After reviewing the housing stability-focused Benchmark Your Metro results showing that Metro Atlanta is the top dog among its Peer Metros on ethnoracial equity measures, Tiffani Martin wants to take a deeper look. She places a new order for a Neighborhood Deep-Dive Dataset with the following parameters:
Tiffani’s choices reflects her interest in knowing more about ethnoracial equity at the neighborhood level. Additionally, she wants to preserve only the most reliable data and wants to focus her attention on the primary variables (standard), instead of factoring in secondary variables for more context (premium). Once she is notified that her dataset is ready, Tiffani begins exploring her data, laser-focused on the relationship between neighborhood stratification and the household-level equities enjoyed by different ethnoracial groups.
First things first, the fact that Tiffani chose data quality as her preference means that some neighborhood data needed to be purged to make her wishes real. In practice, this means census tract data was purged at the framework level and the preference level. Given that Tiffani chose the Ethnoracial Equity dataset, reaching the standard for usability meant purging any census tracts with data missing for any of the main socioeconomic variables. So, in Tiffani’s case the original 1489 census tracts (CTs) dropped to 1353 CTs, translating to 90.9 percent of the base data retained. Tiffani also chose data quality over data coverage, so this meant going from 1353 CTs down to 1221 CTs. This second purge translates to the final dataset retaining 82.0 percent of the base data (1489 CTs) and 90.2 percent of the framework data (1353 CTs).
Source: US Census Bureau. 2024. "American Community Survey, Five-Year Estimates."
Tiffani’s freshly curated dataset provides her with the neighborhood data she needs, driven by her preferences. The minor downsides are having less neighborhood data to work with and a bit of incompatibility with MSA-level ethnoracial data. The graph above displays the bottom-up, neighborhoods-based perspective of how the largest ethnoracial groups are represented across Metro Atlanta. The percentages shown are a little different from the top-down, MSA-based ethnoracial percentages (see table below). However, given how minute these differences are, Tiffani is well-pleased with the trade-off.
Source: US Census Bureau. 2024. "American Community Survey, Five-Year Estimates."
Before diving into the numbers, Tiffani reminds herself about the ultimate purpose of purchasing the curated dataset and putting it to use and that is analyzing ethnoracial equity at this critical time from a sound basis, using concrete concepts and metrics. She knows that JMW Research measures socioeconomic stratification based on how a neighborhood’s statistical profile compares to the MSA-level profile. Neighborhoods with stats that are better than the MSA’s are considered advantaged neighborhoods and those with stats that are worse are considered disadvantaged neighborhoods. In addition, neighborhoods with statistics that are on par with MSA-level stats are considered middling neighborhoods. In terms of ethnoracial neighborhood types, the three central categories are majority-Black neighborhoods, majority-White neighborhoods, and neighborhoods that are neither majority-Black nor majority-White.
Next time: with the stage now set, Tiffani will start to meaningfully explore her Neighborhood Deep-Dive Dataset. She will look into how each of the ethnoracial groups are distributed across socioeconomic neighborhood types and explore the power of cross tabulations for making sense of place-based realities.