NOMAD

@article{nokey,

title = {Garden city: A synthetic dataset and sandbox environment for analysis of pre-processing algorithms for GPS human mobility data},

author = {Li, Thomas H. and Barreras, Francisco },

doi = {https://doi.org/10.48550/arXiv.2412.00913},

year  = {2024},

date = {2024-12-14},

journal = {arXiv},

abstract = {Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algorithms, which could compromise the validity of downstream results. The scarcity of "ground-truth" data makes it particularly challenging to evaluate and calibrate these algorithms. To overcome these limitations and allow for an intermediate form of validation of common processing algorithms, we propose a synthetic trajectory simulator and sandbox environment meant to replicate the features of commercial datasets that could cause errors in such algorithms, and which can be used to compare algorithm outputs with "ground-truth" synthetic trajectories and mobility diaries. Our code is open-source and is publicly available alongside tutorial notebooks and sample datasets generated with it.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling},

author = {Barreras, Francisco (Paco) and Watts, Duncan J. },

url = {https://www.nature.com/articles/s43588-024-00637-0},

doi = {https://doi.org/10.1038/s43588-024-00637-0},

year  = {2024},

date = {2024-06-19},

urldate = {2024-06-19},

journal = {Natural Computational Science},

volume = {4},

pages = {398-411},

abstract = {Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges—some related to accessing and processing these data, and some related to data quality—and propose several research directions to address them moving forward.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{milkman2022citywide,

title = {A citywide experiment testing the impact of geographically targeted, high-pay-off vaccine lotteries},

author = {Katherine L. Milkman and Linnea Gandhi and Sean F. Ellis and Heather N. Graci and Dena M. Gromet and Rayyan S. Mobarak and Alison M. Buttenheim and Angela L. Duckworth and Devin Pope and Ala Stanford and Richard Thaler and Kevin G. Volpp },

url = {https://www.nature.com/articles/s41562-022-01437-0},

doi = {10.1038/s41562-022-01437-0},

issn = {2397-3374},

year  = {2022},

date = {2022-09-01},

journal = {Nature Human Behavior},

abstract = {Lotteries have been shown to motivate behaviour change in many settings, but their value as a policy tool is relatively untested. We implemented a pre-registered, citywide experiment to test the effects of three high-pay-off, geographically targeted lotteries designed to motivate adult Philadelphians to get their COVID-19 vaccine. In each drawing, the residents of a randomly selected ‘treatment’ zip code received half the lottery prizes, boosting their chances of winning to 50×–100× those of other Philadelphians. The first treated zip code, which drew considerable media attention, may have experienced a small bump in vaccinations compared with the control zip codes: average weekly vaccinations rose by an estimated 61 per 100,000 people per week (+11%). After pooling the results from all three zip codes treated during our six-week experiment, however, we do not detect evidence of any overall benefits. Furthermore, our 95% confidence interval provides a 9% upper bound on the net benefits of treatment in our study.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}