DOI: 10.5061/dryad.k3j9kd5kq
title: Data from: Predicting the composition of solid waste at the county scale

description: This repository contains compiled data from published waste
 characterization studies with intent for training waste composition
 prediction models. The data is first used in a study published in the
 journal Waste Management titled "Predicting the composition of solid
 waste at the county scale". The data available is contained in
 reports that we refer to as waste characterization studies; these reports
 are often titled similarly. The data is pulled from these reports, where
 each data point is an estimated percentage breakdown of the waste stream
 into various material categories for a particular geographic area and
 year. Some reports provide multiple data points, such as data for each
 county within a state. The project team manually extracted this data from
 the publicly available report PDFs. Since different reports often use
 unique sets of material categories, the team translated the data into a
 standardized characterization. This process is detailed in the
 aforementioned paper.
description: # Data from: Predicting the composition of solid waste at the county scale
 Dataset DOI: [10.5061/dryad.k3j9kd5kq](10.5061/dryad.k3j9kd5kq) ### Files
 and variables #### File: Compiled\_Characterization.csv **Description:**
 The resulting data table has 58 columns: 3 description columns, 43
 detailed waste material category columns, and 12 aggregated waste category
 columns. Each row (i.e., waste characterization datapoint) contains the
 following: * Notes: The area covered by the data, often matching the
 verbiage of the original report. * YEAR: The reported year the data was
 gathered (note: this may be earlier than the year that the data was
 published). * FIPS: Comma separated list of Federal Information Processing
 System (i.e., unique identifiers of geographic areas) codes for the
 counties that are represented by the data. * Detailed Material Categories
 (Columns 4-46): Percentage of the waste stream that is made up by the
 material category represented by each column (note: materials categories
 that did not match the common categorization were left incomplete). *
 Newspaper * Corrugated Cardboard * Office * Magazine/Glossy * Paperboard *
 Mixed Paper * Other Paper * \#1 PET Bottles * \#2 HDPE Bottles * \#3-#7
 Bottles * Expanded Polystyrene * Film Plastic * Other Rigid Plastic *
 Clear Glass * Green Glass * Amber Glass * Other Glass * Steel Cans *
 Aluminum Cans * Other Ferrous * Other Non-Ferrous * Appliances/White Goods
 * Yard Waste * Wood * Food Waste * Textiles * Other Organics * Wood
 (C&D) * Drywall * Inerts * Fines/Dirt * Carpet (inc padding) * Asphalt
 Roofing * Other C&D * Vehicle Fluids * Lightbulbs *
 Pharmaceutical/Medical * Other HHW * Electronics * Batteries * Tires *
 Bulky Items/Furniture * Other Inorganics * Aggregated Material Categories
 (Columns 47-58): Combination of detailed material categories into fewer
 general material categories. * Paper * Plastic * Glass * Metal * Yard
 Waste (Major) * Food * Textiles (Major) * Other Organics (Major) * Wood
 (Major) * C&D Excluding Wood * HHW * Other **Missing Data:** All
 missing data, input as 'N/A', is from a source that reported the
 data in an aggregated manner that is not compatible with the chosen
 categories. For example, if a report includes only the aggregate
 "Glass" data, then we cannot fill in data for "Clear
 Glass" or "Green Glass". In those cases, the aggregated
 data is reported, but missing values are introduced into the more detailed
 categories. **Abbreviations:** PET - Polyethylene terephthalate (a type of
 plastic). HDPE - High-density polyethylene (a type of plastic). C&D -
 Construction and Demolition. HHW - Household Hazardous Waste. ###
 Predictor Data The data used to train and test the composition prediction
 model is publicly available. This data was compiled from three sources:
 Bureau of Economic Analysis (BEA), U.S. Census Bureau, and National Cancer
 Institute (NCI). Note: this data was not used for prediction as it is
 available. The composition prediction model described in the paper used
 transformations of this data. The data was made scale invariant (such as
 population/area to get population density and GDP/population to get
 per-capita GDP), and the natural logarithm was taken to better linearly
 correlate with the isometric log ratio transformed compositions. Details
 can be found in the paper. #### Access information * The BEA data was
 retrieved in 2024 from the interactive tables hosted on the BEA website:
 [https://www.bea.gov/itable/](https://www.bea.gov/itable/) * The U.S.
 Census Bureau data was retrieved in 2024 from the TIGERweb application:
 [https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_main.html](https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_main.html) * The NCI data was retrieved from the SEER county population data posted in 2022: [https://seer.cancer.gov/popdata/](https://seer.cancer.gov/popdata/)