| 1. |
- Lu, Xin, et al.
(författare)
-
Approaching the Limit of Predictability in Human Mobility
- 2013
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 3
-
Tidskriftsartikel (refereegranskat)abstract
- In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
|
|
| 2. |
- Tatem, Andrew J., et al.
(författare)
-
Remotely measuring populations during a crisis by overlaying two data sources
- 2015
-
Ingår i: International Health. - : Elsevier / Oxford University Press (OUP): Policy B - Oxford Open Option B. - 1876-3413 .- 1876-3405. ; 7:2, s. 90-98
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods. Methods: We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales. Results: The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed,wewere able to obtain measurements on long- and shortterm dynamic elements of populations at different points throughout the crisis. Conclusions: Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.
|
|
