that of migrants in other places. The positive effects indicate the outflows from an origin or the inflows to a destination tend to cluster in a similar way. Simultaneously, the negative effects suggest the flows from the neighborhood of an origin to the neighborhood of a destination tend to disperse in a dissimilar way. (2) Multilateral effects of the regional economic and social factors through the spatial network system lead to the clustering migration flows across interrelated regions. Distance decay effect plays the most influential force in shaping the patterns of migration flows among all the factors and the negative spillover effect further aggravates the friction of distance. As for destinations, the influence of wage level and migration stocks is beyond that of GDP and the positive spillover effects of these factors enhance the attraction of neighborhood regions. The spillover effects of unemployment rate and college enrollment of higher education are significantly negative while the effect of population in a destination is not significant. As for origins, population and migration stocks lead to positive spillover effects on the neighborhoods while the effects of other factors are negative. (3) Changes in the regional characteristics will potentially lead to a series of events to the whole migration system, and the flows to and from the center of oscillation and its neighborhoods vibrate greatly compared with other regions. The simulation results of 5% GDP increase in Jiangsu province indicate that the outflows to other regions decrease while the inflows from all others increase to some different extent. Comparatively, the influence on the flows to and from the regions neighboring Jiangsu is significant while that of remote regions is much less, which cannot be explained by the traditional gravity model.

[17]

古恒宇, 沈体雁, 刘子亮, 等. 基于空间滤波方法的中国省际人口迁移驱动因素
地理学报, 2019, 74(2):222-237.

DOI:10.11821/dlxb201902002 [本文引用: 1]

人口迁移数据中往往存在较强的网络自相关性,以往基于最小二乘估计的重力模型与迁移数据的拟合度较低,而改进后的泊松重力模型仍存在过度离散的缺陷,以上问题均导致既有人口迁移模型中的估计偏差。本文构建了特征向量空间滤波（ESF）负二项重力模型,基于2015年全国1%人口抽样调查数据,研究2010-2015年中国省际人口迁移的驱动因素。结果表明：① 省际人口迁移流间存在显著的空间溢出效应,ESF能有效地提取数据中的网络自相关性以降低模型的估计偏差,排序在前1.4%的特征向量即可提取较强的网络自相关信息。② 省际人口迁移流之间存在明显的过度离散现象,考虑到数据离散的负二项重力模型更适用于人口迁移驱动因素的估计。③ 网络自相关性会导致模型对距离相关变量估计的上偏与大部分非距离变量估计的下偏,修正后的模型揭示出以下驱动因素：区域人口特征、社会网络、经济发展、教育水平等因素是引发省际人口迁移的重要原因,而居住环境与公路网络等因素也逐渐成为影响人口迁移重要的“拉力”因素。④ 与既有研究相比,社会网络因素(迁移存量、流动链指数)对人口迁移的影响日益增强,而空间距离对人口迁移的影响进一步呈现弱化趋势。

[ Gu

Hengyu

, Shen

Tiyan

, Liu

Ziliang

, et al. Driving mechanism of interprovincial population migration flows in China based on spatial filtering
Acta Geographica Sinica, 2019, 74(2):222-237.]. DOI: 10.11821/dlxb201902002.

[本文引用: 1]

According to previous studies, not only does the conditional gravity model based on ordinary least squares often bring about poor fitting of migration flows in reality, but also there exists overdispersion in the extended Poisson gravity model. Simultaneously, network autocorrelation usually exists in population migration data （e.g., the spatial interaction among migration flows). The problems mentioned above result in biased estimation. In order to capture network autocorrelation and deal with the issue of overdispersion, we build an eigenvector spatial filtering negative binomial gravity model (ESF NBGM) based on the data of 1% national population sample survey in 2015, to analyze the driving mechanism of interprovincial population migration flows in China. The results are as follows: (1) Positive spatial spillover effect exists in interprovincial population migration flows, and ESF can capture network autocorrelation in data, so as to reduce the estimated deviation of the model. Furthermore, eigenvectors ranking top 1.4% can properly interpret the spatial pattern of high network autocorrelation in data. (2) There exists overdispersion in China's interprovincial migration flows. Considering this problem, a negative binomial regression model is more suitable for the estimation of driving mechanism for population migration, together with statistical enhancement. (3) Network autocorrelation leads to overestimation of distance variables and underestimation of non-distance variables. The results of the improved model reveal that: chief factors the affect driving mechanism are regional population characters, social network, economic development and education level. Meanwhile, living environment and road network gradually become one of the most crucial pulling factors that influence migration flows. (4) Compared to previous studies, social network (i.e. migration stock) plays a more significant role in population migration flows, while the impact of spatial distance keeps weakening.

