Using decision-tree methodologies to explore determinants of health and wellbeing outcomes at the local authority scale: the case study of London
Jun 1, 2023
Understanding temporal and spatial patterns of urban activities across demographic groups through geotagged social media data
Distribution of distinct point-based geotags in Greater London. Highlights
Jan 17, 2023
The Road to Recovery: sensing public opinion towards reopening measures with social media data in post-lockdown cities
Daily trends of reopen discussion in London under Covid-19 Pandemic
May 15, 2021
Delineating urban functional use from points of interest data with neural network embedding: a case study in Greater London
Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. A neural network embedding model is employed in delineating urban functional use from POI (Points of Interest) data. Doc2Vec model directly trains vector representations for spatial areas while considering the spatial distribution of POIs. This paper explores the functional similarity among 574 POI classes and 4836 LSOAs (Lower Layer Super Output Areas) in Greater London. Doc2Vec model outperforms other semantic models (Word2Vec, LDA and TF-IDF) in urban functional areas identification. Similarity of POI classes trained by Doc2Vec model Create your slides in Markdown - click the Slides button to check out the example. Add the publication’s full text or supplementary notes here. You can use rich formatting such as including code, math, and images.
May 10, 2021
Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications and Methods
Crowdsourced data, such as social media data, points of interest (POIs) data, and collaborative websites, generated by the crowd, have become fine-grained proxy data of urban activity and widely used in research in urban studies. This paper conducts a literature search in the Web of Science database, selecting 226 highly related papers published between 2013 and 2019. Based on these papers, the review first conducts a bibliometric analysis identifying underpinning domains, pivot scholars, and papers around this topic.
Mar 26, 2020