Name
Abstract | ||
|---|---|---|
This demonstration presents a noise map of New York City, based on four ubiquitous data sources: 311 complaint data, social media, road networks, and Point of Interests (POIs). The noise situation of any location in the city, consisting of a noise pollution indicator and a noise composition, is derived through a context-aware tensor decomposition approach we proposed in [5]. Our demo highlights two components: a) ranking locations based on inferred noise indicators in various settings, e.g., on weekdays (or weekends), at a time slot (or overall time), and in a noise category (or all categories); b) revealing the distribution of noises over different noise categories in a location. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2638728.2638776 | UbiComp Adjunct |
Keywords | Field | DocType |
urban noises,big data,miscellaneous,social media,urban computing | Data mining,Road networks,Telecommunications,Social media,Ranking,Computer science,Human–computer interaction,Urban computing,Noise map,Big data,Noise pollution,Tensor decomposition | Conference |
Citations | PageRank | References |
2 | 0.46 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yilun Wang | 1 | 297 | 13.03 |
| Yu Zheng | 2 | 8939 | 432.87 |
| Tong Liu | 3 | 96 | 7.23 |