Using Foursquare data to reveal spatial and temporal patterns in London
Year: 2019
Type: conference-paper
Abstract: Understanding the functional structure of contemporary cities \ncan find many applications ranging from urban planning to local governance regarding inequality issues. This exploration \nis often constrained by the availability of large-scale data at \nfine spatio-temporal resolution. Longitudinal data, such as \nthe Foursquare data, allow researchers to explore different \naspects of the functional structure of cities, such as the role \nof places and the human mobility that occurs between them. \nHowever, never mind their size, this type of data is often incomplete, capturing only parts of the functional city due to \nthe biases in the data collection that might represent a part of \nthe population only. \nIn this paper we explore how Foursquare data can help unveil \naspects of the functional structure of London. We explore the \nspatial pattern of venue locations and visit patterns captured \nin the Foursquare data. We also touch upon the temporal patterns of these data, as well as the interactions between categories of places (as derived by the semantic information that \ncomes with the venues). Our findings reveal some of the characteristics and limitations of the Foursquare data with regard \nto investigating the functional structure of London and other \ncities.
Source: HAL
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2972373571