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Predicting the temporal activity patterns of new venues

D'Silva, K; Noulas, A; Musolesi, M; Mascolo, C; Sklar, M; (2018) Predicting the temporal activity patterns of new venues. EPJ Data Science , 7 , Article 13. 10.1140/epjds/s13688-018-0142-z. Green open access

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Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners.

Type: Article
Title: Predicting the temporal activity patterns of new venues
Open access status: An open access version is available from UCL Discovery
DOI: 10.1140/epjds/s13688-018-0142-z
Publisher version: https://doi.org/10.1140/epjds/s13688-018-0142-z
Language: English
Additional information: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Science & Technology, Social Sciences, Physical Sciences, Mathematics, Interdisciplinary Applications, Social Sciences, Mathematical Methods, Mathematics, Mathematical Methods In Social Sciences, Human Mobility Prediction, Urban Traffic, Spatio-Temporal Patterns, Urban Computing, Cell Phones, Urban
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10050598
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