D'Silva, K;
Noulas, A;
Musolesi, M;
Mascolo, C;
Sklar, M;
(2017)
If I build it, will they come?: Predicting new venue visitation patterns through mobility data.
In: Hoel, EG and Newsam, SD and Ravada, S and Tamassia, R and Trajcevski, G, (eds.)
Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.
(pp. 54:1-54:1).
ACM: Redondo Beach, CA, USA.
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Abstract
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as !rst rounds of sta"ng and resource allocation. Traditionally, this estimation has been performed through coarse measures such as observing numbers in local venues. The advent of crowdsourced data from devices and services has opened the door to better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from the location-based service Foursquare, 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. Our evaluation shows that temporally similar areas of a city can be valuable predictors, decreasing error by 41%. Our !ndings have the potential to impact the design of location-based technologies and decisions made by new business owners.
| Type: | Proceedings paper |
|---|---|
| Title: | If I build it, will they come?: Predicting new venue visitation patterns through mobility data. |
| Event: | SIGSPATIAL’17 |
| ISBN-13: | 978-1-4503-5490-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3139958.3140035 |
| Publisher version: | http://dl.acm.org/citation.cfm?id=3139958 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Human mobility prediction, urban traffic, spatio-temporal patterns, urban computing |
| 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/10051126 |
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