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Building understanding

We hope that through this guide we’ve been able to impart some helpful tips around sourcing, storing, and understanding data regardless of the project you are trying to do or the impact that you are hoping to uncover.

Through our project, we tried to better understand migration, housing, and demographic realities in the Detroit area by exploring Census data in conjunction with other data sources.
Here are a few topline trends from our exploration:
Significant correlation between income and public transportation. Zipcodes with lower median incomes have higher rates of public transportation use.
Higher median income areas have a much higher rate of mortgaged housing units.
The 10 Zipcodes with the highest unemployment rates in the city of Detroit also contain the 4th, 10th, 5th, 20th, 15th, 7th, 1st, 6th, 7th, and 12th (respectively) Highest Ranked Percent of Income Spent On Rent populations.
This data can also be used to easily identify areas in the city that are predominantly commercial areas (such as 48243). This was most distinct when observing data associated with zip code 48226, which is a zip code located in the heart of downtown Detroit. This zip code also contains 1 of 2 international bridges in the city.
If you are interested in creating analysis that queries various boundary areas (such as Census Tract, City Council District, and Zip Code) in the same visualization, we suggest creating a datatable that contains all of these fields/data relationships in order to make analysis as smooth as possible. During our exploration, we found difficulty combining Census Blocks & Census Tracts with Zip Code & City Council District associated data. This can be avoided by cleaning and appending geographic data you wish to query to all the data tables you are utilizing.
We encourage you to also look at the and to see if you notice any interesting trends when filtering by different aspects of data!

Using a combination of shapefiles, organizational data (such as surveys), and public data (such as ACS data) can be used to create powerful analyses, while data visualization expands participation and collaboration. In our experiences in the progressive data space, we often noticed that individuals who didn’t see themselves as ‘data people’ had difficulty thinking about how to combine data that they routinely collect with publicly available data in order to make their presentation and targeting richer. This project provided us with an opportunity to learn more about Data Warehousing and Visualization, while this living resource-page serves as a resource for others to learn more about the tools we explored in our 2022 Fellowship year. We hope that this can be a helpful starting place to better understand how information such as surveys can be used in combination with visualization tools to learn more about different communities and populations.

Many thanks to Re:Power. This project was produced as part of the inaugural Data X Power Movement Tech Fellowship. You can find the work of our fellowship colleagues .

Thank you to our Fellowship Mentors, Charles Douglas and Michael Ingram. Thank you to Oluwakemi Oso, Toria Boldware King, and the Re:Power team for providing us with resources, data learning labs, and an inspiring peer cohort.

Thank you!

- Usha Yeruva & Leslie Potts

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