Harnessing the Potential of the American Community Survey: Delving into Methods of Data Delivery
Eve Ahearn (Enigma Technologies Inc.), Olga Ianiuk (Enigma, Technologies Inc.)

TL;DR
This paper examines the limitations of current ACS data delivery methods and presents Enigma's innovative approach that enhances data accessibility, exploration, and integration for data analysis and science.
Contribution
The paper introduces a structured, linked, and programmatically accessible data delivery method for ACS data, improving usability over traditional formats.
Findings
Enigma's approach improves data retrieval and exploration.
Metadata and technical notes are accessible via API.
The scalable ingestion process enhances reproducibility.
Abstract
The American Community Survey (ACS) is the bedrock underpinning any analysis of the US population, urban areas included. The Census Bureau delivers the ACS data in multiple formats, yet in each the raw data is difficult to export in bulk and difficult to sift through. We argue that Enigma's approach to the data delivery, such as our raw data and metadata presentation, reflects the survey's logical structure. It can be explored, interlinked, and searched; making it easier to retrieve the appropriate data applicable to a question at hand. We make the use of data more liquid via curated tables and API access; even metadata and notes from technical documentation are programmatically accessible. Additionally, we are working towards opening our scalable and reproducible ingestion process of ACS estimations. This paper details all of the ways the Census Bureau currently makes the data…
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Taxonomy
TopicsCensus and Population Estimation · Data Quality and Management · Survey Methodology and Nonresponse
