Name
Abstract | ||
|---|---|---|
Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model's prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1080/13658816.2018.1460753 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE |
Keywords | Field | DocType |
Urban population, fine-grained prediction, mobile phone data, Shanghai | Data mining,Population,Computer science,Location data,Artificial intelligence,Mobile phone,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 9 | 1365-8816 |
Citations | PageRank | References |
2 | 0.38 | 8 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Chen | 1 | 6 | 2.14 |
| Tao Pei | 2 | 2 | 1.06 |
| Shih-Lung Shaw | 3 | 341 | 23.87 |
| Lu Feng | 4 | 27 | 5.47 |
| Mingxiao Li | 5 | 17 | 3.48 |
| Shifen Cheng | 6 | 7 | 1.12 |
| Xiliang Liu | 7 | 166 | 13.32 |
| Hengcai Zhang | 8 | 11 | 2.06 |