Predicting the composition of solid waste at the county scale

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., , # 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 ca...,