An experienced racial-ethnic diversity dataset in the United States using human mobility data

Abstract

Abstract Despite the importance of measuring racial-ethnic segregation and diversity in the United States, current measurements are largely based on the Census and, thus, only reflect segregation and diversity as understood through residential location. This leaves out the social contexts experienced throughout the course of the day during work, leisure, errands, and other activities. The National Experienced Racial-ethnic Diversity (NERD) dataset provides estimates of diversity for the entire United States at the census tract level based on the range of place and times when people have the opportunity to come into contact with one another. Using anonymized and opted-in mobile phone location data to determine co-locations of people and their demographic backgrounds, these measurements of diversity in potential social interactions are estimated at 38.2 m × 19.1 m scale and 15-minute timeframe for a representative year and aggregated to the Census tract level for purposes of data privacy. As well, we detail some of the characteristics and limitations of the data for potential use in national, comparative studies.