This notebook demonstrates how to open and plot data using rioxarray, stackstac and matplotlib.
Author: Aimee Barciauskas
Lasted Updated Date: November 14, 2022
About the data
CDC’s Social Vulnerability Index (SVI) uses 15 variables at the census tract level. The data comes from the U.S. decennial census for the years 2000 & 2010, and the American Community Survey (ACS) for the years 2014, 2016, and 2018. It is a hierarchical additive index (Tate, 2013), with the component elements of CDC’s SVI including the following for 4 themes: Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation.
SVI indicates the relative vulnerability of every U.S. Census tract–subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.
Scientific research
The SVI Overall Score provides the overall, summed social vulnerability score for a given tract. The Overall Score SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)¹.
The Overall SVI Score describes the vulnerability in a given county tract based on the combined percentile ranking of the four SVI scores (Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation). The summed percentile ranking from the four themes is ordered, and then used to calculate an overall percentile ranking, ranging from 0 (less vulnerable) to 1 (more vulnerable)². Tracts with higher Overall SVI Scores typically rank high in other SVI domains, and reveal communities that may require extra support, resources, and preventative care in order to better prepare for and manage emergency situations.
Interpreting the data
The Overall SVI Score describes the vulnerability in a given county tract based on the combined percentile ranking of the four SVI scores (Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation). The summed percentile ranking from the four themes is ordered, and then used to calculate an overall percentile ranking, ranging from 0 (less vulnerable) to 1 (more vulnerable)². Tracts with higher Overall SVI Scores typically rank high in other SVI domains, and reveal communities that may require extra support, resources, and preventative care in order to better prepare for and manage emergency situations.
Credits
Center for International Earth Science Information Network, (CIESIN), Columbia University. 2021. Documentation for the U.S. Social Vulnerability Index Grids. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/fjr9-a973. Accessed 13 May 2022.
Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index Database. https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2018Documentation_01192022_1.pdf
If you are running this notebook on a VEDA Notebook Hub, data access should already be configured.
Outside the VEDA Hubs
The data is in a protected bucket. Please request access by emailng aimee@developmentseed.org or alexandra@developmentseed.org and providing your affiliation, interest in or expected use of the dataset. The team will help you configure the cognito client.
You should then run:
%run -i 'cognito_login.py'
Declare your collection of interest
You can discover available collections the following ways:
Use the {STAC_API_URL}/collections API endpoint (JSON response)
Programmatically using pystac (see example in the list-collections.ipynb notebook
In the STAC Browser: http://delta-staging-stac-browser.s3-website-us-east-1.amazonaws.com/
Social Vulnerability Index (Overall)
This notebook demonstrates how to open and plot data using
rioxarray,stackstacandmatplotlib.About the data
CDC’s Social Vulnerability Index (SVI) uses 15 variables at the census tract level. The data comes from the U.S. decennial census for the years 2000 & 2010, and the American Community Survey (ACS) for the years 2014, 2016, and 2018. It is a hierarchical additive index (Tate, 2013), with the component elements of CDC’s SVI including the following for 4 themes: Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation.
SVI indicates the relative vulnerability of every U.S. Census tract–subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.
Scientific research
The SVI Overall Score provides the overall, summed social vulnerability score for a given tract. The Overall Score SVI Grid is part of the U.S. Census Grids collection, and displays the Center for Disease Control & Prevention (CDC) SVI score. Funding for the final development, processing and dissemination of this data set by the Socioeconomic Data and Applications Center (SEDAC) was provided under the U.S. National Aeronautics and Space Administration (NASA)¹.
The Overall SVI Score describes the vulnerability in a given county tract based on the combined percentile ranking of the four SVI scores (Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation). The summed percentile ranking from the four themes is ordered, and then used to calculate an overall percentile ranking, ranging from 0 (less vulnerable) to 1 (more vulnerable)². Tracts with higher Overall SVI Scores typically rank high in other SVI domains, and reveal communities that may require extra support, resources, and preventative care in order to better prepare for and manage emergency situations.
Interpreting the data
The Overall SVI Score describes the vulnerability in a given county tract based on the combined percentile ranking of the four SVI scores (Socioeconomic Status, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation). The summed percentile ranking from the four themes is ordered, and then used to calculate an overall percentile ranking, ranging from 0 (less vulnerable) to 1 (more vulnerable)². Tracts with higher Overall SVI Scores typically rank high in other SVI domains, and reveal communities that may require extra support, resources, and preventative care in order to better prepare for and manage emergency situations.
Credits
Center for International Earth Science Information Network, (CIESIN), Columbia University. 2021. Documentation for the U.S. Social Vulnerability Index Grids. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/fjr9-a973. Accessed 13 May 2022.
Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index Database. https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2018Documentation_01192022_1.pdf
Accessing the data
VEDA Notebook Hub
If you are running this notebook on a VEDA Notebook Hub, data access should already be configured.
Outside the VEDA Hubs
The data is in a protected bucket. Please request access by emailng aimee@developmentseed.org or alexandra@developmentseed.org and providing your affiliation, interest in or expected use of the dataset. The team will help you configure the cognito client.
You should then run: