Seaborn Trade Activity Estimation

Seaborn Trade Activity Estimation#

This section introduces the use of an alternative big data source – Automatic Identification System (hereafter, AIS) – to monitor seaborn trade activity in Lebanon. AIS was originally developed by the International Maritime Organization in 2004 to prevent collisions between large vessels. This system requires all commercial ships (gross tonnage greater than 300) and passenger ships to broadcast their position via ground stations and satellites.

A nascent literature has been dedicated to convert raw AIS messages into volumes of trade (Arslanalp et al. [2021]; Cerdeiro et al. [2020]; Jia et al. [2019]; Verschuur et al. [2021]; Verschuur et al. [2021]). These papers utilize dynamic information on ship movements, static characteristics of each ship, and reported draft (depth of submergence), to estimate the amount of goods offloaded or loaded at a certain port. While this methodology has produced results that correlate with aggregated trade value statistics, users should use the results with caution as a near real-time proxy for relative trends in trade. The methodology is based primarily on draft values from AIS data which are subject to a few limitations. Thus, AIS-derived trade values may not match perfectly with reported values.

For the purposes of the monitor, we implement and adjust the methodology described in “Tracking trade from space: an application to pacific island countries” [Arslanalp et al., 2021], using the same AIS data used by the authors, facilitated by the UN Global Platform AIS Task Team.

The final data product, Trade Estimation for Lebanon, visualized in the subsequent notebooks, can also be accessed through Project SharePoint.

Note

This workflow and notebooks were originally developed by Cherryl Chico in support of the World Bank’s Pacific Observatory project. It has been adapted for the Syria Economic Monitor by Andres Chamorro.

Methodology#

The method can be broken down into the following four steps (each step was implemented in a python notebook).

Step 1. Data Extraction#

We retrieve all AIS messages that intersect a 10-kilometer buffer from each port in Lebanon (Bayrut, Sayda, and Tarabulus), available from December 1st, 2018, to November 1st, 2023. We use international port boundaries provided by Verschuur et al. [2021].

Summarize AIS Data by Routes#

We collapse the AIS data into routes. An incoming or outgoing route is captured by grouping continous AIS messages from a unique vessel traversing from port buffer to port boundary (or vice versa). We keep the first and last AIS messages for each route as they contain relevant arrival and departure information.

Step 2. Data Filtering and Ship Registry Information#

There may be routes that are quick because of small movements by vessels located at the edges of the boundaries. To remove the noise, we say that a vessel has fully exited the port when it spends at least 1 day outside of Port Boundary. Otherwise, we treat consecutive routes within 24 hours of each other as one.

We then combine the routes data with Ship Registry data to get the finer vessel categories and other information on the vessel required for trade estimation. The matching is done using both MMSI and IMO numbers from both data sources. We validate the match by comparing the vessel names.

Step 3. Backpropagate Departure Draft#

The difference in arrival vs. departure draught is a key calculation to estimate trade. However, the difference between outgoing and incoming draft is only identified in a subset of port calls. For the remaining routes, we apply the back-propagation technique, searching for the arrival draft at the next port visited.

Step 4. Trade Estimation#

Volume Displacement Method#

We estimate the volume of the load of the ship based on the volume of the displaced water. The volume of the displaced water by the ship is derived from the volume of a rectangular block with the same length and width. The ship’s draught or the height of the ship submerged in water is the height of the rectangular block. Since the ship is not rectangular, we reduce this by a factor called block coefficent that is dependent on the ship’s draught. To convert volume into weight (in tonnes), we multiply the volume by the density of saltwater.

Design Block Coefficient#

From any reported draught \(D_r\), displacement at reported draught \(Disp_r\) can be estimated as the volume of a rectangular block with the same dimensions of the ship. The length and width of the ship represents the length and width of the rectangular block and the draught represents the height. The volume is adjusted by a factor called block coefficient and then converted to tonnes by multiplying by the density of salt water.

\[Disp_r = Cb_rLW\rho d_r\]

where \(Cb_{r}\) is the block coefficient at draught \(d_{r}\), \(L\) is the length, \(W\) is the width, \(\rho\) = density of salt water, and \(d_r\) = reported draught

We can derive the block coefficent for any given draught as follows:

\[Cb_{r} = \frac{Disp_{r}}{LW\rho*d_{r}}\]

Given design draught, and design block coeffient, the block coefficient at reported draught can be estimated as:

\[ Cb_r = 1 - (1 - Cb_d) \frac{d_d}{d_r} ^ \frac{1}{3} \]

