• Corpus ID: 253708330

The distorting lens of human mobility data

@inproceedings{Gallotti2022TheDL,
  title={The distorting lens of human mobility data},
  author={Riccardo Gallotti and David A Maniscalco and Marc Barthelemy and Manlio De Domenico},
  year={2022}
}
The description of complex human mobility patterns is at the core of many important applications ranging from urbanism and transportation to epidemics containment. Data about collective human movements, once scarce, has become widely available thanks to new sources such as Phone CDR, GPS devices, or Smartphone apps. Nevertheless, it is still common to rely on a single dataset by implicitly assuming that it is a valid instance of universal dynamics, regardless of factors such as data gathering… 

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References

SHOWING 1-10 OF 93 REFERENCES

On the Use of Human Mobility Proxies for Modeling Epidemics

Results suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations.

Biases in human mobility data impact epidemic modeling

The concept of data generation bias is introduced, a previously overlooked type of bias, which is present when the amount of data that an individual produces influences their representation in the dataset, and how biases can severely impact outcomes of dynamic processes such as epidemic simulations.

The universal visitation law of human mobility.

A simple and robust scaling law is revealed that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe and gives rise to prominent spatial clusters with an area distribution that follows Zipf's law.

Cross-Checking Different Sources of Mobility Information

A cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census, and cell phones to assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density, and the mobility patterns of individuals.

Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic

A regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020, is introduced, which may help monitor epidemic spreading dynamics, inform public health policy, and deepen the understanding of human behaviour changes under the unprecedented public health crisis.

Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

It is found that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

The scales of human mobility.

It is shown that day-to-day human mobility does indeed contain meaningful scales, corresponding to spatial 'containers' that restrict mobility behaviour, and a simple model is presented which given a person's trajectory-infers their neighbourhood, city and so on, as well as the sizes of these geographical containers.

Understanding individual human mobility patterns

The trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period is studied, finding that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity.

Human mobility in response to COVID-19 in France, Italy and UK

A framework to quantify the substantial impact of the mobility restrictions is provided and a percolation model mimicking mobility network disruption is introduced, finding that node persistence in the percolations process is significantly correlated with the economic and demographic characteristics of countries.

Uncovering the socioeconomic facets of human mobility

This work analyzes a heavily aggregated and anonymized summary of global mobility and investigates the relationships between socioeconomic status and mobility across a hundred cities in the US and Brazil, finding either a clear connection or little-to-no interdependencies.
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