ArticleRoyal Society open science2015
Modelling human mobility patterns using photographic data shared online.
Article in Royal Society open science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Challenges and Potential Opportunities of Mobile Phone Call Detail Records in Health Research: Review.JMIR mHealth and uHealth · 2018Review
- Article
- Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter.PloS one · 2018Article
- Using social media to quantify spatial and temporal dynamics of nature-based recreational activities.PloS one · 2018Article
- Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians.Royal Society open science · 2016Article
- Predicting the impact of urban flooding using open data.Royal Society open science · 2016Article
- Estimating suicide occurrence statistics using Google Trends.EPJ data science · 2016Article
- Tracking Protests Using Geotagged Flickr Photographs.PloS one · 2016Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans are inherently mobile creatures. The way we move around our environment has consequences for a wide range of problems, including the design of efficient transportation systems and the planning of urban areas. Here, we gather data about the position in space and time of about 16 000 individuals who uploaded geo-tagged images from locations within the UK to the Flickr photo-sharing website. Inspired by the theory of Lévy flights, which has previously been used to describe the statistical properties of human mobility, we design a machine learning algorithm to infer the probability of finding people in geographical locations and the probability of movement between pairs of locations. Our findings are in general agreement with official figures in the UK and on travel flows between pairs of major cities, suggesting that online data sources may be used to quantify and model large-scale human mobility patterns.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.