Recent traffic dynamics in the European container port system
Big Data versus Small Data: Container Port Traffic …...Big Data is Great, but What About Small...
Transcript of Big Data versus Small Data: Container Port Traffic …...Big Data is Great, but What About Small...
Ad Hoc Expert Meeting on Measuring Shipping Connectivity and
Performance: The Need for Statistics and Data, Geneva, May 15
2017
Big Data versus Small Data: Container Port Traffic and Maritime Connectivity
Jean-Paul RodrigueDept. of Global Studies & Geography, Hofstra University, New York, USA
‘Big Data’ versus ‘Small Data’
• Big Data• Massive quantities.
• Usually collected automatically by sensors.
• Collected in real time.
• Happens ‘by accident’ as a by-product of a digital footprint.
• Ex-post usefulness.
• Small Data• Limited quantities.
• Collected semi-automatically (often human input).
• Collection delayed by reporting systems (daily, monthly, quarterly, annually).
• Purposefully collected (regulation, reporting, decision making).
• Ex-ante usefulness.
1. Container Port Traffic Data
Big Data is Great, but What About Small Data?
▪ Frustration about container port traffic data• One of the world’s most simple and indicative data is not
comprehensively available.
• Port authority web sites are a mess:• Often difficult to find traffic data; often out of date.
• Data published in a variety of inconvenient formats (GIF, PDF).
• Wide variations in the consistency and level of detail.
• No standards.
• Data collection/compilation is usually a manual process.
• Several regional trade groups collect and maintain data from their constituents:• AAPA, ECLAC, ESPO.
• No international agency has ‘claimed the ownership’ of the data.
Global Container Ports Database
PORT Port name
UNLOCODE United Nations Code for Trade and Transport Locations.
STATUS Active, Merged, Part, Inactive
CITY The metropolitan area in which the port is located (or is mainly serving)COUNTRY Country
RANGE Maritime rangeLONG; LAT Longitude and latitude
ALIAS Alternate port name (if more than one usual name)Port Authority Name of the port authoritySource Link to online data source
DEPTH_X Max alongside depth of container terminals; MLW
CHANNEL MLW Port Channel Depth
REEFER Number of reefer slots at the terminal
Y_XXXX Annual traffic in TEU for year XXXX
550 active ports totaling 645 M TEU of volume in 2015
Container Ports and Main Maritime Ranges of the Americas, 2015
Net Container Volume Changes in the Americas, 2010 / 2015
Share of the Maritime Ranges of the Americas in Total Container Volumes, 1990-2015
8.4210.89 16.56 24.29
26.11 29.54
6.91
9.4212.42
17.10
17.64 22.14
1.21
1.64
2.503.15
3.94 4.83
2.80
4.358.85
12.44
15.62 17.22
0.481.45 2.39
4.959.83 13.39
0.98 2.13 3.69 7.42 10.00 12.10
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1990 1995 2000 2005 2010 2015
South American East Coast
South American West Coast
Caribbean
Gulf Coast
North American East Coast
North American West Coast
Cargo Handled by the Top 5 North American Container Ports, 1985-2015 (in TEUs)
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TEU
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ion
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Los Angeles Long BeachNew York/New Jersey SavannahVancouver Total North American TrafficShare of Top 5 Ports
Monthly Container Traffic at the Port of Los Angeles, 1995-2017
0
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Out Loaded
In Loaded
In Empty
Out Empty
Proposal: A Data Template for Automated Data Harvesting
[Channel] [Depth] [Berths]
[Cranes] [RTGs] [Yard]
[Capacity]
Facilities
[Calls] [Total] [Full] [Empty]
[Inbound] [Outbound]
[Transshipment] [40] [40HC]
[20] [Reefer] [Other]
Container Traffic
CY, FY, Monthly
Metadata
Terminal
XML
Filter/ Query
2. Developing a Global Connectivity Index
The Components of Connectivity: The ‘Bowtie Approach’
Gateway or hub
(‘connector’)
Foreland ConnectivityHinterland Connectivity
Global air and
maritime shipping
networks
Regional corridors (rail,
road, fluvial)
LSCI
Functional Variations in Connectivity
Foreland Connectivity
Hinterland Connectivity
Global Hub
Global GatewayRegional Hub
Regional Center
Top 25 Gateways, Global Gateways Index, 2010
0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0%
Shanghai
Singapore
Hong Kong
Shenzhen
Dubai
Los Angeles
Busan
Guangzhou
Ningbo
Qingdao
Rotterdam
Tianjin
Kuala Lumpur
Kaohsiung
Antwerp
Hamburg
Bangkok
New York
Tokyo
Johor Bahru
Containers Air Cargo
Connectivity Pattern of the World’s Major Maritime Bottlenecks
Panama
(22.2 M TEU)
TI: 35%
(17.1 M TEU)
TI: 80%
(15.6 M TEU)
TI: 60%
(30.2 M TEU)
TI: 75%(59.4 M TEU)
TI: 80%TEU (2015)
Transshipment Incidence (TI)
Oresund
(11.7 M TEU)
TI: 75%
Gibraltar Hormuz Malacca
Suez
The connectivity of intermediacy
Container Traffic at Main Ports around the Panama Canal
Container Traffic Handled at the Main Ports Around the Suez Canal
Container Traffic at Main Ports around the Strait of Malacca
Container Traffic Handled at the Main Ports Around the Strait of Hormuz
Container Traffic Handled at the Main Ports Around the Strait of Gibraltar
Container Traffic Handled at the Main Ports Around the Strait of Oresund
Conclusion: Big Data = More Inertia?
I’m too nimble and
sexy for you