Downloads provided by UsageCounts
This paper explores new methods of anonymising and representing large human-generated datasets (here mobile phone activity data). We use a quadtree algorithm developed by Lagonigro et. al (2020) to spatially aggregate mobile in-app events and compare outputs obtained between days and against an ordnance survey grid. We aim to demonstrate potential usage of new computational tools and exemplify their potential to inform more elaborate and project-specific regionalisation methods.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 7 | |
| downloads | 7 |

Views provided by UsageCounts
Downloads provided by UsageCounts