[18]

Montis A

, Caschili

, Chessa

. Time evolution of complex networks: Commuting systems in insular Italy
Journal of Geographical Systems, 2011, 13(1):49-65. DOI: 10.1007/s10109-010-0130-8.

URL [本文引用: 1]

[19]

吴康, 方创琳, 赵渺希, 等. 京津城际高速铁路影响下的跨城流动空间特征
地理学报, 2013, 68(2):159-174.

[本文引用: 1]

[ Wu

Kang

, Fang

Chuanglin

, Zhao

Miaoxi

, et al. The intercity space of flow influenced by high-speed rail: A case study for the rail transit passenger behavior between Beijing and Tianjin
Acta Geographica Sinica, 2013, 68(2):159-174.]. DOI: 10.11821/xb201302002.

[本文引用: 1]

[20]

Chen

Wei

, Liu

Weidong

, Ke

Wenqian

, et al. Understanding spatial structures and organizational patterns of city networks in China: A highway passenger flow perspective
Journal of Geographical Sciences, 2018, 28(4):477-494. DOI: 10.1007/s11442-018-1485-x.

URL [本文引用: 2]

[21]

陈伟, 修春亮, 柯文前, 等. 多元交通流视角下的中国城市网络层级特征
地理研究, 2015, 34(11):2073-2083.

DOI:10.11821/dlyj2015110006

交通流是人流、物流等要素流动的主要载体和表现形式,对于认识城市间相互作用等具有重要意义。基于城市间公路、铁路和航空客流数据,对中国城市网络空间关联进行特征提取和规律挖掘。研究表明：① 公路流表现出强烈的空间依赖性和对城市群发育程度的良好识别作用;② 铁路流呈现出“两横三纵”带状分布格局;③ 航空流视角则基本形成了以“菱形结构”为核心的城市网络框架。不同类型交通流刻画出不同层面的城市间关联格局,但却有着其内在联系。航空流是城市关联格局骨架构筑的主要形式,铁路流则为核心骨架的连通提供支撑轴带,而公路流是对整体骨架和支撑轴带的有效填充,从而形成区域间相互依赖、不可或缺的要素关联和空间关系。

[ Chen

Wei

, Xiu

Chunliang

, Ke

Wenqian

, et al. Hierarchical structures of China's city network from the perspective of multiple traffic flows
Geographical Research, 2015, 34(11):2073-2083.]. DOI: 10.11821/dlyj2015110006.

[22]

王姣娥, 景悦. 中国城市网络等级结构特征及组织模式: 基于铁路和航空流的比较
地理学报, 2017, 72(8):1508-1519.

DOI:10.11821/dlxb201708013 [本文引用: 4]

交通流是反映城市间社会经济联系的重要表征,被广泛应用于城市网络研究中。基于2010年中国城际铁路与航空客流OD数据,本文从城市节点、流量、子网络视角对中国城市网络的结构特征与组织模式进行了比较研究,发现：① 铁路与航空流视角下的中国城市网络均呈现出以北上广为顶层节点的空间等级结构体系,但除顶层结构外两种网络结构差异较大。② 城市网络体系中的铁路流联系表现出空间邻近性特征,而航空流联系则主要受到城市节点的规模大小与职能属性的影响。③ 铁路流的首位联系受省级行政区划的制约,航空流的首位联系空间跨度大,形成了若干具有垂直层间联系的地域子系统。④ 铁路网络拥有具有显著地域特征的7个子网络,而航空网络中则不存在明显的子网络。技术经济特征与管理体制是造成铁路与航空两种网络特征差异的主要原因。

[ Wang

Jiao'e

, Jing

Yue

. Comparison of spatial structure and organization mode of inter-city networks from the perspective of railway and air passenger flow
Acta Geographica Sinica, 2017, 72(8):1508-1519.]. DOI: 10.11821/dlxb201708013

[本文引用: 4]

[23]

吴志峰, 柴彦威, 党安荣, 等. 地理学碰上“大数据”: 热反应与冷思考
地理研究, 2015, 34(12):2207-2221.

