Genoveva Vargas-Solar
email: genoveva.vargas@imag.fr

Ana-Sagrario Castillo-Camporro

José Luis Zechinelli-Martini

Javier A Espinosa-Oviedo

Smart Carlos

Alberto Ochoa

Innovative

From Data Harvesting to Querying for Making Urban Territories Smart

Keywords: urban computing, data analytics, data indexing, smart cities

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The development of digital technologies in the different disciplines, in which cities operate, either directly or indirectly, is altering expectations among those in charge of the local administration. Every city is a complex ecosystem with subsystems to make it work such as work, food, clothes, residence, offices, entertainment, transport, water, energy etc. With the growth of cities, there is more chaos and most decisions are politicized, there are no common standards and data is overwhelming. The intelligence is sometimes digital, often analogue, and almost inevitably human.

Urban computing [36] is a world initiative leading to better exploit resources in a city to offer higher-level services to people. It is related to sensing the city's status and acting in new intelligent ways at different levels: people, government, cars, transport, communications, energy, buildings, neighbourhoods, resource storage, etc. A vision of the city of the "future", or even the city of the present, rests on the integration of science and technology through information systems.

Data-centric solutions are in the core of urban computing that aims at understanding events and phenomena emerging in urban territories, predict their behaviour and then use these insights and foresight to make decisions. Data analytics and exploitation techniques are applied in different conditions and using ad hoc methodologies using data collections of different types. Today important urban computing centres in metropolises, have proposed and applied these techniques in these cities for studying real state, tourism, transport, energy, air, happiness, security and wellbeing. The adopted strategies have to do with the type of context in which they work.

This chapter provides a summarized, critical and analytical point of view of the data-centric solutions that are currently applied for addressing urban problems in cities leading the use of urban computing techniques to address their daily life issues. The chapter puts in perspective families of strategies developed in the state of the art for addressing given urban problems and exhibits guidelines that can lead to a methodological understanding of these strategies. Current solutions address a spectrum that goes from data harvesting techniques to decision making support. The chapter describes them and discusses their main characteristics.

Accordingly, the chapter is organised as follows. Section 2 characterises urban data and introduces data harvesting techniques used for collecting urban data. Section 3 discusses approaches and strategies for indexing urban data. Section 4 describes urban data querying. Section 5 summarizes data and knowledge fusion techniques. Finally, Section 6 discusses the research and applied perspectives of urban computing for concluding the chapter.

Data Harvesting Techniques in Urban Computing

Urban computing is an interdisciplinary field which concerns the study and application of computing technology in urban areas. A new research opportunity emerges in the database domain for providing methodologies, algorithms and systems to support data processing and analytics processes for dealing with urban computing. These processes involve harvesting data about the urban environment to help improve the quality of life for people in urban territories like cities. In this context academic and industrial contributions have proposed solutions for building networks of data, retrieving, analysing and visualizing them for fulfilling analytics requirements stemming from urban computing studies and projects.

Urban data processing is done using: (i) continuously harvested observations of the geographical position of individuals (that accept sharing their position) along time;

(ii) collections of images stemming from cameras observing specific "critical" urban areas, like terminals, airports, public places and government offices; (iii) data produced by social networks and applications like Twitter, Facebook, Waze and similar.

Independently of the harvesting strategies and processing purposes, it is important to first characterise urban data. This is done in the next section.

Urban Data

Urban data can be characterized concerning three properties: time, space and objects (occupying urban territories). They are elementary properties that can guide the way urban data can be harvested and then processed for understanding urban phenomena. For urban data, time must be considered from two perspectives, as its mathematical definition as a continuous or discrete linearly ordered set consisting of time instants or time intervals, called time units [3]. But, also under a cyclic perspective to consider iterations of seasons, weeks and days. Regarding space, it can be represented by [3] different referencing models: coordinate-based models with tuples of numbers representing the distance to certain reference points or axes; division-based models using a geometric or semantic-based division of space; and linear models with relative positions along with linear reference elements, such as streets, rivers and trajectories. Finally, the third urban data property, object, refers to physical and abstract entities having a certain position in space (e.g., vehicles, persons and facilities); temporal properties, for objects existing in a certain period (i.e., event); spatio-temporal properties, which are objects with a specific position in both space and time.

Besides time, space and object properties, Yixian Zheng et al. [25] identify six types of data that can be harvested and represent the type of entities that can be observed within urban territories according to the urban context they refer to human mobility, social network, geographical, environmental, health care and divers.

Human mobility data enables the study of social and community dynamics based on different data sources like traffic, commuting media, mobile devices and geotagged social media data.

Traffic data produced by sensors installed in vehicles or specific spots around the city (e.g., loop sensors, cameras). These data can include vehicles' positions observed recurrently at given intervals. Using these points (positions), it is then possible to compute trajectories which are spatiotemporally time-stamped and can be associated with instant speed and heading directions. Traffic occupation inroads can be measured with loops that compute within given time intervals, which vehicles travel across two consecutive loops.

Using this information, it is possible to compute travel speed and traffic volume on roads.

Ground truth traffic conditions are observed using surveillance cameras, that generate a huge volume of images and videos. Extracting information such as traffic volume and flowrate from these images and videos is still challenging. Therefore, in general, these data only provide a way to monitor citywide traffic conditions manually.

People's regular movement data are produced by personalized RFID transportation cards for buses or metro that they tap in station entries to enter/exit the public transportation system. This generates a huge amount of records of passenger trips where each record includes an anonymous card ID, tap-in/out stops, time, fares for this trip and transportation type (i.e. bus or metro). Commuting data recording people's regular movement in cities can be used to improve public transportation and to analyse citywide human mobility patterns.

Records of exchanges like phone calls, messages, internet, between mobile phones and cell stations collected by telecom operators are data that contain communication information, people's locations based on cell stations. These data offer unprecedented information to study human mobility. Social Networks Data. Social networks posts (e.g. blogs, tweets) are tagged with geoinformation that can help to better understand people's activities, the relations among people and the social structure of specific communities. User-generated texts, photos and videos, contain rich information about people's interests and characteristics, that can be studied from a social perspective. For example, evolving public attention on topics and spreading of anomalous information. The major challenges with geo-tagged social network data lie in their sparsity and uncertainty.

Finally, data refer to points of interest (POI) to depict information of facilities, such as restaurants, shopping malls, parks, airports, schools and hospitals in urban spaces.

Each facility is usually described by a name, address, category and a set of geographical coordinates.

Environmental data. Modern urbanization based on technology has led to environmental problems related to energy consumption and pollution. Data can be produced by monitoring systems observing environment through different variables and observations (e.g. temperature, humidity, sunshine duration and weather conditions), air pollution data, water quality data and satellite remote sensing data, electricity and energy consumption, CO2 footprints, gas. These data can help to have insight on consumption patterns, on correlations among actions and implications and foresight about the environment.

Divers data. Other data are complementary to urban data, particularly those concerning social and human aspects, such as health care, public utility service, economy, education, manufacturing and sports.

Data Harvesting Techniques

Data acquisition techniques can unobtrusively and continually collect data on a citywide scale. Data harvesting is a nontrivial problem given the three aspects to consider: (i) energy consumption and privacy, (ii) loose-controlled and nonuniform distributed sensors, (iii) unstructured, implicit, and noise data.

Crowdsensing. The term "crowdsourcing" is defined as the practice of obtaining needed services or content by soliciting contributions from a large group of people. People play the role of urban data consumers, but also participate in the data analysis process through crowdsourcing. Techniques use explicit and implicit crowdsourcing for collecting data that contain information about the way people evolve in public and private places. These data collections can be used as input for learning crowd behaviour and simulating it more accurately and realistically.

