BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data

arXiv:2402.11318v1 Announce Type: new
Abstract: While extremely useful (e.g., for COVID-19 forecasting and policy-making, urban mobility analysis and marketing, and obtaining business insights), location data collected from mobile devices often contain data from a biased population subset, with some communities over or underrepresented in the collected datasets. As a result, aggregate statistics calculated from such datasets (as is done by various companies including Safegraph, Google, and Facebook), while ignoring the bias, leads to an inaccurate representation of population statistics. Such …

abstract analysis arxiv business business insights communities covid covid-19 cs.cy cs.db cs.lg data devices forecasting insights location making marketing mobile mobile devices mobility policy population statistics type urban