Abstract Human mobility data (MD) from smartphones may offer a scalable alternative to fieldwork for Natural Resource Damage Assessments (NRDAs) and non-market valuation. We test MD9s utility for estimating recreational demand changes after a 2019 Houston tank fire. We apply count regressions and zonal travel cost models to calculate welfare losses. While this MD dataset reflects expected temporal patterns, comparisons with reference data reveal that "coverage rates" vary significantly across sites. This measurement error complicates counterfactual predictions. Economists should use caution when deriving absolute recreational value from MD.
Duff et al. (Wed,) studied this question.