Automated Author Profile

Grindrod, Peter

University of Oxford

Current S-Index

1.2

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

1.2

Average Dataset Index per dataset

Total Datasets

1

Total datasets for this author

Average FAIR Score

13.5%

Average FAIR Score per dataset

Total Citations

3

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Data from: Comparison of social structures within cities of very different sizes (Version: 1)

People make a city, making each city as unique as the combination of its inhabitants. However, some cities are similar and some cities are inimitable. We examine the social structure of 10 different cities using Twitter data. Each city is decomposed to its communities. We show that in many cases one city can be thought of as an amalgamation of communities from another city. For example, we find the social network of Manchester is very similar to the social network of a virtual city of the same size, where the virtual city is composed of communities from the Bristol network. However, we cannot create Bristol from Manchester since Bristol contains communities with a social structure that are not present in Manchester. Some cities, such as Leeds, are outliers. That is, Leeds contains a particularly wide range of communities, meaning we cannot build a similar city from communities outside of Leeds. Comparing communities from different cities, and building virtual cities that are comparable to real cities, is a novel approach to understand social networks. This has implications when using social media to inform or advise residents of a city.

Authors

  • Grindrod, Peter ;
  • Lee, Tamsin E.
3 Citations0 Mentions13% FAIR1.5 Dataset Index
10.5061/dryad.2gf23January 2016