The advances of location-acquisition technologies like GPS and Wi-Fi has enabled people to record their location history with a sequence of time-stamped locations, called trajectories. Regarding non-obstructive data harvesting, work has been carried out using cellular networks for user tracking, profiting from call delivery that uses transitions between wireless cells. Geolife 1 is a social networking service which aims to understand trajectories, locations and users, and mine the correlation between users and locations in terms of user-generated GPS trajectories. In [17] a new vision has been proposed regarding the smart cities' movement, under the hypothesis that there is the need to study how people psychologically perceive the urban environment, and to capture that quantitatively. Happy Maps uses crowdsourcing and geo-tagged pictures and the associated metadata to build alternative cartography of a city weighted for human emotions. People are more likely to take pictures of historical buildings, distinctive spots and pleasant streets instead of car-infested main roads. On top of that, Happy Maps adopts a routing algorithm that suggests a path between two locations that is the shortest route that maximizes the emotional gain.

Discussion and Synthesis

An important aspect to consider is that data is non-uniformly distributed in geographical and temporal spaces, and it is not always harvested homogeneously according to the technique and also the conditions of the observed entities in an urban territory.

1 https://www.geospatialworld.net/article/geo-life-health-smart-city-gis/ Having the entire dataset may be always infeasible in an urban computing system. Some information is transferrable from the partial data to the entire dataset, for example, the travel speed of taxis on roads can be transferred to other vehicles that are also travelling on the same road segment. Some information cannot be transferable, for example, the traffic volume of taxis on a road may be different from private vehicles In some locations, when crowdsensing is used, more data can be harvested as required and in other places fewer data than required. In the first case, a down-sampling method, e.g., compressive sensing, could be useful to reduce a system's communication loads. In the last case, in the context of crowdsensing, some incentives can motivate users to contribute with data should be considered. How to configure the incentive for different locations and periods to maximize the quality of the received data (e.g., the coverage or accuracy) for a specific application is yet to explore. Three types of strategies can be adopted for harvesting data. (i) Traditional sensing and measurement that implies installing sensors dedicated to some applications. (ii) Passive crowdsensing using wireless cellular networks built for mobile communication between individuals to sense city dynamics (e.g., predict traffic conditions and improve urban planning). We described how this technique can be specialised into three strategies:

• Sensing City Dynamics with GPS-Equipped Vehicles: mobile sensors continually probing the traffic flow on road surfaces processed by infrastructures that produce data representing city-wide human mobility patterns.

• Ticketing Systems of Public Transportation (e.g., model the city-wide human mobility using transaction records of RFID-based cards swiping).

• Wireless Communication Systems (e.g., call detailed records CDR).

• Social Networking Services (e.g., geotagged posts/photos, posts on natural disasters analysed for detecting anomalous events and mobility patterns in the city) (iii) Participatory sensing where people obtain information around them and contribute to formulating collective knowledge to solve a problem (i.e., human as a sensor):

• Human crowdsensing: users willingly sense information gathered from sensors embedded in their own devices (e.g., GPS data from a user's mobile phone used to estimate real-time bus arrivals).

• Human crowdsourcing: users are proactively engaged in the act of generating data: reports on accidents, police traps, or any other road hazard (e.g. Waze), citizens turning into cartographers, to create open maps of their cities.

Managing and Indexing Urban Data

The objective of managing and indexing urban data is to harness a variety of heterogeneous data to quickly answer users' instant queries, e.g. predicting traffic conditions and forecasting air pollution. Three problems are addressed in this context: stream and trajectory data management, graph data management and hybrid indexing structures.

Stream and trajectory data management

Urban data often collected recurrently or even continuously (velocity) can lead to huge volumes of data collections that should be archived, organized (indexed) and maintained on persistence supports with efficient associated read and write mechanisms. Indexing and compression techniques are often applied to deal with data velocity and volume properties.

The continuous movement of an object is recorded in an approximate form as discrete samples of location points. A high sampling rate of location points generates accurate trajectories but will result in a massive amount of data leading to enormous overhead in data storage, communications, and processing. Thus, it is necessary to design data reduction techniques that compress the size of a trajectory while maintaining the utility of the trajectory. There are two major types of data reduction techniques running in batch after the data is collected (e.g., Douglas-Peucker algorithm [7]) or in an online mode as the data is being collected (such as the sliding window algorithm [12,16]).

Trajectory reduction techniques are evaluated concerning three metrics: processing time, compression rate, and error measure (i.e., the deviation of an approximate trajectory from its original presentation). Recent research [18], has proposed solutions to the trajectory reduction through a hybrid spatial compression algorithm and error-bounded temporal compression algorithm. Chen et al. [5] propose to simplify a trajectory by considering both the shape skeleton and the semantic meanings of the trajectory [31,32]. For example, when exploring a trajectory (e.g., travel route) shared by a user, the places where she stayed, took photos, changed moving directions significantly would be more significant than other points. Consequently, points with an important semantic meaning should be given a higher weight when choosing representative points for a simplified trajectory.

Graph Data Management

Graphs are used to represent urban data, such as road networks, subway systems, social networks, and sensor networks. Graphs usually associated with a spatial property, resulting in many spatial graphs [36]. For example, the node of a road network has a spatial coordinate and each edge denoting a road segment has a spatial length. Graphs also contain temporal information, for instance, the traffic volume traversing a road segment changes over time, and the travel time between two landmarks is timedependent: spatio-temporal graphs [36]. Queries like "find the top-k tourist attractions around a user that are most popular in the past three months", can be asked on top of graphs.

Hybrid Indexing Structures are intended to well organize different data sources. For example, combining POIs, road networks, traffic, and human mobility data simultaneously. Hybrid structures can be used for indexing special regions. For instance, a city partitioned into grids by using a quad-tree-based spatial index (see Figure 2

Querying Urban Data

Querying the actual location of a moving object has been studied extensively in moving object databases using 3DR-Tree [19] and MR-Tree [28]. Yet, sometimes queries must explore historical trajectories satisfying certain criteria, for example, retrieving the trajectories of tourists passing a given region and within a period. This corresponds to a spatiotemporal range query [23,24]), for example, taxi trajectories that pass a crossroad (i.e., a point query), or the trajectories that are similar to a query trajectory [6,20] (i.e., a trajectory query).

Dealing with the uncertainty of a trajectory refers to positioning moving objects while their locations can only be updated at discrete times. The location of a moving object between two updates uncertain because the time interval between two updates can exceed several minutes or hours. This can, however, save energy consumption and communication bandwidth.

Map matching is to infer the path that a moving object like a vehicle has traversed on a road network based on the sampled trajectory. Map-matching techniques dealing with high-sampling-rate trajectories have already been commercialized in personal navigation devices, while those for low-sampling-rate trajectories [15] are still considered challenging. According to Yuan et al. [30], given a trajectory with a sampling rate around 2 minutes per point, the highest accuracy of a map-matching algorithm is about 70%.

When the time interval between consecutive sampling points becomes even longer existing map-matching algorithms do not work very well any longer [36]. Wei et al. [26] proposed to construct the most likely route passing a few sampled points based on many uncertain trajectories. Krumm et al. and Xue et al. [13,29] propose solutions to predict a user's destination based on partial trajectories. More generally, a user's and other people's historical trajectories as well as other information, such as the land use of a location, can be used in destination prediction models.

Other important problems include observing a certain number of moving objects travelling a common sequence of locations in similar travel time where the locations in a travel sequence are not consecutive for finding sequential patterns from trajectories.

Other approaches discover a group of objects that move together for a certain time period, under different patterns such as flock [8,9], convoy [10,11], swarm [14], traveling companion [21,22], and gathering [34,35,36,25]. These "group patterns", can be distinguished based on how the "group" is defined and whether they require the periods to be consecutive. For example, a flock is a group of objects that travel together within a disc of some user-specified size for at least k consecutive timestamps [10]. Li et al. [14] relaxed strict requirements on consecutive periods and proposed the pattern swarm, which is a cluster of objects lasting for at least k (possibly non-consecutive) timestamps.

Data and Knowledge Fusion

In urban computing scenarios, it is necessary to exploit a variety of heterogeneous data sources that need to be integrated. Then, it is necessary to fusion knowledge to explore and exploit datasets to extract insight and foresight of urban patterns and phenomena.

