Spatial pattern of location advantages of ports along the Maritime Silk Road

doi:10.1007/s11442-021-1837-9

Abstract

Abstract:

Location advantages of ports refer to the current developments of ports based on their conditions, such as geographic location, traffic accessibility and hinterland economy, etc., and the spatial pattern of ports’ location advantages reflects the spatial distributions, the regularities and the correlations among their conditions for development. A good understanding of the spatial patterns of ports’ location advantages can help to better identify the relative advantages of ports, position ports’ functions and make strategic plans for development. This paper selected 1259 ports from 63 countries along the Maritime Silk Road as research objects and builds an accessing model to analyze their location advantages on the bases of six factors: the influence of strategic shipping pivot, the competitiveness of port location potential, port network status, the influence of city, the influence of traffic trunk, and road network density in hinterland. The study has the following three findings. Firstly, the location advantages of ports show a “high-low-high” distribution pattern from the west to the east, displaying an obvious “core-periphery” regionalized distribution. Secondly, most ports have high location advantages, mainly located in Strait of Malacca, the United Arab Emirates, northern Mediterranean coastal region and China-Japan region, the top 10 ports are mainly located in Singapore, China, Malaysia and Japan, indicating that the shipping industry in Asia-Pacific region has stepped to the far front of the global competition; slow economic growths, wars, far away from the Belt and Road countries or bad climate have low location advantages, mainly located in African coastal areas, Oceania, Northeast Europe and Russia. Thirdly, compared with the landward location advantages, the seaward location advantages have a higher influence, and different indicators of location advantages have different influences on the evaluation results, the competitiveness of port location potential being the core indicator.

Key words: Maritime Silk Road, location advantages, ports, shipping network, spatial patterns

MOU Naixia, WANG Chunying, CHEN Jinhai, YANG Tengfei, ZHANG Lingxian, LIAO Mengdi. Spatial pattern of location advantages of ports along the Maritime Silk Road[J].Journal of Geographical Sciences, 2021, 31(1): 149-176.

Figures/Tables 22

Figure 1

Table 1

Summary of the main research on location advantages"

Reference	Research content	Conclusions	Related indicator
Wang et al., 2018b	The correlation between port location and strategic position	Port will exert more influence on the trades and the transportation of strategic supplies within its area if it belongs to a strategic hub, a strategic corridor or a strategic sea	The influence of strategic shipping pivot
An et al., 2000	Analyze the development situation of large- scale container ports and fierce competition in the world	Discuss the blueprint of China’s container hub ports, and verify the correlation between port location conditions and cargo source competitiveness	The competitiveness of port location potential
Peng et al., 2018b	Use weighted degree centrality, weighted closeness centrality and weighted betweenness centrality to evaluate the networks status of ports	Network status index plays a decisive role in the comprehensive competitiveness of a port: the higher a port’s ranking in network status, the stronger its comprehensive competitiveness	Port network status
Lu and Chen, 2008	Evaluate the location advantages of Wuhan city circle based on the gravity scale of administrative division elements and traffic factors	Wuhan city circle has significant location advantages compared with other counties and cities in the province	The influence of city
Jin et al., 2008	Select the density of traffic network, the influence degree of traffic trunk line and the degree of location advantages to identify the traffic location advantages of each regional unit in China from the three dimensions of point, line and surface	The distribution of regional traffic advantage in China is “partial normal”	The influence of traffic trunk and road network density in hinterland
Yang et al., 2008	Select the length of national railway, inland waterway and highway to quantitatively analyze the development of China’s inter provincial comprehensive transportation	There are serious differences of the transportation development in China	Road network density in hinterland

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Table 7

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Assessment index	Data	Data sources
The influence of strategic shipping pivot	The distribution of strategic hubs, strategic corridors and strategic sea	See reference (Wang et al., 2018b) (44 strategic hubs, 7 strategic corridors and 3 strategic seas are in worldwide)
The competitiveness of port location potential	The hinterland economic data, the throughputs of ports, port sizes	The economic data is from https://www.yidaiyilu.gov.cn/ and http://www.worldbank.org/; The throughputs data of ports are from http://www.worldbank.org/ and http://data.stats.gov.cn/gjwz.htm; Ports’ sizes are from World Port Index (http://www.nga.mil/)
Port network status	AIS data	Global AIS data of 2014
The influence of city	City information	From OpenStreetMap (http://www.openstreetmap.org/)
The influence of traffic trunk	Road network information	From OpenStreetMap (http://www.openstreetmap.org/)
Road network density in hinterland	The length of road network, hinterland areas	From https://www.yidaiyilu.gov.cn/ and https://www.cia.gov/index.html

Respects	Range of influence (km)	Attenuation coefficient	Weight
Capital	400	-1/10000	40
First-level administrative center	100	-2/10000	20
Second-level administrative center	50	-3/10000	15

Respects	Range of influence（km)	Attenuation coefficient	Weight
Railway (Railway Station)	60	-1/30	2
Highway	45	-1/30	1.5
Trunk	15	-1/15	1
Airport	10	-1/10	1

Seaward index	Indicators	Weights	Landward index	Indicators	Weights
	tde influence of strategic shipping pivot	0.164		tde influence of city	0.156
	tde competitiveness of port location potential	0.196		tde influence of traffic trunk	0.134
	Port network status	0.185		Road network density in hinterland	0.165

Rank	Port	Country	Rank in SF	Rank in SP	Rank in SN	Rank in LC	Rank in LT	Rank in LD	Score
1250	Petropavlovsk	Russia	1259	921	1236	1257	1258	1121	0.157
1251	Nikol Skoye	Russia	1258	920	1237	1256	1259	1122	0.142
1252	De Kastri	Russia	1201	918	1190	1236	1249	1123	0.132
1253	Nikolayevsk Na Amur	Russia	1200	911	1196	1237	1250	1124	0.127
1254	Okha	Russia	1193	912	1197	1238	1251	1125	0.127
1255	Vitaz Marine Terminal	Russia	1194	919	1203	1255	1252	1126	0.122
1256	Okhotsk Iy Reyd	Russia	1256	910	1214	1258	1257	1127	0.114
1257	Bukhta Nagayeva	Russia	1257	916	1234	1259	1258	1128	0.099
1258	Whangaroa	New Zealand	907	1041	1239	1253	928	894	0.079
1259	Kingston	New Zealand	1157	1042	1240	1254	1209	895	0.032