Harnessing the Potential of the American Community Survey: Delving into Methods of Data Delivery

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 available, the barriers each of these raise to applying this data in analysis and how our approach overcomes them. Finally, this paper will address other recent innovations in making Census datasets more usable, the use cases suited to each and how they fit into the wider application of data science.