Data fusion. There are three major ways to achieve this goal:

• Fuse data sources at a feature level putting together the features extracted from different data sources into one feature vector. Beforehand and given the heterogeneity of data sources, a certain kind of normalization technique should be applied to this feature vector before feeding it into a data analytics model.

• Use different data at different stages. For instance, first partition an urban region, for example, a city into disjoint regions by major roads and then use human mobility data to glean the problematic configuration of a city's transportation network [33].

• Feed different datasets into different parts of a model simultaneously given a deep understanding of the data sources and algorithms applied to analyse them.

Building high-quality training datasets is one of the most difficult challenges of machine learning solutions in the real world. Disciplines like data mining, artificial intelligence and deep learning have contributed to building accurate models but, to do so, they require vastly larger volumes of training data. The traditional process for building a training dataset involves three tasks: data collection, data labelling and feature engineering. From the complexity standpoint, data collection is fundamentally trivial as most organizations understand what data sources they have. Feature engineering is getting to the point that is 70%-80% automated using algorithms. The real effort is in the data labelling stage.

New solutions are emerging for combining strong and weak supervision methods to address data labelling.

Knowledge fusion. Data mining and machine-learning models dealing with a single data source have been well explored. However, the methodology that can learn mutually reinforced knowledge from multiple data sources is still missing. The fusion of knowledge does not mean simply putting together a collection of features extracted from different sources but also requires a deep understanding of each data source and effective usage of different data sources in different parts of a computing framework.

End-to-end urban computing scenarios call for the integration of algorithms of different domains. For instance, data management techniques with machine-learning algorithms must be combined to provide both efficient and effective knowledge discovery ability. Similarly, integrating spatio-temporal data management algorithms with optimization methods. Visualization techniques should be involved in a knowledge discovery process, working with machine-learning and data-mining algorithms.

Perspectives of the Role of Data Science for Making Urban Spaces Smart

This chapter discussed and described issues regarding data for enabling urban computing tasks that can lead to the design of smart urban territories. Having a data centred analysis of the problems and challenges introduced by urban computing exhibits the requirement to study data concerning different perspectives. First, the chapter characterised data produced within urban territories in terms of their mathematical properties (spatiotemporal), concerning the "semantics" of the entities composing urban territories (e.g., points of interest, roads, infrastructure) and also from the mobile entities that populate urban territories, like people, vehicles and the built environment. This variety of data are produced by producers with different characteristics, and approaches today use hardware, software and passive and active participation of people to generate phenomenological observations of urban territories. Finally, the chapter discusses how to create insight and foresight of important situations happening in urban territories, for example, computing trajectories of entities evolving in these territories observed in space and time; other social foresight of behaviours like popular POIs, the population of regions, etc.

The vision of urban computing-acquisition, integration, and analysis of big data to improve urban systems and life quality-is leading to smarter cities. Urban computing blurs the boundary between databases, machine learning, and visualization and even bridges the gap between different disciplines (e.g., computer sciences and civil engineering). To revolutionize urban sciences and progress, quite a few techniques still need to be explored such as the hybrid indexing structure for multimode data, the knowledge fusion across heterogeneous data sources, exploratory visualization for urban data, the integration of algorithms of different domains, and intervention-based analysis. [ 24 ] F. Wang, W. Chen, F. Wu, Y. Zhao, H. Hong, T. Gu, L. Wang, R. Liang, and H. Bao.

Bibliography

A visual reasoning approach for data-driven transport assessment on urban roads. In

Figure 1

 1 Figure1summarizes the urban data types considered in urban computing:

Figure 1

 1 Figure 1 Urban Data TypesUrban data can be harvested from different sources and using different techniques. These

  ) where each leaf node (grid) of the spatial index maintains two lists storing the POIs and road segments. Then, each road segment ID points to two sorted lists: a list of taxi IDs sorted by their arrival time 𝑡𝑎 at the road segment; a list of drop-off and pick-up points of passengers sorted by the pick-up time (𝑡𝑝) and drop-off time (𝑡𝑑).

Figure 2

 2 Figure 2 Hybrid index for organizing urban data [36] Different kinds of index structures have been proposed to manage different types of data

  scheme for large-scale GPS track retrieval. In Proceedings of the 9th International Conference on Mobile Data Management.IEEE, 2008, 1-8.

Visual

  Analytics Science and Technology (VAST), 2014 IEEE Conference on, pages 103-112. IEEE, 2014 [ 25 ] Z.Wang, M.Lu, X.Yuan, J.Zhang,andH.v.d.Wetering.Visual traffic jam analysis based on trajectory data. Visualization and Computer Graphics, IEEE Transactions on, 19(12):2159-2168, 2013 [ 26 ] L. Y. Wei, Y. Zheng, and W. C. Peng. Constructing popular routes from uncertain trajectories. In Proceedings of the 18th SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2012, 195-203 [ 27 ] Y.Wu, S.Liu, K.Yan, M.Liu and F.Wu. Opinionflow: Visual analysis of opinion diffusion on social media. Visualization and Computer Graphics, IEEE Transactions on, 20(12):1763-1772, 2014 [ 28 ] X. Xu, J. Han, and W. Lu. RT-tree: An improved R-tree index structure for spatiotemporal databases. In Proceedings of the 4th International Symposium on Spatial Data Handling. 1999, 1040-1049 [ 29 ] A. Y. Xue, R. Zhang, Y. Zheng, X. Xie, J. Huang, and Z. Xu. 2013. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In Proceedings of the 29th IEEE International Conference on Data Engineering. IEEE, 254-265 [ 30 ] J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-Drive: Driving directions based on taxi trajectories. In Proceedings of ACM SIGSPATIAL Conference on Advances in Geographical Information Systems. 2010, ACM, 99-108 [ 31 ] Y. Zheng, X. Xie, and W. Y. Ma. Search your life over maps. In Proceedings of the International Workshop on Mobile Information Retrieval. 2008, 24-27 [ 32 ] Y. Zheng and X. Xie. GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin 33, 2, 2010, 32-40 [ 33 ] Y. Zheng, Y. Liu, J. Yuan, and X. Xie. Urban computing with taxicabs. In Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, 2011, 89-98 [ 34 ] Y. Zheng, F. Liu, and H. P. Hsieh. U-Air: When urban air quality inference meets big data. In Proceedings of 19th SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2013, 1436-1444 [ 35 ] K. Zheng, Y. Zheng, N. J. Yuan, S. Shang, and X. Zhou. Online Discovery of Gathering Patterns over Trajectories. IEEE Transactions on Knowledge Discovery and Engineering, 2014 [ 36 ] Zheng, Yu, et al. "Urban computing: concepts, methodologies, and applications." ACM Transactions on Intelligent Systems and Technology (TIST) 5.3 (2014): 38


Liehui Wang

Nanyi Zhang

Fei Ye

Yui-Yip Lau

César Ducruet

The complex network analysis of liner shipping networks: Lessons from the merger between COSCO and CSCL

Keywords: corporate merger, shipping network patterns, complex networks, COSCO Shipping Lines

are leading enterprises in China's shipping industry. They merged and reorganized as COSCO Shipping Lines in 2016. Through using a complex network methodology, we analyze the spatial patterns of their shipping networks before and after the merger. We evaluate the integration effects based on two main dimensions: network and hub economies. While complementarities are found between COSCO and CSCL networks before the merger, COSCO Shipping Lines increased the total number of service nodes and shipping routes significantly. The merger also had the effect of strengthening the hub capacity of 7 of the top 15 hub ports in the main markets covered by the new company. Results underline that this strategy of overseas pivotal ports promoted the development of hub economy and regional market expansion.

