Diversity Explorer

The Diversity Explorer project highlights more experimental modes of data visualization while demonstrating some of the possibilities for working with very fine-grained data about persons and households. This project, which is based on data from the 2016 American Community Survey, also enables the BRC team to consider methods for building tools that use modified datasets published by other institutions.

2018 Pilot

The Diversity Explorer team has created a prototype interface, allowing users to explore types of households in Boston by composition, spotlighting three characteristics: race, language spoken, and birthplace. For more information, read the 2018 pilot summary.

Project Team

Pedro Cruz—Faculty, Art + Design

John Wihbey—Faculty, Journalism

Alexa Gagosz—Graduate Student, Journalism

Avni Ghael—Graduate Student, Computer Science

Aashish Singh—Graduate Student, Computer Science

Project Details

Diversity Explorer focuses on the use of data visualizations to creatively represent indices of diversity within households in Boston. Building on previous work in information visualization by Pedro Cruz, this project develops experimental ways of interpreting and bringing representational meaning to micro-census data. The Diversity Explorer interactive visualization uses data from the 2016 American Community Survey to allow exploration by a number of indices of diversity, including birthplace, race, and languages spoken at home. This project also contributes to a growing topic within data analysis on how to use, modify, and re-use large datasets (a topic also addressed by the Boston 911 Data project).

During the BRC prototyping phase, Diversity Explorer worked with a sample of the 2016 ACS data to develop a visualization depicting household-level diversity within Boston households. Diversity Explorer foregoes geo-spatial representation to instead present this data at the household level. This scoping of the data allows for users/viewers to interpret data in different ways. Diversity Explorer works to meaningfully represent fine-grained data-sets, exploring relationships to larger community changes in Boston.