Regionalization results for four scenarios and all MSAs.
co-ncrn_co-ncrn.github.io
co-ncrn
Overview
Abstract
# Synopsis
CO-NCRN is the University of Colorado at Boulder branch of the [NSF-Census Research Network](https://www.ncrn.info/node/university-colorado-boulder-university-tennessee). The ACS Regionalization code is one project to emerge from our work, providing an algorithm that takes in small-area American Community Survey estimates and produces optimized regions that reduce high levels of uncertainty in the data. The results are maps and tables with new geographies that maintain granularity but improve data quality, thereby enhancing researchers' ability to ensure their results are not comprised by high uncertainty.
Please visit the main page of this repository, [co-ncrn.github.io](co-ncrn.github.io) to acess information about this project and obtain regionalization results for a selection of input data applied to all metropolitan statistical areas in the United States. To use your own input variables and geographies, you can access the regionalization algorithm at [this repository](https://github.com/geoss/ACS_Regionalization).
# Results
Regionalization results are provided for four scenarios, housing, poverty, transportation, and a general scenario, and applied to 388 Metropolitan Statistical Areas as defined in 2013. The input data derives from the 2008-2012 American Community Survey.
Each result can be downloaded from the website as a zip file containing:
- A crosswalk between input tracts and output regions
- Input values for original and derived ACS variables for original tracts and computed regions
- Output values for the original and derived ACS variables for the computed regions
- Shapefiles for the input tracts and output regions
- A weights matrix for the output regions
- A data dictionary
- A metadata file
GeoJSON maps of the input tracts and output regions with associated data are displayed and available for download as well.
CO-NCRN is the University of Colorado at Boulder branch of the [NSF-Census Research Network](https://www.ncrn.info/node/university-colorado-boulder-university-tennessee). The ACS Regionalization code is one project to emerge from our work, providing an algorithm that takes in small-area American Community Survey estimates and produces optimized regions that reduce high levels of uncertainty in the data. The results are maps and tables with new geographies that maintain granularity but improve data quality, thereby enhancing researchers' ability to ensure their results are not comprised by high uncertainty.
Please visit the main page of this repository, [co-ncrn.github.io](co-ncrn.github.io) to acess information about this project and obtain regionalization results for a selection of input data applied to all metropolitan statistical areas in the United States. To use your own input variables and geographies, you can access the regionalization algorithm at [this repository](https://github.com/geoss/ACS_Regionalization).
# Results
Regionalization results are provided for four scenarios, housing, poverty, transportation, and a general scenario, and applied to 388 Metropolitan Statistical Areas as defined in 2013. The input data derives from the 2008-2012 American Community Survey.
Each result can be downloaded from the website as a zip file containing:
- A crosswalk between input tracts and output regions
- Input values for original and derived ACS variables for original tracts and computed regions
- Output values for the original and derived ACS variables for the computed regions
- Shapefiles for the input tracts and output regions
- A weights matrix for the output regions
- A data dictionary
- A metadata file
GeoJSON maps of the input tracts and output regions with associated data are displayed and available for download as well.
Authoring entity
| Agency Name | Role | Affiliation |
|---|---|---|
| co-ncrn | owner | https://co-ncrn.github.io |
Language
English
Methods, software and scripts
Software
| Name |
|---|
| HTML |
| JavaScript |
| CSS |
| Python |
License
| Name |
|---|
| GNU General Public License v3.0 |
Metadata production
Producers
| Name | Role |
|---|---|
| GitHub Bot | bot |
Date of Production
01 December 2019