OECD Fishing Commerce Data Analysis

Posted on May 10, 2021

The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Introduction

The Organization for Economic Cooperation and Development (OECD) is an international organization founded to promote world traded and the economic development of its member nations. Fishing trade data is among the OECD's interest, and it has several datasets that describe the fishing economy of each nation.  The data recorded by the OECD changes as the needs of its member nations change, and it frequently begins recording new data while also halting the acquisition of data that hasn't proven to be useful.

The code used for this project can be found on Github.

Discontinued Data Sets

Recently, the OECD has discontinued the a dataset that can be found here, in the OECD open data portal.  This dataset records what the OECD characterizes as Foreign Landings at Domestic Ports (FLDP). FLDP is defined as a transaction between a member nation fishery and a foreign shipping partner, where the shipping partner sells a quantity of fish to the local fishery port. The OECD has stopped recording FLDP data in 2016, and this project explores the the data further to determine whether or not this data could still be useful to the OECD according to the organization's mission.

FLDP Data Set Analysis - Pt. 

The FLDP data set was recorded between the years of 2000 to 2015.  Each entry represents a single shipment received a member nation fishery, including:

  • The name of the nation receiving the shipment
  • The Year the shipment was received
  • The species of fish received in the shipment
  • The weight of the shipment in Tonnes
OECD - International Fishing Commerce Data Analysis
Figure 1: Total FLDP in Tonnes of all fish species received per country in the year 2001.

In the dataset, it is clear that certain member nations consistently receive a larger quantity of fish than in other nations year after year.  It is clear that Norway receives the higher FLDP quantities than most OECD nations, and this is reflected by the consistency of Norway's reporting. Figure 1 shows a heatmap of the total FLDP shipments received by each member nation in Tonnes. Norway appears to be the clear leader in fishing imports, but in taking a closer look at the data, Norway's dominance might not be as pronounced as it appears.

 

OECD - International Fishing Commerce Data Analysis
Figure 2: FDLP quantities in tonnes per species per year received by Norway.

FLDP Data Set Analysis - Pt. 2

In contrast to Norway, many nations fail to show the same consistency in their reporting.  Nations that appear to receive smaller quantities of FDLP shipments demonstrate inconsistent trends.  The most notable of these trends is the tendency to report no data in a given year.  For example, in Spain, the total number of fish appears to decrease over the years of 2010 and 2012 until it reaches close to 0 tonnes in 2013.  Spain failed to report FLDP metrics received in 2014 and 2015, the last two years that this data was recorded by the OECD. Figure 2 suggests that that several member nations did not engage in accurate reporting for various years between 2001 and 2015.

OECD - International Fishing Commerce Data Analysis
Figure 3: FDPL quantities in tonnes per species per year received by Spain

In the OECD data set documentation, each nation has a unique description of exactly how FDLP data is defined and reported.  This implies that the OECD does not hold member nations to a universal measurement standard of reporting. Due to this inconsistency, it is not clear whether each member nation was recording comparable data. For example, Spain notes that it only records FLDP data from the EU, but it is unclear whether FLDP from non-EU countries are accepted by the Spanish Fisheries. Sweden refers users to a detailed documentation of it's reporting practices overseen by the Swedish Agency for Marine and Water Management, but it is not clear how this organization communicates with external nations and international affairs.

Conclusion

Given the OECD's mission statement to stimulate international economic progress it would be useful for the OECD to continue to record FLDP data. It can be inferred that the OECD has chosen to stop recording FLDP data because of the inconsistency in reporting and not because of the lack of impact this data could have on economic development. Other fishery and aquaculture metrics, for example the Fishery Support Estimate (FSE), have clear international standards for evaluating economic development that are consistent across all member nations. If the OECD were able to adopt similar standards for FLDP records, this data could remain useful to the OECD's mission.

About Author

Matthew Boubin

Matt Boubin is an electrical engineer with three years of digital signal processing experience in commercial aviation.
View all posts by Matthew Boubin >

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI