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Predicting the temporal activity patterns of new venues

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...

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Autores principales: D’Silva, Krittika, Noulas, Anastasios, Musolesi, Mirco, Mascolo, Cecilia, Sklar, Max
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448359/
https://www.ncbi.nlm.nih.gov/pubmed/31008012
http://dx.doi.org/10.1140/epjds/s13688-018-0142-z
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author D’Silva, Krittika
Noulas, Anastasios
Musolesi, Mirco
Mascolo, Cecilia
Sklar, Max
author_facet D’Silva, Krittika
Noulas, Anastasios
Musolesi, Mirco
Mascolo, Cecilia
Sklar, Max
author_sort D’Silva, Krittika
collection PubMed
description 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.
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spelling pubmed-64483592019-04-17 Predicting the temporal activity patterns of new venues D’Silva, Krittika Noulas, Anastasios Musolesi, Mirco Mascolo, Cecilia Sklar, Max EPJ Data Sci Regular Article 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. Springer Berlin Heidelberg 2018-05-18 2018 /pmc/articles/PMC6448359/ /pubmed/31008012 http://dx.doi.org/10.1140/epjds/s13688-018-0142-z Text en © The Author(s) 2018 Open Access 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.
spellingShingle Regular Article
D’Silva, Krittika
Noulas, Anastasios
Musolesi, Mirco
Mascolo, Cecilia
Sklar, Max
Predicting the temporal activity patterns of new venues
title Predicting the temporal activity patterns of new venues
title_full Predicting the temporal activity patterns of new venues
title_fullStr Predicting the temporal activity patterns of new venues
title_full_unstemmed Predicting the temporal activity patterns of new venues
title_short Predicting the temporal activity patterns of new venues
title_sort predicting the temporal activity patterns of new venues
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448359/
https://www.ncbi.nlm.nih.gov/pubmed/31008012
http://dx.doi.org/10.1140/epjds/s13688-018-0142-z
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