Introduction

In the liner shipping business, the performances of shipping firms rely on their conduct of taking such decisions as capacity and pricing level. From the mid-1990s onwards, a tendency towards mergers and acquisitions has spread quickly across shipping firms [START_REF] Yip | Scale diseconomies and efficiencies of liner shipping[END_REF]. After the global financial crisis in 2008, the shipping market was generally oversupplied. Against the backdrop of performance losses and financial constraints faced by most shipping companies in 2016, the industry underwent a consolidation and reorganization. The ultimate objective was to solidify their competitive position against competitors [START_REF] Yip | Scale diseconomies and efficiencies of liner shipping[END_REF]. Typical examples of such mergers and reorganizations include the French company CMA-CGM merged among Neptune Orient Lines, the Norwegian Solstad Offshore, and REM Offshore; NYK (Nippon Yusen Kabushiki Kaisha) and MOL (Mitsui O.S.K. Lines) integrated with Kawasaki steam ship and its consignment business, STX marine shipbuilding's self-rescue reorganization plan; and Hapan-Lloyd, integrated with UASC (United Arab Shipping Company) and Maersk Line consolidated with Hamburg Süd Group.

Following this trend, China merged and reorganized its major shipping giants, namely COSCO and CSCL, renamed as COSCO Shipping Lines. COSCO and CSCL were China's largest shipping companies in 2015 while ranked sixth and seventh in the world, respectively. From the 1 st March, 2016, COSCO Shipping Lines entered a transition process through the integration of shipping routes and the leasing of container ships formerly operated by CSCL. It also began to integrate the assets within the CSCL networks gradually according to its restructuring plan. COSCO Shipping Lines had formed a new shipping network structure by December 2016.

Recently, merger and reorganization activity among shipping enterprises has surged. Some research studies (e.g., [START_REF] Drahokoupil | Chinese investment in Europe: corporate strategies and labour relations[END_REF][START_REF] Yeong | An analysis of the competitiveness of major liner shipping companies[END_REF][START_REF] Onwuegbuchunam | Structural changes in the global transport chain: implications for ports[END_REF] have put forward different views on the merger of COSCO and CSCL. But, there have been few studies to review and analyze the development and formation of shipping networks [START_REF] Lau | An empirical study of shipping networks[END_REF]. Some scholars have begun to study the impact of shipping alliances and restructuring activities on shipping structures. However, this has been limited to the qualitative explanation of the changes in global shipping capacity patterns in the context of corporate restructuring. Scholars have also explored the development of specific shipping companies in the context of mergers. The focus has been on the use of long-term stock and financial data to examine the economic benefits, whereas, changes in market capacity patterns have only been examined in brief overviews of the restructuring of the enterprise. The merger and acquisition dynamics in the shipping sector are classic topics, but lost ground from the 1960s to the 2010s due to the emergence of complementary approaches [START_REF] Lau | An empirical study of shipping networks[END_REF]). Thus, even in the case studies of the globalization of the shipping networks of a single shipping enterprise, there is still a lack of a specific characterization and quantitative analysis of the key changes in shipping networks structure. Furthermore, the spatial patterns of Chinese companies' shipping networks have been under-researched.

This study employs a complex network approach to analyze the effects of company integration on the spatial patterns of shipping networks in the case of China's shipping industry. In doing so, we investigate port connectivity and explore the development of hub ports in affected regions. In addition, we discuss the underlying operational factors used by actors to design and develop their shipping networks, in terms of firm performance, capacity, network configuration, transit time, geographical coverage, and transportation time (see [START_REF] Jiang | Port connectivity study: an analysis framework form a global container liner shipping network perspective[END_REF][START_REF] Tu | Shipping network design in a growth market: the case of Indonesia[END_REF] The rest of the paper is organized as follows. Section 2 includes a literature review. Section 3 presents our data and methodology. Section 4 provides a comprehensive analysis results about the shipping network structures of COSCSO and CSCL in 2015 and COSCO shipping lines in 2016. After Section 4, discussion and conclusion delivers in Section 5.

Literature review

Industry changes

There are three main approaches in which shipping companies can expand their markets, namely: alliances, mergers and acquisitions (M&A) and internal growth. The research on mergers and reorganizations of shipping companies and their alliance strategy has mainly focused on the motivation and impact factors (Rodriguez-Pose and Zadermach, 2003;[START_REF] Frémont | Empirical evidence for integration and disintegration of maritime shipping: port and logistics activities[END_REF][START_REF] Fusilo | Structural factors underlying mergers and acquisitions in liner shipping[END_REF][START_REF] Das | To partner or to acquire? A longitudinal study of alliances in the shipping industry[END_REF][START_REF] Andreou | Valuation effects of mergers and acquisitions in freight transportation[END_REF][START_REF] Wang | The evolution mechanism of container port network[END_REF][START_REF] Choi | Evaluation of M&A effects in Japanese shipping companies: case study of NYK & Showa line and OSK & Navix line[END_REF][START_REF] Alexandrou | Mergers and acquisitions in shipping[END_REF]. [START_REF] Frémont | Empirical evidence for integration and disintegration of maritime shipping: port and logistics activities[END_REF] explored the actual development of vertical integration of shipping companies and found that, in practice, shipping companies participated in intermodal transport in order to develop their core container shipping business. Based on the study of the motivation and purpose of M&A in shipping companies, [START_REF] Fusilo | Structural factors underlying mergers and acquisitions in liner shipping[END_REF] quantitatively verified the determining factors (i.e., technical, regulatory environment, and industry demand) of US shipping companies' M&A based on the neoclassical model. Rodriguez-Pose and Zademach (2003) adopted econometric models to examine the impact of geographic dimensions on corporate M&A activity. [START_REF] Das | To partner or to acquire? A longitudinal study of alliances in the shipping industry[END_REF] concluded that alliances can expand market coverage while M&A can achieve a higher synergistic economy. [START_REF] Andreou | Valuation effects of mergers and acquisitions in freight transportation[END_REF] suggested that companies pursue economies of scale while striving towards economies of scope.

Utility analysts have paid more attention to the economic benefits of M&A after the reorganization of the enterprises. [START_REF] Alexandrou | Mergers and acquisitions in shipping[END_REF] systematically explored the benefits of M&A on the basis of the valuation benefits of the financial indicators of shipping companies involved in them. [START_REF] Choi | Evaluation of M&A effects in Japanese shipping companies: case study of NYK & Showa line and OSK & Navix line[END_REF] examined through the M&A process of Japanese shipping companies and evaluated the effects of M&A through changes in a series of financial data. The merger of corporate induces a change in the market capacity pattern and an increase in the profitability of enterprises by improving asset utilization. The achievement of synergy is the main driving force for M&A and the evaluation criteria for the success of enterprise integration.

Shipping networks from an industry perspective

Research on shipping networks has focused on global, regional, and national scales and identified key factors determining the allocation of liner services under regional differences. Geographers in particular have been particularly keen in studying the globalization of liner shipping networks from an industry perspective. [START_REF] Slack | Fifty years of organisational change in container shipping: regional shift and the role of family firms[END_REF] studied the container shipping development focusing on the evolution of major shipping companies in the past 50 years. [START_REF] Frémont | Empirical evidence for integration and disintegration of maritime shipping: port and logistics activities[END_REF] analyzed the differences and characteristics of horizontal and vertical integration among types of shipping enterprise integration. Also, the study explored the main process of vertical integration by using a large scale of survey and in-depth interviews with various employees in shipping industry.

In addition, [START_REF] Frémont | Global maritime networks: the case of Maersk[END_REF] selected a single shipping company as a research target to explore the evolution of the enterprise shipping networks pattern, acquisition, route planning, and new liner operational mode. He particularly underlined how the hub port strategy played an important role in the formation of the shipping companies, with a focus on Maersk Line and CMA-CGM's global networks structure (see also [START_REF] Frémont | A geo-history of maritime networks since 1945. The case of the Compagnie Générale Transatlantique's transformation into CMA-CGM[END_REF]. In addition, [START_REF] Parola | The spatial coverage of shipping lines and container terminal operators[END_REF] sorted through the global landscape of major liner companies and terminal operators. Overall, these studies on shipping companies inclined towards the development of shipping companies by using simple statistical analysis or the evolution of global markets for shipping companies through the case study approach like Maersk Line and CMA-CGM.

