NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > R Shiny > Waste Management: Where does our waste go?

Waste Management: Where does our waste go?

Bettina Meier
Posted on Oct 24, 2019
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

flickr Image by Jeffrey Beall; Creative Commons Licence: Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0)

 

The issue of waste management is of increasing interest as we grow aware of the problems posed by the amount of waste that is currently produced and how it is disposed of. What happens to our household waste once it is picked up by our local council? How much does household or industry waste contribute to our overall waste production as a society?

I wanted to look into some of these questions and visualise the types of waste we produce, the routes waste takes to where it is processed, and how it is processed.

For this analysis I chose the Waste Data Interrogator 2018 published by the UK Government, Department for Environment, Food and Rural Affairs, as a resource. This data set contains comprehensive information reported by ~6,000 operators of regulated waste management facilities in England on the quantities and types of waste received as well as on waste sent on to other facilities.

The database is restricted to waste management facilities in England as environmental regulation responsibilities for Wales and Scotland are being held by Natural Resources Wales (NRW) and the Scottish Environment Protection Agency (SEPA), respectively.

Types

Waste types are categorised based on the European Waste Catalogue (EWC), a hierarchical list of waste descriptions established by the European Commission. Reporting for the Waste Data Interrogator 2018 resource uses two hierarchical levels: a basic system with three main categories โ€“ (1) inert/construction and demolition, (2) household/industrial and commercial, and (3) hazardous. In addition there is a system of higher granularity with specific EWC descriptors.

Looking at the composition of waste received across waste management facilities, 54.2% is Household/Industrial/Commercial, 42.7% Inert/Construction and Demolition waste, and 3.1% hazardous waste, which is defined as waste that is harmful to humans or the environment.

At a more detailed level, the five largest waste classes are construction and demolition waste (41%), municipal waste (25.7%), waste and water treatment (24.3%), a category of unspecified waste (2.6%), and agriculture and food processing waste (2.0%). To help users understand the waste numbers in tonnes behind these percentages, the app provides additional tables for all categories.

Panel 1. Contribution of different basic waste classes (top) and detailed EWC classes (bottom) as percentages of the total waste received by waste management facilities in England.

 

A comparison of the three basic waste categories shows not all UK regions handle comparable waste numbers. Essex handled the largest amount of Inert/Construction and Demolition Waste. Great Manchester and Meyerside received more Household/Industrial/Commercial Waste (Panel 2). Tees Valley Unitary Authorities and West Midlands Met Districts processed the largest numbers for hazardous waste.

Panel 2. Household/Industrial/Commercial waste in tonnes received by different regions.

 

Waste Disposal Choices

In addition to location differences, waste disposal choices differ for the 3 waste categories. Most, 53.7%, of Inert/Construction and Demolition Waste was recovered, 30.4% went to Landfill and 10.1% was transferred for recovery or disposal (combined: 94% of all Inert/Construction and Demolition Waste). In comparison a lower proportion of Household/Industrial/Commercial Waste went to Landfill (13.5%), while 13.7% required treatment prior to disposal (Panel 3). As expected, hazardous waste required the largest processing of waste prior to disposal (24.7%).

Interestingly, a significant proportion of waste is being transferred from the receiving waste management facilities for disposal or recovery, encompassing 20% of all Household/Industrial/Commercial and Hazardous Waste and 12% of Inert/ Construction and Demolition Waste.

Panel 3. Breakdown of waste disposal procedures for Household/Industrial/Commercial waste.

 

To gain a better understanding of the waste routes, information on waste origin and respective receiving waste disposal sites can be reviewed as both - general waste categories and at a more detailed level. Here visualised the amount of Household/ Industrial/Commercial waste (top) and more specifically of municipal waste (bottom) from Aberdeenshire received by various waste disposal regions (Panel 4).

Panel 4. Breakdown of waste disposal procedures for Household/Industrial/Commercial waste.

 

My app also includes a map for a more intuitive visualisation of the distances and routes waste travels from its origin to its site of disposal.

Panel 5. Visualisation of waste origin (green dot) and respective waste disposal sites (red dots) across all waste types.

 

Travels

Overall, waste travels large distances prior to disposal. Additionally, over 20% of household and hazardous waste is currently being transferred on from the receiving waste disposal site for disposal or recovery elsewhere. It would be interesting to understand if this is due to certain features of a specific waste subgroup and could be identified at the site of origin, due to capacities of waste disposal sites, or other factors.

Please feel free to go to my shiny app to explore UK waste information details or locations of your interest.

 

Future Directions 

I would like to integrate waste data information from the NRW, SEPA and the Northern Ireland Environment Agency (NIEA) to gain a view on the UK-wide waste management. To obtain an understanding of how waste management has changed over the years, waste information from previous years for which information is provided by the UK government could be included. Enriching the data underlying my Shiny app with information on capacity and possible specialisation of UK waste disposal sites could provide useful insights for future waste management decisions.

About Author

Bettina Meier

Bettina Meier is a NYC Data Science Fellow with a PhD in biochemistry/molecular biology and experience in Cancer research, Genetics, and NGS data analysis.
View all posts by Bettina Meier >

Leave a Comment

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

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 ChatGPT 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 football 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 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

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application