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• Port authority web sites are a mess:• Often difficult to find traffic data; often out of date.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)

0%

10%

20%

30%

40%

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60%

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19

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TEU

Mill

ion

s

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