RECORD
From centre to centres: polycentric structures in individual mobility
Physics and Society · Submitted 18 Aug 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. | background 1.00 | 0.06 | 0.02 | asserted 0.89 |
| s1 | The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. | background 0.93 | 0.08 | 0.02 | asserted 0.95 |
| s2 | In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. | gap 0.71 | 0.76 | 0.21 | asserted 1.00 |
| s3 | We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. | contribution 0.87 | 0.95 | 0.10 | asserted 0.99 |
| s4 | With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of. | contribution 0.64 | 0.96 | 0.80 | asserted 0.99 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.
Human review
How the literature uses it
1 arXiv paper cite this in 2 sentences
What it cites
Uses the method or tool of 1 paper
-
For both datasets, the raw data of mobility traces is first put through the Infostop algorithm [ref] with parameters $r_1,r_2=30$ metres, $t_min = 10$ mins.
Infostop: Scalable stop-location detection in multi-user mobility data · Data description and pre-processing · 1.00
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.