Name
Abstract | ||
|---|---|---|
In recent years, call detail records CDRs have been widely used in human mobility research. Although CDRs are originally collected for billing purposes, the vast amount of digital footprints generated by calling and texting activities provide useful insights into population movement. However, can we fully trust CDRs given the uneven distribution of people’s phone communication activities in space and time? In this article, we investigate this issue using a mobile phone location dataset collected from over one million subscribers in Shanghai, China. It includes CDRs ~27% plus other cellphone-related logs e.g., tower pings, cellular handovers generated in a workday. We extract all CDRs into a separate dataset in order to compare human mobility patterns derived from CDRs vs. from the complete dataset. From an individual perspective, the effectiveness of CDRs in estimating three frequently used mobility indicators is evaluated. We find that CDRs tend to underestimate the total travel distance and the movement entropy, while they can provide a good estimate to the radius of gyration. In addition, we observe that the level of deviation is related to the ratio of CDRs in an individual’s trajectory. From a collective perspective, we compare the outcomes of these two datasets in terms of the distance decay effect and urban community detection. The major differences are closely related to the habit of mobile phone usage in space and time. We believe that the event-triggered nature of CDRs does introduce a certain degree of bias in human mobility research and we suggest that researchers use caution to interpret results derived from CDR data. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/13658816.2015.1137298 | International Journal of Geographical Information Science |
Keywords | Field | DocType |
Mobile phone location data, call detail records, human mobility, urban dynamics | Population,Data mining,Distance decay,Computer science,Phone,Mobile phone,Use caution | Journal |
Volume | Issue | ISSN |
30 | 9 | 1365-8816 |
Citations | PageRank | References |
15 | 0.85 | 20 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziliang Zhao | 1 | 15 | 0.85 |
| Shih-Lung Shaw | 2 | 341 | 23.87 |
| Yang Xu | 3 | 65 | 7.03 |
| Feng Lu | 4 | 15 | 0.85 |
| Jie Chen | 5 | 17 | 1.94 |
| Ling Yin | 6 | 55 | 6.05 |