usio

This project provides scripts for creating an US input-output database for the use in openLCA. It creates a direct requirements matrix from the BEA make and use tables. This DR matrix is than combined with a satellite matrix and converted to a set of process dates sets in the olca-schema format which can be imported into openLCA. The satellite matrix is based on the satellite matrix of the OpenIO database but was mapped to the flows of the openLCA reference list.

Usage

In order to build the database using the scripts in this repository you need to have Python 3.x and NumPy installed (for Windows you can find NumPy binaries here: http://www.lfd.uci.edu/~gohlke/pythonlibs/). If this is the case, just download this repository and execute the make.py script:

python make.py

This will create a usio_[description].zip file in the build directory.

Data flow

The data package is created with the following steps:

1. The original BEA make and use tables are converted to a simple CSV matrix format (see the matrix format description below).

2. From these make and use tables a direct requirements matrix is calculated as described in Concepts and Methods of the U.S. Input-Output Accounts (see Chapter 12).

3. This direct requirement matrix is then combined with a satellite matrix and converted into a set of process data sets. The CSV tables of the satellite matrix and a JSON-LD package template are already prepared and stored in the data folder.

Configuration

By default the BEA make and use tables after redefinition are taken to calculate the direct requirement matrix but the script also works with the tables before redefinitions (which are also included in the data folder). The creation of the direct requirements matrix can be configured to apply scrap adjustments and to remove value added sectors or not:

# no scrap adjustments and removal of value added sectors
import iodb    
iodb.create_drc_matrix(make_csv_file, use_csv_file, dr_csv_file)

# now with scrap adjustments and removal of value added sectors
iodb.create_drc_matrix(make_csv_file, use_csv_file, drc_csv_file, 
                       scrap='Scrap', value_added=['VA1', 'VA2'])

Additionally, the different build steps are independent from each other and consume and produce simple data formats. Thus, it should be possible to update the data sources (e.g. by using the BEA 2007 statistics) without much effort.

The CSV matrix format

The data processing is based on matrices that are stored in a simple CSV file format which describes the content of a matrix with 3 columns: a row identifier (a string), a column identifier (a string), the value of the respective row and column (a number). Here is an example of how the content of such a file may look like:

"A","A",300
"A","B",25
"A","Scrap",3
"B","A",30
"B","B",360
"B","C",20
"B","Scrap",2
"C","B",15
"C","C",250

This repository contains also a small tool for comparing two matrices in this format. See the diffexample.py script in the scripts folder for how to use this tool. Additionally, there is a Excel macro for converting matrices in Excel into this matrix format in the script folder.