US Census utilities for a variety of data loading and mapping purposes.
Reason this release was yanked:
Replaced with 0.4.3
Project description
censusdis
Introduction
censusdis
is a package for discovering, loading, analyzing, and computing
diversity, integration, and segregation metrics
to U.S. Census demographic data.
It can be installed in any python 3.9+ virtual environment using
pip install censusdis
Modules
The modules that make up the censusdis
package are
Module | Description |
---|---|
censusdis.geography |
Code for managing geography hierarchies in which census data is organized. |
'censusdis.data` | Code for fetching data from the US Census API, including managing datasets, groups, and variable hierarchies. |
censusdis.maps |
Code for downloading map data from the US, caching it locally, and using it to render maps. |
censusdis.states |
Constants defining the US States. Used by the three other modules. |
Demonstration Notebooks
There are several demonstration notebooks avaialable to illustrate how censusdis
can
be used. They are found in the
notebook
directory of the source code.
The notebooks include
Notebook Name | Description |
---|---|
SoMa DIS Demo.ipynb | Load race and ethhicity data for two twons in Essex County, NJ and compute diversity and integration metrics. |
ACS Demo.ipynb | Load American Community Survey (ACS) data for New Jersey and plot diversity statewide at the census block group level. |
Seeing White.ipynb | Load nationwide demographic data at the county level and plot of map of the US showing the percent of the population wh identify as white only at the county level. |
Map Demo.ipynb | Demonstrate loading at plotting maps of New Jersey at different geographic granularity. |
Exploring Variables.ipynb | Load metatdata on a group of variables, visualize the tree hierarchy of variables in the group, and load data from the leaves of the tree. |
Diversity and Integration Metrics
Diversity and integration metrics from the divintseg
package are
demonstrated in some of the notebooks.
For more information on these metrics see the divintseg project.
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