Implementation#

01 Data Extraction
02 Data Filtering and Ship Registry Information
03 Backpropagate Departure Draft
04 Trade Estimation

Preliminary Results (October 2024)#

Count of Unique Vessels#

Ship Type Level 2	Ship Type Level 5	2019	2020	2021	2022	2023	2024
Bulk Carriers	Aggregates Carrier	0	0	1	1	0	0
Bulk Carriers	Bulk Carrier	20	18	11	9	2	0
Dry Cargo/Passenger	Container Ship (Fully Cellular)	37	29	27	16	17	14
Dry Cargo/Passenger	General Cargo Ship	61	53	37	23	21	12
Dry Cargo/Passenger	General Cargo Ship (Open Hatch)	1	0	2	0	0	0
Dry Cargo/Passenger	General Cargo Ship (with Ro-Ro facility)	1	0	0	1	0	1
Dry Cargo/Passenger	Heavy Load Carrier	0	1	0	1	0	0
Dry Cargo/Passenger	Livestock Carrier	8	5	2	1	1	0
Dry Cargo/Passenger	Ro-Ro Cargo Ship	5	4	2	2	1	0
Dry Cargo/Passenger	Vehicles Carrier	3	2	0	0	0	0
Tankers	Chemical/Products Tanker	3	5	5	1	1	2
Tankers	Vegetable Oil Tanker	1	0	0	0	0	0

Trade Volume Estimates#

The vessel-level data can be accessed via this link.