Other geographers discussed changes in the port networks of specific companies such as COSCON and Evergreen in a comparative perspective along the Europe-Asia route [START_REF] Comtois | Géopolitique et transports: nouvelles perspectives stratégiques dans le détroit de Taiwan[END_REF] and in relation to China [START_REF] Rimmer | China's extra-and intra-Asian liner shipping connections, 1990-2000[END_REF]. Interestingly, only one study looked at the effect of alliances on shipping network structure, through a comparison between the Caribbean and Mediterranean basins [START_REF] Mccalla | The geographical hierarchy of container shipping networks in the Caribbean basin and Mediterranean sea[END_REF], concluding that the Caribbean network is more vulnerable due to its higher reliance upon such alliances. Last but not least, [START_REF] Metge | L'Afrique dans la logistique mondiale: une approche par les réseaux d'armateurs de lignes maritimes conteneurisées[END_REF] analysed the port networks of major shipping lines across the whole African continent using cartography and correlation analysis, pointing to overlaps and differences in terms of main hub ports and outlying firms.

Graph theory and complex networks

Increasing computational power as well as newly available data (e.g. vessel movements, shipping schedules) allowed for more quantitative analyses of shipping networks and to shift from case studies at the local level to global-level studies mixing all major companies. Tools from graph theory, a branch of mathematics, and from complex networks, a branch of physics and computer science, served to measure the centrality of port nodes and the structure of such composite networks. In doing so, research on the spatial patterns of shipping networks and global shipping networks in specific regions has become an emerging trend. [START_REF] Wang | International reorganization of shipping liners and its effect on world network structure[END_REF], [START_REF] Kaluza | The complex network of global cargo ship movements[END_REF], Ducruet and[START_REF] Ducruet | The worldwide maritime network of container shipping: spatial structure and regional dynamics[END_REF][START_REF] Xu | Evolution of regional inequality in the global shipping network[END_REF] described the pattern of global shipping networks through a number of indicators such as degree of distribution, clustering coefficient, primacy ratio, and other indicators of centrality. [START_REF] Ducruet | The worldwide maritime network of container shipping: spatial structure and regional dynamics[END_REF] also pointed out the fact that East Asia is developing rapidly. Wilmsmeier and Notteboom (2011) compared the regional differentiation of liner shipping networks in South America and North Europe. The research study further discussed the liner shipping allocation factors from the different perspectives of port environment, contact with the hinterland, market operator strategy, and government policy.

Based on complex network theory, Chen and Hu (2016) analyzed the complex network characteristics of the Southeast Asia shipping networks along Maritime Silk Road. They highlighted that Singapore, Port Klang, and Tanjung Pelepas were all significant in the regional network (see also [START_REF] Wang | From hierarchy to networking: The evolution of the '21st century Maritime Silk Road' container shipping system[END_REF]. Based on the schedule of ships, [START_REF] Wang | Spatial structure of container port systems across the Taiwan Straits under the direct shipping policy: a complex network system approach[END_REF] selected complex network indicators to describe the spatial structural change of the container port system on both sides of the Straits. According to the complex network method, [START_REF] Du | Spatial pattern analysis of China container shipping network on complex theory[END_REF] studied the spatial pattern of China's container shipping networks through a comprehensive analysis of overall network indicator degree, degree of distribution, and port centrality. [START_REF] Ducruet | China's global shipping connectivity: internal and external dynamics in the contemporary era (1890-2016)[END_REF] proposed an analysis of China's overseas connectivity based on various methods including single linkage analysis, over the periods 1890-2008 for all ships and 1977-2016 for containerships, shedding new light on the external hub ports concentrating flows such as Hong Kong, Busan, Kaohsiung, and Yokohama.

Based on shipping company schedules, this study combines complex network indicators, VOSviewer and Arcgis visual analysis to describe the spatial patterns of shipping networks accurately and explore the shipping routes of COSCO Shipping Lines, which reached the fourth rank in the world after the reorganization of the two former companies.

Data and methodology

Background and data

Shipping routes are a very important resource for a shipping company as they affect income and cost as well as business model feasibility. The shipping network's space pattern directly reflects the service scope and market coverage of the enterprise and is the intuitive response of the enterprise in pursuing economies of scope and scale. Based on the volume of shipping and the length of routes, global container shipping networks encompass two levels of organization: main routes and regional feeder routes. Main routes generally refer to East-West trunk lines of the northern hemisphere connecting Far East, North America, and Europe with each other, while regional feeder networks are mainly composed of North-South services connecting the northern hemisphere with Africa, South America, Australia, but also South-South routes [START_REF] Wang | The evolution mechanism of container port network[END_REF]. Accordingly, we define the main shipping markets of COSCO and CSCL as: East Asia, Southeast Asia, South Asia, the Middle East, the Mediterranean, Northwest Europe, North America East Coast, and North America West Coast as well as the secondary markets as: Australia, New Zealand, East Africa, West Africa, South America East Coast, and South America West Coast.

We collected the shipping schedules from COSCO and CSCL in October 2015 as well as COSCO Shipping Lines in December 2016. In 2015, statistics show that CSCL had 151 ports and 691 routes and COSCO had 142 ports and 670 routes. After the merger in 2016, COSCO Shipping Lines had 200 ports and 1,316 routes. We merged the data for terminals at the same port and then used the port as the node in the network. The edge of the network between the two nodes is established if there is a route between the two nodes. The number of routes is the weight of the edge. By using Gephi 8.0.2 software, we constructed the directed and weighted networks of the two shipping companies in 2015 and COSCO Shipping Lines in 2016. The shipping network was created based on ports of call and the moving tracks of container ships. The moving tracks of container ships generated the links of port-to-port according to the successive ports of call (namely from port A to port B and from port B to port C). With the utilization of quantitative analysis, we explored the spatial structures of the shipping networks of the two companies from the perspective of the transportation network economy.

Methodology

Based on the transportation characteristics and the scale-free characteristics of complex networks, a few hub ports play leading roles in the overall network in the context of economies of scale and scope [START_REF] Rong | On the economies of scale and scope in transport network[END_REF]. Under complex network methodology, economies of size and capacity are the key elements of studying shipping networks. This study mainly concentrates on the changes in the ports and the routes connections of shipping networks. These changes represent the market coverage of the shipping networks and capture the size of the shipping networks. The economy of capacity identifies the phenomenon where the average cost gradually declines when the port increases its capacity in the networks to handle and transfer passengers and cargo, to compile trains, to load vehicles, to take off and land, and to dock ships. The two factors considered in this paper are the number of ports connected to a certain port and the frequency of routes passing through the port. The route frequency refers to the number of ships passing through the shipping route over a particular period. We can explore the changes in hub capacity during the port route integration process through the port strength variation index Ki.

Where:

𝐾 𝑖 =𝐾 𝑖1 +𝐾 𝑖2 ,𝐾 𝑖1 = (2016𝐷 𝑖 -2015𝐷 𝑖 max)/2015𝐷 𝑖 min, 𝐾 𝑖2 = (2016𝑆 𝑖 -2015𝑆 𝑖 max)/2015𝑆 𝑖 min.
(1) When the port only has one company calling in 2015:

𝐾 𝑖 =2016𝐷 𝑖 /2015𝐷 𝑖 +2016𝑆 𝑖 /2015𝑆 𝑖 .;
Where: 𝐷 𝑖 of node 𝑖 is defined as the number of other nodes connected to that node. 𝑆 𝑖 is the sum of the number of edges that connect node 𝑖 to all other nodes in the network. The calculation step is shown in Equation 2.

As can be seen in Table 1, based on the size of the index 𝐾 𝑖 , the port route changes are divided into five main categories: reduction, high intensive growth, low intensive growth, fully integrated growth, and expansion growth. The port change index is the amount of change in port connectivity in the shipping network. The categories of port change index reflect whether the port connectivity has increased and the levels of growth after the change of shipping network. Based on the positive and negative level of change, the connectivity of the port is classified as the reduction and growth. Taking the multiple of increments as the classification criterion, the growth of the port's connectivity is classified as an intensive, a fully integrated, or an expansion. 0<𝐾 𝑖 ≤1 High intensive growth In 2016, the port route was more than the largest anchor strength of the two companies in 2015 and the integration of the two companies was high.

