Name
Title | ||
|---|---|---|
An ensemble method to generate high-resolution gridded population data for China from digital footprint and ancillary geospatial data |
Abstract | ||
|---|---|---|
•Combine digital footprint data to produce high-resolution population maps for China.•The mapping approach models spatial dependence and improves downscaling accuracy.•The gridded maps are able to reveal wilderness areas and light-duty roads.•The hourly maps show different day-night population density patterns in urban areas. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jag.2022.102709 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | DocType | Volume |
Dynamic population distribution,Digital footprint,Geospatial big data,Ensemble learning,Spatial dependence | Journal | 107 |
ISSN | Citations | PageRank |
1569-8432 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenna Tu | 1 | 0 | 0.34 |
| Zhang Liu | 2 | 4 | 2.79 |
| Yunyan Du | 3 | 0 | 0.34 |
| Jiawei Yi | 4 | 0 | 0.34 |
| Fuyuan Liang | 5 | 0 | 0.34 |
| Nan Wang | 6 | 0 | 0.34 |
| Jiale Qian | 7 | 0 | 0.34 |
| Sheng Huang | 8 | 0 | 0.34 |
| Huimeng Wang | 9 | 0 | 0.34 |