References#

AKV21(1,2): Serkan Arslanalp, Robin Koepke, and Jasper Verschuur. Tracking trade from space: an application to pacific island countries. IMF Working Papers, 2021(225):A001, 2021. URL: https://www.elibrary.imf.org/view/journals/001/2021/225/article-A001-en.xml, doi:10.5089/9781513593531.001.A001.
AJL17: George Azzari, Meha Jain, and David B. Lobell. Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries. Remote Sensing of Environment, 202:129–141, December 2017. doi:10.1016/j.rse.2017.04.014.
BRVLJ10: I. Becker-Reshef, E. Vermote, M. Lindeman, and C. Justice. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114(6):1312–1323, June 2010. doi:10.1016/j.rse.2010.01.010.
BF13: Douglas K. Bolton and Mark A. Friedl. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173:74–84, May 2013. doi:10.1016/j.agrformet.2013.01.007.
CKomaromiLS20: Diego A. Cerdeiro, András Komáromi, Y. Liu, and Mamoon M. Saeed. World seaborne trade in real time: a proof of concept for building ais-based nowcasts from scratch. IMF Working Papers, 2020.
DMCS03: Paul C. Doraiswamy, Sophie Moulin, Paul W. Cook, and Alan Stern. Crop Yield Assessment from Remote Sensing. Photogrammetric Engineering & Remote Sensing, 69(6):665–674, June 2003. doi:10.14358/PERS.69.6.665.
GTS+14: Dirk Geudtner, Ramón Torres, Paul Snoeij, Malcolm Davidson, and Björn Rommen. Sentinel-1 system capabilities and applications. In 2014 IEEE Geoscience and Remote Sensing Symposium, volume, 1457–1460. 2014. doi:10.1109/IGARSS.2014.6946711.
HT04: Ramaswamy Hariharan and Kentaro Toyama. Project Lachesis: Parsing and Modeling Location Histories. In Geographic Information Science. Volume 3234, volume 3234, 106–124. 2004. doi:10.1007/978-3-540-30231-5_8.
HDM+02: A Huete, K Didan, T Miura, E.P Rodriguez, X Gao, and L.G Ferreira. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2):195–213, November 2002. doi:10.1016/S0034-4257(02)00096-2.
JA15: Hadi H. Jaafar and Farah A. Ahmad. Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands. International Journal of Remote Sensing, 36(18):4570–4589, September 2015. doi:10.1080/01431161.2015.1084434.
JPZ+22: Zhonglin Ji, Yaozhong Pan, Xiufang Zhu, Dujuan Zhang, and Jinyun Wang. A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics. Ecological Indicators, 137:108759, April 2022. doi:10.1016/j.ecolind.2022.108759.
JPS19: Haiying Jia, Vishnu Prakash, and Tristan Smith. Estimating vessel payloads in bulk shipping using ais data. International Journal of Shipping and Transport Logistics, 11:25, 01 2019. doi:10.1504/IJSTL.2019.096864.
Joh16: David M. Johnson. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geoinformation, 52:65–81, October 2016. doi:10.1016/j.jag.2016.05.010.
JLH+18: František Jurečka, Vojtěch Lukas, Petr Hlavinka, Daniela Semerádová, Zdeněk Žalud, and Miroslav Trnka. Estimating Crop Yields at the Field Level Using Landsat and MODIS Products. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 66(5):1141–1150, October 2018. doi:10.11118/actaun201866051141.
KMC20: Kefyalew Sahle Kibret, Carsten Marohn, and Georg Cadisch. Use of MODIS EVI to map crop phenology, identify cropping systems, detect land use change and drought risk in Ethiopia – an application of Google Earth Engine. European Journal of Remote Sensing, 53(1):176–191, January 2020. doi:10.1080/22797254.2020.1786466.
Kun14: Robin Kunaka, Charles; Carruthers. Trade and Transport Corridor Management Toolkit. World Bank Training, World Bank, 2014. License: CC BY 3.0 IGO. URL: http://hdl.handle.net/10986/18241.
Pet18: Lillian Kay Petersen. Real-Time Prediction of Crop Yields From MODIS Relative Vegetation Health: A Continent-Wide Analysis of Africa. Remote Sensing, 10(11):1726, November 2018. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute. doi:10.3390/rs10111726.
PAK+22: Hoa Thi Pham, Joseph Awange, Michael Kuhn, Binh Van Nguyen, and Luyen K. Bui. Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices. Sensors, 22(3):719, January 2022. doi:10.3390/s22030719.
RRK+09: Atiqur Rahman, Leonid Roytman, Nir Y. Krakauer, Mohammad Nizamuddin, and Mitch Goldberg. Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh. Sensors, 9(4):2968–2975, April 2009. doi:10.3390/s90402968.
SH15: Jingjing Shi and Jingfeng Huang. Monitoring Spatio-Temporal Distribution of Rice Planting Area in the Yangtze River Delta Region Using MODIS Images. Remote Sensing, 7(7):8883–8905, July 2015. doi:10.3390/rs70708883.
SCC+13: N. T. Son, C. F. Chen, C. R. Chen, L. Y. Chang, H. N. Duc, and L. D. Nguyen. Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam. International Journal of Remote Sensing, 34(20):7275–7292, October 2013. doi:10.1080/01431161.2013.818258.
TMA+23: Gizachew Ayalew Tiruneh, Derege Tsegaye Meshesha, Enyew Adgo, Atsushi Tsunekawa, Nigussie Haregeweyn, Ayele Almaw Fenta, Tiringo Yilak Alemayehu, Temesgen Mulualem, Genetu Fekadu, Simeneh Demissie, and José Miguel Reichert. Mapping crop yield spatial variability using Sentinel-2 vegetation indices in Ethiopia. Arabian Journal of Geosciences, 16(11):631, November 2023. doi:10.1007/s12517-023-11754-x.
VKH21a(1,2): Jasper Verschuur, Elco E Koks, and Jim W Hall. Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data. PLoS One, 16(4):e0248818, April 2021.
VKH21b: Jasper Verschuur, Elco E. Koks, and Jim W. Hall. Observed impacts of the covid-19 pandemic on global trade. Nature Human Behaviour, 5(3):305–307, Mar 2021. URL: https://doi.org/10.1038/s41562-021-01060-5, doi:10.1038/s41562-021-01060-5.
WDS15: J. S. J. Wijesingha, N. L. Deshapriya, and L. Samarakoon. Rice Crop Monitoring and Yield Assessment with MODIS 250m Gridded Vegetation Products: A Case Study of Sa Kaeo Province, Thailand. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3:121–127, April 2015. doi:10.5194/isprsarchives-XL-7-W3-121-2015.
WZZ+23: Bingfang Wu, Miao Zhang, Hongwei Zeng, Fuyou Tian, Andries B Potgieter, Xingli Qin, Nana Yan, Sheng Chang, Yan Zhao, Qinghan Dong, Vijendra Boken, Dmitry Plotnikov, Huadong Guo, Fangming Wu, Hang Zhao, Bart Deronde, Laurent Tits, and Evgeny Loupian. Challenges and opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4):nwac290, March 2023. doi:10.1093/nsr/nwac290.
WorldBank21: World Bank. World Development Report 2021 : Data for Better Lives. World Bank, 2021. License: CC BY 3.0 IGO. URL: http://hdl.handle.net/10986/35218.
WorldBank22: World Bank. Syria Economic Monitor - Spring 2022 : Lost Generation of Syrians. Economic Updates and Modeling, World Bank, 2022. License: CC BY 3.0 IGO. URL: http://hdl.handle.net/10986/37617.