1<𝐾 𝑖 <2 Low intensive growth In 2016, the port route was more than the largest anchor strength of the two companies in 2015and the integration of the two companies was low.

𝐾 𝑖 =2 Fully integrated growth The strength of the port route in 2016 was equal to the overall level of full integration of the two companies' routes in 2015.

𝐾 𝑖 >2 Expansion growth

In 2016, the port route intensity was stronger than the overall level of full integration of the two companies' routes in 2015.

Complex network indicators include degree, weighted degree, and betweenness centrality. The Degree is an important indicator to describe complex network nodes. The degree Di of node I is defined as the number of other nodes connected to that node. The greater the degree of the node, the closer the node is to the other nodes in the network and the more likely it is to act as a hub towards its immediate neighbors. In container shipping networks, the degree of the port is captured by the number of connecting ports. It indicates the contact range of the port. In the regional container networks, there is often more than one route connecting two ports. Additionally, the connection between ports has a weighted relationship among the number of routes. The node degree value can represent the significance of the nodes in the unprivileged network, reflecting the close relationship between a port and the rest of the ports within a port system. Importantly, the node weighted degree more accurately expresses the strength of the node connection in the weighted network to consider the frequency of the port-to-port relationship. Therefore, in this study, the weighted degree of the port definition is [START_REF] Fusilo | Structural factors underlying mergers and acquisitions in liner shipping[END_REF]:

𝑆𝑖 = ∑ 𝑊𝑖𝑗 𝑗∈𝑁 (2)
Wij represents the weight of the connection between node I and other nodes j in the network. The strength of the node association Si is also named in the weighted degree in Gephi software. It indicates the sum of the number of edges that connect node I to all other nodes in the network.

Betweenness centrality mainly highlights the influence of the nodes in the whole network. It is an important parameter of network centrality and is used to indicate the strength of indirect connections between nodes. In general, hubs are nodes with a large betweenness centrality. The betweenness centrality of node I refer to the proportion of the number of nodes i in the shortest paths in the network. The number of nodes I is mathematically expressed as follows [START_REF] Fusilo | Structural factors underlying mergers and acquisitions in liner shipping[END_REF]:

𝐶 𝐵𝑖 = ∑ ∑ 𝛿 𝑙 𝑖 𝑙∈𝑆 𝑘𝑗 |𝑆 𝑘𝑗 | 𝑘,𝑗 (3)
Where in 𝑆 𝑘𝑖 ,k, i is a combination of the shortest paths; ∑ 𝛿 𝑙 𝑖 𝑙∈𝑆 𝑘𝑗 is the sum of the shortest paths through node i. In shipping networks, the greater in the betweenness centrality of a port infers the stronger in the accessibility between that port and other ports, and the stronger its global hub function.

4 Main results: shipping network structure before and after the merger 4.1 Difference in network structure of COSCO and CSCL in 2015 Table 2 compares the regional coverage of COSCO and CSCL in terms of ports of call. The two shipping firms had a high degree of commonality in North West Europe and Australia where the proportion of the same ports owned by COSCO and CSCL were 0.91 and 0.78, respectively. For West Africa, East Asia, North America, and South Asia, the overlap is noticeable, with a proportion ranging between 0.6 and 0.67. Their differences are mainly demonstrated in the following. CSCL had the advantage of port coverage in the Mediterranean, the Middle East, South America East, and North America East. COSCO's network is more developed in Southeast Asia, while the proportion of the same ports owned ranged from 0.36 to 0.46. The difference in port selection was the largest in the West Coast of South America where the overlap was only 0.29. Notes: The share of calls on the same port is the ratio of the two shipping companies' port calls (i.e. the same port)

in the total number of the two shipping enterprises' ports.

The distribution between the two companies of the ports with important connections (weighted degree greater than 10) shows remarkable spatial differences. According to Figures 1 and2, the two shipping firms formed critical port connections in China's coastal areas and Southeast Asia. CSCL had relative advantages in the Mediterranean, the Middle East, and Northwest Europe. COSCO had relative advantages in North America and Australia. However, neither company formed an important port network in South America or Africa. The important port connections (weighted degree greater than 10) are mainly related to the east-west shipping markets such as East Asia-North America West Coast, East Asia-Southeast Asia, Southeast Asia-the Mediterranean, and the Mediterranean-South American East Coast. The CSCL network in China-Australia-Southeast Asia and North America West-North America East shows relatively frequent contact. In summary, the two shipping firms called the ports independently on the edges of the shipping networks. The shipping networks of the two shipping firms mainly focused on the main market. Although COSCO and CSCL showed commonalities in the main market, they still had different advantages in specific ports and areas. Moreover, the differences between the two shipping firms stood out clearly in the secondary markets from the north to the south.

Port increase and route expansion for COSCO Shipping Lines in 2016

We analyzed the economies of size of shipping networks based on the increase and decrease in the number of port calls. During the restructuring of the COSCO Shipping Lines' networks, the increasing number of port calls showed an expansion of size. But, an expansion in secondary markets was not obvious and new port connections remained low. As shown in Figure 3, the additional 39 ports of COSCO in 2016 mainly concentrated on main markets compared with 2015: North West Europe (10 ports), the Mediterranean (9 ports), North America (4 ports), Australia (3 ports), South America East (3 ports), South Asia (2 ports), and Southeast Asia (3 ports). One new port was added to East Asia, the Middle East, North America West, and West Africa respectively.

In addition, only one or two routes passed most of the ports. There were 22 ports where the numbers of ports of call were cancelled. It mainly occurred in North and South Asia, followed by two ports in Southeast Asia, the Mediterranean, the Middle East, and East Africa respectively. Also, one port was dropped in South America East, North America East, and South America West respectively. Among them, Pasir Gudang port in Malaysia, and Port of Sohar in Oman, Rio de Janeiro in South America were among the dropped significant ports. The changes in ports of call in the shipping networks are the basic reflection of market expansion. The expansion of shipping networks has shown that the integration of the two shipping firms achieved a considerable expansion. In 2016, COSCO Shipping Lines had the widest coverage in the Mediterranean and East Asian navigation areas, followed by Southeast Asia, North West Europe, and the Middle East. North America, Australia, New Zealand, and South America East coast networks also exhibit wide coverage. COSCO Shipping Lines maintained its dominant position in the main market while expanding its coverage towards several additional ports in secondary markets.

We analyzed the changes in the size of the shipping networks from the sum of the frequency of any two ports. In 2016, COSCO Shipping Lines significantly enlarged its route links and the overall network gained economy of size. The frequencies of COSCO's and CSCL's shipping routes in 2015 were 670 and 691, respectively. After integration, the COSCO Shipping Lines networks dramatically increased to 1316 in 2016. Based on our analysis, only the links of port-to-port were considered. In 2015, the number of ports by COSCO and CSCL was 142 and 151 respectively. In 2016, the number of the ports with COSCO Shipping Lines was 200. From the perspective of the direct connections of various shipping networks in 2016, the COSCO Shipping Lines route interval contact among Southeast Asia, Australia, New Zealand and other navigation zones did not change.

Surprisingly, the other navigation zones expanded their scopes. According to Figures 4 to 6, the expansion of Mediterranean connections was the most evident. New direct links were established with four regions including West Africa, South America, and South Asia. As a result, shipping networks attained a wider range of route connections. Closer connections were observed in the shipping patterns of COSCO Shipping Lines. In 2016, Southeast Asia-East Asia and East Asia-North America West were closely associated, followed by the Middle East-Southeast Asia, Southeast Asia-South Asia, the Middle East-the Mediterranean, and North-West Europe-the Mediterranean.

