This national, tract-level experienced racial segregation dataset uses data for over 66 million anonymized and opted-in devices in Cuebiq’s Spectus Clean Room data to estimate 15 minute time overlaps of device stays in 38.2m x 19.1m grids across the United States in 2022. We infer a probability distribution of racial backgrounds for each device given their home Census block groups at the time of data collection, and calculate the probability of a diverse social contact during that space and time. These measures are then aggregated to the Census tract and across the whole time period in order to preserve privacy and develop a generalizable measure of the diversity of a place. We propose that this dataset is a better measurement of the segregation and diversity as it is experienced, which we show diverges from standard measurements of segregation. The data can be used by researchers to better understand the determinants of experienced segregation; beyond research, we suggest this data can be used by policy makers to understand the impacts of policies designed to encourage social mixing and access to opportunities such as affordable housing and mixed-income housing, and more.

For the purposes of enhanced privacy, home census block groups were pre-calculated by the data provider, and all calculations are done at the Census tract, with tracts that have more than 20 unique devices over the period of analysis.

Repository for the manuscript Spatial immunization to abate disease spreading in transportation hubs By Mattia Mazzoli, Riccardo Gallotti, Filippo Privitera, Pere Colet and Jose J. Ramasco

This repository contains two python codes. One code describes an SIR epidemic model embedded in London Heathrow Airport, under the assumption of a spatial immunization scenario (sirnurlamp_gamma.py). All other models present in our work rely on the same structure. The second code represents the supersampling process that we applied to original Cuebiq data. The code describes all the necessary processes to translate trajectories in the airport perimeter to the temporal contact network used in the epidemic models.