DOI:10.11821/dlyj201512001 [本文引用: 1]

互联网时代的“大数据”热潮迅猛波及到经济社会的各个领域,地理学是大数据研究与应用的天然试验场。聚焦地理学与大数据的碰撞,回顾大数据在地理学研究中的应用探索,重点讨论大数据给地理学研究与发展带来的机遇与挑战。讨论认为：大数据已经对地理学研究产生了一定的影响。其中,人文地理学领域的反应最为热烈,基于大数据的研究案例纷纷呈现;地理信息科学在互联网大数据时代将会更加迅猛发展,自然地理学领域正在寻找和等待爆发点。目前,大数据还不能改变地理学的核心命题与基本范式,在坚持地理学核心思想的同时,应该对地理学领域有关大数据理论的探讨与应用尝试持有一种开放包容的态度。

[ Wu

Zhifeng

, Chai

Yanwei

, Dang

Anrong

, et al. Geography interact with big data: Dialogue and reflection
Geographical Research, 2015, 34(12):2207-2221.]. DOI: 10.11821/dlyj201512001.

[本文引用: 1]

In the internet era, "Big data" wave spread rapidly to the economic and social fields. Geography is the natural laboratory in which big data research and application can be seen at work. The written speech focused on collision between geography and big data. It reviewed big data research and application in geography study. We also discussed the opportunities and challenges we would face during this collision. In summary, big data has had a certain influence on the geography research, especially in the human geography domain. Geographic information science will develop rapidly in the internet era of big data. But there are few disturbances in physical geography. Big data can not change the core proposition and the basic paradigm of geography. We should hold an open inclusive attitude to big data theory study and application research in geography.

[24]

赵梓渝, 王士君. 2015年我国春运人口省际流动的时空格局
人口研究, 2017, 41(3):101-112.

[本文引用: 1]

[ Zhao

Ziyu

, Wang

Shijun

. A spatial-temporal study of inter-provincial migration pattern during Chinese Spring Festival travel rush
Population Research, 2017, 41(3):101-112.]

[本文引用: 1]

[25]

孙玺箐, 司守奎. 复杂网络算法与应用. 北京: 国防工业出版社, 2015: 24-67.

[本文引用: 3]

[ Sun

Xiqing

, Si

Shoukui

. Complex Network Algorithms and Applications. Beijing: National Defense Industry Press, 2015: 24-67.]

[本文引用: 3]

[26]

吴康, 方创琳, 赵渺希. 中国城市网络的空间组织及其复杂性结构特征
地理研究, 2015, 34(4):711-728.

DOI:10.11821/dlyj201504010 [本文引用: 1]

全球化、信息化与快速城市化深刻影响了中国的城市体系,多区位企业组织所形成的城市网络正处于日益复杂的空间嬗变过程。基于2010年企业名录的总部—分支机构型关联数据,研究构建了330×330的地级以上城市网络连接关系,并运用复杂网络分析工具来探索中国城市网络的空间组织特征。研究发现：① 中国的城市网络联系呈现以“北京—上海—广深—成都”为核心的菱形空间结构,不同等级的网络流强度具有显著的空间异质性,城市网络的空间组织是一个择优性和地理邻近性复杂作用的过程;② 中国城市网络正处于一个简单随机向复杂有序结构的转化期,整体大尺度的网络结构还有待形成;③ 中国城市网络整体表现出明显的小世界网络效应;④ 中国城市的二值点度网络为明显的异配性连接特征,而加权强度网络连接则一定程度上表现出“富人圈”的现象;⑤ 中国城市网络的层级性并不明显,城市网络的点度和强度的关系呈非线性增加特征。

[ Wu

Kang

, Fang

Chuanglin

, Zhao

Miaoxi

. The spatial organization and structure complexity of Chinese intercity networks
Geographical Research, 2015, 34(4):711-728]. DOI: 10.11821/dlyj201504010.

