Name
Abstract | ||
|---|---|---|
The availability of big data on human activity is currently changing the way we look at our surroundings. With the high penetration of mobile phones, nearly everyone is already carrying a high-precision sensor providing an opportunity to monitor and analyze the dynamics of human movement on unprecedented scales. In this article, we present a technique and visualization tool which uses aggregated activity measures of mobile networks to gain information about human activity shaping the structure of the cities. Based on ten months of mobile network data, activity patterns can be compared through time and space to unravel the "city's pulse" as seen through the specific signatures of different locations. Furthermore, the tool allows classifying the neighborhoods into functional clusters based on the timeline of human activity, providing valuable insights on the actual land use patterns within the city. This way, the approach and the tool provide new ways of looking at the city structure from historical perspective and potentially also in real-time based on dynamic up-to-date records of human behavior. The online tool presents results for four global cities: New York, London, Hong Kong and Los Angeles. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Globe,World Wide Web,Visualization,Computer science,Timeline,Cellular network,Big data,Land use |
DocType | Volume | Citations |
Journal | abs/1509.00459 | 0 |
PageRank | References | Authors |
0.34 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dániel Kondor | 1 | 66 | 8.74 |
| Pierrick Thebault | 2 | 5 | 1.48 |
| Sebastian Grauwin | 3 | 37 | 4.45 |
| istvan godor | 4 | 2 | 1.09 |
| simon moritz | 5 | 0 | 0.34 |
| Stanislav Sobolevsky | 6 | 464 | 32.15 |
| Carlo Ratti | 7 | 1211 | 113.38 |