Data Visualization

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Using Tableau

Tableau is a visual analytics platform that is available both as a desktop application and a cloud based tool. Tableau can be used to create complex analytics and visualization projects. Tableau is a leading analytics platform.


Tableau does allow free, one-year licensing to students and accredited institutions through their . Other pricing options for Tableau .


Data Sources: Where data connections are stored, referenced, and modified.
Worksheets: A single view of analyzed data uploaded from the data source. This is where major analysis and visualization takes place.
Dashboards: A collection of views from multiple worksheets
Stories: A sequence of worksheets and dashboards presenting together to convey bundled information.

Combing Data Between Datasets (and Shapefiles):

Relating Data: Various data in Tableau can be combined as long as the datasets share a column of output data. The headers of data are not required to match, but creating common labeling in your data cleaning phase can help easily identify matching datsets. This can be used to match output data (such as ACS response data) to shapefiles using a matching output such as Census Tract, Zip Code, or City Council District. Tableau intuitively will identify relationships between data sources while also preserving the original tables’ data integrity.
Joins: Joins can be built across data tables through Tableau’s Logic Table and Relationship Canvas tool.
Please note: Published Tableau Sources cannot be used as joins
Relationships: A relationship is a query that will join multiple data tables together as long as they have a matching field. Tableau will also attempt to create relationships based on matching key constraints and matching field names.
Blends: Tableau is able to blend data by linking a common primary and secondary dimension.
Utilizing Tableau Public: Tableau Public is a free platform that allows users to view, explore, create, and share visualizations created in either Tableau Online or Tableau Desktop. .

Adding map backgrounds:

Tableau provides a range of map customization features, included pre-loaded map Background Layers. .
While Tableau does allows various fields to also be loaded as labels onto the visualization itself, map background layers can provide more detail and Geospatial references that do not overshadow your visualizations. The data in a Background Map Layer will remain visible even when overlayed data from visualizations have been filtered.
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Visualization Examples:

ACS_Cat_Interest provides a screenshot of a data table with a variety of metrics related to zip code data from the ACS source file we analyzed. :
Tableau allows for more detailed analysis of loaded data. Below is an example of metric data being shifted from raw numbers to Ranked representation, using Tableau’s built in analysis options.
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This visualization shows Median rent data distributed across zip codes within Detroit, Michigan.
This visualization shows the boundaries of Detroit City Council Districts (as of 2018).
This visualization shows ACS data reporting on those that commute via carpool, distributed by zip code within Detroit, Michigan.
This visualization assigns a distinct color code to all zip codes within Detroit City boundaries.

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