Pick Fishes by Seafood Watch Data

Posted on Nov 20, 2020

Data Background

This project used Selenium to scrape data from the Monterey Bay Aquarium website www.seafoodwatch.org. This organization collects a range of data on seafood production techniques and fisheries to generate scores for each seafood type. These scores serve to indicate the environmental impact and sustainability of each seafood type.

The goal is to give recommendations to the consumer for choosing seafood that is fished or farmed in the most environmentally sustainable manner. I obtained their data for 97 types of seafood and further analyzed it to identify various trends as to the best/worst seafood to consume, the various production methods utilized and production locations.

Seafood in Danger

  • Overharvesting of seafood
  • Stocks in peril / need for sustainable production of seafood
  • Environmental impact of seafood harvesting and production
  • Illicit fishing flaunts regulations and abuses crews 

Seafood Watch Program

The Seafood Watch program from the Monterey Bay Aquarium (www.seafoodwatch.org) aids consumers and business in choosing seafood through a scoring system that assigns both a numeric score and a recommendation for each seafood type. This is based on numerous scientific investigations and analyses that determine the status of each seafood by looking individually at the available sources for each. The numeric scores assigned range from 0 to 10 while the recommendation categories are "Best Choice", "Good Alternative" or "Avoid". 

Data Objectives

I sought to make the Seafood Watch scoring system more user-friendly for consumers and businesses by the following:

  • Get scores for all sources in a given seafood group type, find average and distributions
  • Determine best and worst seafood types according to scores
  • Determine relationship between source production method and scores
  • Determine relationship between source location and scores 

Data Science Methodology

Selenium / Python was used to scrape the Seafood Watch website to obtain the following data:

  • Seafood type name (group)
  • Number of recommendation categories
  • Heading text
  • Sources
    • Type (species common names)
    • Catch/production method
    • Location
    • Overall numerical score
    • Seafood Watch subscores for Fisheries (4 - 5 items) or Aquaculture (10 items)
    • Source assessment text
  • Eco-certifications
    • Type (species common names)
    • Catch/production method
    • Location

Code available on GitHub

Data Science Results

Conclusions

  • Highest scoring seafoods in general were:
    • Scallop, Seaweed, Catfish, Sturgeon, Bass, Abalone, Tilapia, Sole, Turbot, Arctic Char
    • These have a higher probability to be more sustainable / environmentally conscious choices given no further details.
  • Choose seafood labeled as Eco-Certified by one of the certifying organizations. These would be expected to score highly in the parallel Seafood Watch classification system.
  • Choose seafood produced by aquaculture / farming techniques as sources using these methods scored higher on average.
  • Always avoid the following as these had no sources that scored above the mean:
    • Shark, Toothfish, Triggerfish, Brill, Orange Roughy, Sardine, Pike

 

Caveats

  • Assume the integrity of Seafood Watch scoring system. Assume sources studied are rather complete, but likely there are other sources not represented, including unregulated, illegal, etc.
  • Seafood types analyzed represent a group of similar species, it is possible that not all species in a given type are the same with regards to scoring
  • There are some seafood types that receive low scores due to incomplete or lack of data
  • Bias towards seafood types commonly consumed in the US, as well as US sources

Future Work

  • Look further into subscores and EDA for best / worst types.
  • Further investigation of location text to determine ranking of best / worst locations
  • Further investigations into individual species within groups

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

About Author

Ryan Kniewel

I have a diverse background in biotechnology and synthetic biology with over 20 years of experience engineering microorganisms using tools from biochemistry, molecular biology, genetics and bioinformatics. I am expanding my knowledge base to address a new range...
View all posts by Ryan Kniewel >

Related Articles

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