Port hub enhancement in 2016

The two factors of port connection degree and route frequency are considered. According to the size of the port strength change index Ki, the port integration changes were divided into five categories: reduction, high intensive growth, low intensive growth, fully integrated growth, and expansion growth. These portray changes in hub capacity are provided in the integration process 2015-2016.

In 2015, a total of 161 ports from COSCO's and CSCL's networks were included in the 2016 COSCO Shipping Lines' networks. After the integration of the routes, there were 70 ports with expansionary growth, 27 ports with fully integrated growth, 21 ports with low intensive growth, 24 ports with high intensive growth, and 19 ports with reduction. This means that only 12% of the ports showing the routes were under contraction. The ports with significantly enhanced hub capacity are shown in Figure 7. These are mainly distributed in Southeast Asia, the Mediterranean, Australia and New Zealand, the Middle East, West Africa, South America, and North America. Ports exhibiting weak hub capabilities were mainly located in North West Europe, the Mediterranean, North America, the Middle East, and the East Asian navigation zones. It should be noted that China's domestic network and its ports are excluded from the analysis. The ports in the Mediterranean and the Middle East changed significantly. Overall, main markets such as Southeast Asia, the Mediterranean, and North America significantly strengthened their hubs during the integration process while secondary markets like West Africa and South America East coast were also strengthened. After the shipping networks integration of the two shipping firms, the ports with important connections (i.e., weighted degree greater than 10) were mainly concentrated on the main east-west market. Australia and New Zealand showed important links, as well as South America and Africa, which initially formed ports with pivotal connections. According to Figure 8, the important ports were addressed in East Asia where it includes 16 important ports in China's coastal areas and in surrounding areas such as Kaohsiung and Busan. There are nine important ports in Southeast Asia, notably strategic transhipment ports such as Singapore and Klang. Nine ports are located in the Mediterranean of which Piraeus which is a key port in the region. Six ports are situated in North-West Europe. Rotterdam and Hamburg are considered as the centers of the region. In addition, there were 10 ports in North America, including 4 in the US East Coast and 6 in the US West Coast. Moreover, 4 ports in the Middle East consist of Jeddah Port and Port Said. In secondary markets, there were four ports with high port call frequency in Australia and New Zealand. Three of these ports were strategic ports in the original COSCO shipping network. Two are in South America (Santos and Callao) and one is in Africa (Lagos). But, the overall network level was low. The connections of the high-frequency zone were not only limited to the port connections of the main east-west navigation area and the ports in the north-south navigation area, but also connected with other regions like West Africa-Southeast Asia, Australia-Southeast Asia and Australia-New Zealand-East Asia. Besides, the linkage between Australia-New Zealand-North America West, South America West-the Mediterranean, and South America West and North America West ports were highly intensive. The scale-free characteristics of the shipping networks indicate that a small number of hub ports play a leading role in the entire shipping networks. In the transportation networks, the nodes with large betweenness centrality are generally recognized as hub nodes. We looked at the top 15 ports in terms of centrality in 2016 (see Table 3). There were 15 hub ports were changed including seven expansion growth, two with low intensive growth, five with high intensive growth, and one with reduction.

The calling strengths of hub ports with important network status, such as Colombo, Algeciras, Istanbul, Rotterdam, Klang, Piraeus, and Manzanillo (Mexico) increased significantly. This showed a growth trend in the COSCO Shipping Lines' networks. COSCO Shipping Lines used the 21 st Century Belt and Road Initiative (BRI) as an important opportunity to integrate routes around the globe. It has set up 255 international routes and 146 domestic routes. The vessels operated by COSCO have docked with 356 ports in about 105 countries and regions around the world. The main shipping routes and terminals invested by COSCO were mostly distributed along the 21 st Century BRI [START_REF] Xu | Evolution of regional inequality in the global shipping network[END_REF]. The port of Colombo is a hub port for transit cargo in South Asia. It is a transhipment port for the world routes in Europe, Asia, the Pacific and Indian Oceans [START_REF] Ducruet | The worldwide maritime network of container shipping: spatial structure and regional dynamics[END_REF]. Moreover, it is also a key hub port for China's economic development strategy for the BRI [START_REF] Wilmsmeier | Determinants of liner shipping network configuration: a two-region comparison[END_REF]. Algeciras is another key transport hub in the Mediterranean region. Its superior location conditions are attractive for shipping companies looking to expand markets from the north to the south [START_REF] Frémont | Global maritime networks: the case of Maersk[END_REF]. COSCO chose Algeciras as an important port of call for the Strait of Gibraltar. At the same time, the Algeciras Port Authority opened a tender for its 50-year franchise for the public/private terminal. COSCO Shipping Lines intended to acquire the new terminal in Algeciras to consolidate its position in the Mediterranean shipping market. The port of Istanbul is located on the southwestern coast of the Strait of Istanbul. It controls the passage from the Mediterranean Sea to the Black Sea via the Marmara Sea. In 2015, COSCO acquired a 65% controlling stake in the container terminal in Istanbul, Turkey. Rotterdam is a strategic regional hub port in the North-West Europe region. It is considered as an important connection throughout Northwestern Europe to expand in the Nordic market. COSCO Shipping Lines acquired a 35% stake in Rotterdam Euromaxterminal in May 2016. The expansion of the European branch line further utilized Rotterdam's trans-shipment function. Klang is located at the Straits of Malacca connecting between the Indian and Pacific Oceans. It is a hub that competes with Singapore. In 2015, CSCL was mainly affiliated with Klang and Singapore in the Straits of Malacca. COSCO was mainly affiliated with Singapore. The two firms had the same routes in Port Klang. As the matter of fact, the low loading and unloading fees for Klang attracted the liner shipping routes [START_REF] Frémont | Global maritime networks: the case of Maersk[END_REF]. Thus, the network status of Port Klang has increased during the integration process. Controlled by COSCO Shipping Lines and the construction of a sea-rail intermodal transport, Piraeus became a pivot port of the Mediterranean Sea near the Suez Canal [START_REF] Frémont | A geo-history of maritime networks since 1945. The case of the Compagnie Générale Transatlantique's transformation into CMA-CGM[END_REF]. It relies on the Central European Land and Sea Express railway. This is significantly important for highvalue commodities that are shipped in large volumes (Van der Putten, 2014). The objective is to connect among the Mediterranean Sea, the Black Sea, North America, West Asia, and Central and Eastern European countries is a highlight of the 21 st Century BRI. It can enrich the port's value as a consolidation base for hub port [START_REF] Van Der Putten | Chinese investment in the port of Piraeus, Greece: the relevance for the EU and the Netherlands[END_REF]. Panama established diplomatic relations with China on June 13, 2017. Therefore, COSCO and CSCL had a few important transit ports in the Panama Canal and the Manzanillo port (Mexico) with relative call advantages in Latin America. In 2015, COSCO and CSCL had low commonalities but strong complementarily in Manzanillo. Thus, its port status has increased after the integration.

The philosophy of COSCO Shipping Lines for route design and port selection mainly included port cargo volume, loading and unloading costs, port conditions, and alliance factors [START_REF] Parola | The spatial coverage of shipping lines and container terminal operators[END_REF]. In order to gain the economy of scales and synergies, COSCO Shipping Lines restructured the companies' common routes during the route integration process. Singapore, Busan, and Hong Kong were important hub ports in the networks and held strategic terminals for COSCO investment. Due to the high commonalities of routes, the combination of redundant routes during the integration process made port increased overall hub capacity significantly. Shanghai, Ningbo and Shenzhen were the main hub ports in China's coastal area. But, their status in the network analysis was relatively weak for the following reasons. On the one hand, this study excludes China's domestic trade routes 2016 for COSCO Shipping Lines. On the other hand, there were high commonalities in terms of route setting between COSCO and CSCL. Santos Port is the leading container port in South America but its terminal equipment is outdated, its infrastructure is insufficient, and its loading and unloading tools are antiquated. Therefore, its port throughput is low [START_REF] Chen | Analysis of shipping network of Southeast-Asian route along maritime Silk Road based on complex networks[END_REF]. At the same time, COSCO and CSCL showed commonalities in the Santos route. In 2016, COSCO Shipping Lines significantly increased ports of calls to other ports and hence, Santos Port's first position has decreased. Located in the Mediterranean at the northern end of the Suez Canal, Port Said is one of the world's largest trans-shipment ports. It was an important port of call for both COSCO and CSCL in the Suez Canal. However, the strategic position of the adjacent port Piraeus rose sharply while the shipping status of Said declined relatively.

Conclusions and discussion

In the context of COSCO and CSCL shipping firms' restructuration, the reorganization of the shipping networks was a key part of the integration of firm assets. Based on a complex network measurement, we draw the following conclusions by examining the shipping networks pattern and the network effects after integration.

• Our findings indicated that there was no high level of homogeneity between the shipping networks of COSCO and CSCL. Clearly, there were differences in firm marketing tactics.

The main objective of a firm is to maximize the firm's profitability. It requires the firms to take a proactive attitude in response to different customer needs and market changes. Shipping companies align key resources to avoid a decline in firm performance (Nagya et al., 2017). However, COSCO and CSCL had both overlaps and differences. While the two shipping firms were similar in the main shipping market, they had different comparative advantages in specific ports and segments. Both COSCO and CSCL are now using customer focus strategies to increase their understanding of the specific customers' preferences and predict how it is likely to change in the forthcoming years. To this end, shipping firms are able to make use of required resources and skills to fulfil customer needs [START_REF] Kaliszewski | Key factors of container port competitiveness: A global shipping lines perspective[END_REF]Bhattacharya et al., 2018). Moreover, the differentiation between the two firms in secondary markets from the north to the south was obvious. The difference in the alignment of different navigation areas provided support for the reorganization and expansion of their original shipping patterns. The similarities in the main market routes identified that excess capacity appeared in the shipping market. The resources of the two firms in some routes were redundant. As a result, the reconfiguration structure could be optimized to improve the efficiency of route utilization.

• During the restructuring of the COSCO networks, the increasing number of port calls leads to expand the existing size of the network. The newly added ports were mainly concentrated on the crucial market areas containing North West Europe, the Mediterranean Sea, and North America East. Regarding to the secondary markets, they were mainly concentrated in South America. In the meantime, the total number of shipping routes, the links of port-to-port and navigation links expanded significantly. Indeed, ports in different shipping zones also achieved a direct contact.

• In 2015, COSCO and CSCL followed the same market tendencies in their selection of ports in various shipping areas. This included a large proportion in the main market and a small proportion in secondary markets. Hence, their combination resulted in expanded port coverage, strengthened routes, and achieved economies of scale. In terms of port route, the ports with high similarity route were used to rationalize the network during the integration process in order to address redundant resources and integrate capacity allocation. In this process, secondary markets were strengthened to varying degrees, especially for South America and West Africa.

In the study of the globalization process of shipping firms, the hub port strategy played a vital role in the globalization of a shipping network based on internal growth strategies of firms such as the merging of CMA CGM and Delmas, as well as of Maersk Sealand and P&O Nedlloyd in 2005 [START_REF] Frémont | A geo-history of maritime networks since 1945. The case of the Compagnie Générale Transatlantique's transformation into CMA-CGM[END_REF]. The cultivation and selection of overseas hub ports were not only reflected in the overall increase in the network economy in the company optimization approach, but also generated the important strategy of exploring secondary markets under the effect of globalization. During the integration of COSCO and CSCL's routes, Piraeus and Rotterdam are important transhipment ports in the Mediterranean navigation area and the northwest European region respectively. The Algeciras and Manzanillo (Mexico) had transit impacts on improved connections between north and south markets. In the process of consolidating overseas markets and expanding secondary markets, COSCO Shipping Lines strengthened its overseas hub port strategy and selected ports with certain geographical advantages. A typical example is Algeciras, which participated in terminal operations and project cooperation.

In terms of study limitations, we analyzed shipping schedules but excluding other shipping factors such as ship capacity and sailing time. From the perspective of the route networks pattern, the overall operation of the shipping enterprise after reorganization could not be fully investigated. The actual overall pattern of the shipping industry globalization was limited due to the two firms involved in the restructuring of COSCO Shipping Lines that lacked a more comprehensive comparison. In future studies, we will consider other elements such as ship capacity and terminal operations to examine the overall pattern of the global shipping industry. The future M&A strategy means that shipping companies will open up new organic growth paths, markets and customers through acquisition. Its purpose is to boost up the economic growth and sustainable development of shipping enterprises in the complicated operating environment in the future. Therefore, M&A strategies will be continuously promoted. In addition to economies of scope and scale, operations management is also an important indicator that we can use to assess the impact of mergers and acquisitions on enterprises. After the merger, it is necessary to consider whether the information exchange and practices of shipping companies can fully solve various problems in the linkage and the overall operating efficiency.

Figure 1 .

 1 Figure 1. Shipping network of COSCO in 2015 based on weighted degree

Figure 3 .

 3 Figure 3. Changes in ports covered from 2015 to 2016 Notes:Red indicates an additional port, blue indicates reduced ports, and symbol size is the relative size of port degree value, indicating the corresponding connectivity of the port in the network.

Figure 4 .

 4 Figure 4. Links between different COSCO navigation areas in 2015

Figure 7 .

 7 Figure 7. Changes in ports from 2015 to 2016 by port change index

Figure 8 .

 8 Figure 8. Shipping network in 2016 based on weighted degree

Table 1 .

 1 Evaluation of Port Change Index

	Ki	Categories	Explanations
	𝐾 𝑖 ≤0	Reduction	In 2016, the port route was weaker than/equal to the maximum
			strength of the port route for the two enterprises in 2015.

Table 2 .

 2 COSCO and CSCL covered Ports in 2015

	Navigation Area	No. COSCO ports	No. CSCL Ports	No. call the same port	Total no. ports	Share of the same port calls (%)
	Northwestern Europe	11	10	10	11	0.91
	Australia/New Zealand	8	8	7	9	0.78
	West Africa	4	6	4	6	0.67
	East Asia	31	32	25	38	0.66
	North America West 10	11	8	13	0.62
	South Asia	4	4	3	5	0.6
	The Mediterranean	14	21	11	24	0.46
	Southeast Asia	22	9	9	22	0.41
	East Africa	4	3	2	5	0.4
	Middle East	13	19	9	23	0.39
	South America East 7	11	5	13	0.38
	North America East 8	11	5	14	0.36
	South America West 4	5	2	7	0.29

Table 3 .

 3 The top 15 ports by betweenness centrality port change index

	Categories	Ports	Median Centrality	𝐾 𝑖1	𝐾 𝑖2	𝐾 𝑖
	Expansion Growth	Colombo	0.061	1.500	9.000 10.500
		Algeciras	0.060	2.500	2.500 5.000
		Istanbul	0.068	2.400	2.000 4.400
		Rotterdam	0.103	1.364	1.900 3.264
		Klang	0.212	0.724	1.615 2.340
		Piraeus	0.199	1.300	0.786 2.086
		Manzanillo 0.094	1.000	1.000 2.000
	Low intensive growth Busan	0.117	0.714	0.703 1.417
		Singapore	0.435	0.267	0.968 1.234
	High intensive growth Hong Kong 0.052	0.318	0.656 0.974
		Shanghai	0.083	0.231	0.723 0.954
		Said	0.052	0.095	0.800 0.895
		Shenzhen	0.126	0.259	0.575 0.834
		Santos	0.069	0.286	0.500 0.786
	Reduction	Ningbo	0.052	-0.750 0.000 -0.750

Acknowledgments:

This work was supported by the Research Grant of the National Natural Science Foundation of China under Grant [41971155], Major program of National Social Science Fund of China under Grant [16ZAD016], the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities under Grant [17JJD790007].

Declaration of interest statement:

There are no conflicts of interest.