[本文引用: 1]

Fuelled by globalization, informatization and rapid urbanization, the Chinese urban system has witnessed dramatic changes in the past four decades, which shows a combined changing characteristic in both expanded geographical scope and intensified intercity connections. This paper investigates an integrated network-based approaches and spatial analysis to explore the spatial organization process and the basic regularity of Chinese intercity networks. More specifically, this study examines how 330 Chinese cities are connected through 108,570 ownership linkages of 307,915 local corporations for the year 2010. Major findings include: (1) the backbone of the Chinese intercity corporate network is diamond-shaped and anchored by four major metropolitan areas (Beijing in the North; Shanghai, East; Guangzhou-Shenzhen, South; Chengdu, West), intercity network strengths reveal a significant spatial heterogeneity; (2) urban network organization is a complicated process that involve both preferential attachment and geographic proximity interactions; (3) the overall structure of the intercity corporate networks undergo a transition process that from a simple random period to a complex but orderly one and also features small-world network properties; (4) city degree distribution of Chinese intercity networks is characterized by weak assortativity and rich-club effects; and (5) a combination interpretation of clustering coefficient and degree distribution identifies hierarchical and regional tendencies.

[27]

Meo P

, Ferrara

, Fiumara

, et al. Fast unfolding of communities in large networks
Journal of Statistical Mechanics Theory & Experiment, 2008, (10):155-168. DOI: 10.1088/1742-5468.

[本文引用: 1]

[28]

赵梓渝, 魏冶, 庞瑞秋, 等. 中国春运人口省际流动的时空与结构特征
地理科学进展, 2017, 36(8):952-964.

DOI:10.18306/dlkxjz.2017.08.00 [本文引用: 1]

本文通过构建人口省际流动的关系矩阵,透视2015年中国春节期间人口省际流动的动态过程、网络特征,并对各省人口流入、流出的规模差异进行测度。研究结论如下：①春节前后中国各省每日人口净流入人次存在显著的规律性特征,2月13-17日和2月25日-3月1日为各省节前返乡流和节后返工流期间人口净流入、流出峰值时段,可作为基于春运研究中国人口省际流动的重要依据;②识别了14个人口净流入省、17个人口净流出省,净流入位序前六省和后八省分别吸纳和提供了全国9成人口的集聚与扩散。人口主要流入省的沿海绵延和主要流出省的中部“人口塌陷”共同构成了双纵格局;③各省流出首位流的指向特征显著,南方人口流出省的流出指向存在共性,京津、长三角、珠三角地区的人口集聚路径存在差异。④移动数据为发掘人口流动过程中蕴含的丰富信息提供了平台,基于节后、节前净流入值差值的方法可以有效识别中国人口流动的规模差异、属性特征。通过与以往研究对比,证实了移动数据与基于人口普查数据研究的诸多结论相似性。

[ Zhao

Ziyu

, Wei

, Pang

Ruiqiu

, et al. Spatiotemporal and structural characteristics of interprovincial population flow during the 2015 Spring Festival travel rush
Progress in Geography, 2017, 36(8):952-964.]. DOI: 10.18306/dlkxjz.2017.08.004.

[本文引用: 1]

China is one of the most active areas of the world's population mobility. The social structure of China during the socioeconomic transition period, the country's development phase, and its unique cultural background together form the Spring Festival travel rush, a social behavioral phenomenon with a significant regularity and a high degree of uniformity and unity. By constructing the 2015 Spring Festival interprovincial population flow relation matrix, we examined the dynamics of population flow and its spatial characteristics. The results are as follows: (1) 13-17 February and 25 February-1 March were the peak population flow periods before and after the holiday season. Inflow and outflow of population between provinces during these time periods can be indicative of interprovincial migration of floating population in China. We identified 14 net population inflow provinces and 17 net population outflow provinces. The top six and bottom eight population inflow provinces in the eastern and central regions form the double vertical pattern of immigration and emigration of floating population in China. (2) Provincial population outflow primary directions are clear and flow from the central to the eastern coastal areas is the main direction and path of migration of the floating population. Guangdong and Beijing are the primary migration destinations of the floating population in southern and northern china. These two province/municipality monopolized 2/3 of the interprovincial population flow of the country. (3) The source areas of the floating population in the Beijing and Tianjin area, the Yangtze River Delta, and the Pearl River Delta—the three major population agglomeration areas—are significantly different. Those in the Beijing and Tianjin area and the Pearl River Delta are mainly directly from the floating population emigration provinces, but the Yangtze River Delta has formed a more advanced network structure. (4) Mobility-based study on the temporal and spatial characteristics of China's population flow contains a wealth of information on floating population migration, and the Spring Festival travel rush provides an opportunity for such study. By comparing the result with previous research results, similarity between the new data and many conclusions based on the census data is clear.