skip to main content
10.1145/2976796.2976862acmotherconferencesArticle/Chapter ViewAbstractPublication PageswebmediaConference Proceedingsconference-collections
research-article

Study of Google Popularity Times Series for Commercial Establishments of Curitiba and Chicago

Published: 08 November 2016 Publication History

Abstract

Urban computing is a recent area of study that helps us to understand the nature of urban phenomena. In this sense, an important aspect to study is the dynamics of commercial establishments popularity in the city. Recently, Google launched a new service that provides popularity time series of some commercial establishments in several cities. This is a valuable source of data that allow us to better understand the dynamics of establishments popularity, helping to change our perceived physical limits about the city, which can enable the development of new applications and urban services. The results of this study are: (1) characterization of Google popularity time series for bars and restaurants in the cities of Curitiba/Brazil and Chicago/USA. Among the results, we find cultural characteristics of these cities, as well as a favorable clustering of similar venues based on the temporal pattern of popularity; (2) evaluation of reproduction of Google popularity time series using Foursquare data. In this evaluation, we found evidence that Foursquare data might be used for this purpose. This means that for places where Google does not offer this service data from Foursquare, or other source, could be used. This enables the exploration of a greater number of establishments in, for example, a new venue recommendation engine.

References

[1]
J. Cranshaw, R. Schwartz, J. I. Hong, and N. Sadeh. The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. In Proc. of ICWSM, 2012.
[2]
M. A. Domingues, T. E. Santos, R. Hanada, B. C. Cunha, S. O. Rezende, and M. d. G. C. Pimentel. A platform for the recommendation of points of interest in brazilian cities: Architecture and case study. In Proc. of WebMedia, pages 229--236, New York, NY, USA, 2015. ACM.
[3]
R. S. Ehlers. Análise de séries temporais. Universidade Federal do Paraná, 2007.
[4]
E. B. Fowlkes and C. L. Mallows. A method for comparing two hierarchical clusterings. Journal of the American statistical association, 78(383):553--569, 1983.
[5]
T.-c. Fu. A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164--181, 2011.
[6]
D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, and C. Mascolo. Geo-spotting: Mining online location-based services for optimal retail store placement. In Proc. of KDD '13, pages 793--801, Chicago, Illinois, USA, 2013. ACM.
[7]
E. J. Keogh and M. J. Pazzani. A simple dimensionality reduction technique for fast similarity search in large time series databases. In Knowledge Discovery and Data Mining., pages 122--133. Springer, 2000.
[8]
N. P. Kozievitch, L. C. Gomes-Jr, T. M. C. Gadda, K. V. O. Fonseca, and M. Akbar. Analyzing the Acoustic Urban Environment: A Geofencing-Centered Approach in the Curitiba Metropolitan Region, Brazil. In Proc. of SMARTGREENS, 2016.
[9]
V. I. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, volume 10, pages 707--710, 1966.
[10]
O. Maimon and L. Rokach. Data mining and knowledge discovery handbook, volume 2. Springer, 2005.
[11]
G. Navarro. A guided tour to approximate string matching. ACM computing surveys (CSUR), 33(1):31--88, 2001.
[12]
A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. An Empirical Study of Geographic User Activity Patterns in Foursquare. In Proc. of ICWSM'11, 2011.
[13]
T. Oates, L. Firoiu, and P. R. Cohen. Clustering time series with hidden markov models and dynamic time warping. In Proc. of IJCAI, pages 17--21. Citeseer, 1999.
[14]
L. Richardson. Beautiful soup. Crummy: The Site, 2013.
[15]
M. A. Russell. Mining the Social Web. O'Reilly Media, 2013.
[16]
T. Silva, P. Vaz de Melo, J. Almeida, M. Musolesi, and A. Loureiro. You are what you eat (and drink): Identifying cultural boundaries by analyzing food e drink habits in foursquare. In Proc. of ICWSM, Ann Arbor, USA, 2014.
[17]
T. H. Silva and A. A. Loureiro. Computaçao urbana: Técnicas para o estudo de sociedades com redes de sensoriamento participativo. In Anais da XXXIV JAI, volume 8329, pages 68--122. SBC, 2015.
[18]
T. H. Silva, P. O. S. Vaz de Melo, J. M. Almeida, J. Salles, and A. A. F. Loureiro. A picture of Instagram is worth more than a thousand words: Workload characterization and application. In Proc. of DCOSS'13, Cambridge, MA, USA, May 2013.
[19]
T. H. Silva, P. O. S. Vaz de Melo, J. M. Almeida, J. Salles, and A. A. F. Loureiro. Revealing the city that we cannot see. ACM Trans. Internet Technol., 14(4):26:1--26:23, Dec. 2014.
[20]
Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: concepts, methodologies, and applications. ACM TIST, 5(3):38, 2014.

Cited By

View all
  • (2020)Google Popular Times: towards a better understanding of tourist customer patronage behaviorTourism Review10.1108/TR-10-2018-015276:3(533-569)Online publication date: 4-Sep-2020
  • (2019)A ranking method for location-based categorical data in smart citiesProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360291(453-460)Online publication date: 29-Oct-2019
  • (2019)Extraction and Exploration of Business Categories SignaturesBig Social Data and Urban Computing10.1007/978-3-030-11238-7_6(90-104)Online publication date: 23-Jan-2019
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
Webmedia '16: Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web
November 2016
384 pages
ISBN:9781450345125
DOI:10.1145/2976796
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • FAPEPI: Fundacao de Amparo a Pesquisa do Estado do Piaui
  • SBC: Brazilian Computer Society
  • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
  • CGIBR: Comite Gestor da Internet no Brazil
  • CAPES: Brazilian Higher Education Funding Council

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 November 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. caracterização
  2. computação urbana
  3. foursquare
  4. google
  5. popularidade de locais
  6. redes sociais
  7. séries temporais

Qualifiers

  • Research-article

Funding Sources

  • CNPq
  • FAPEMIG
  • Fundação Araucária

Conference

Webmedia '16
Sponsor:
  • FAPEPI
  • SBC
  • CNPq
  • CGIBR
  • CAPES
Webmedia '16: 22nd Brazilian Symposium on Multimedia and the Web
November 8 - 11, 2016
Piauí State, Teresina, Brazil

Acceptance Rates

Webmedia '16 Paper Acceptance Rate 29 of 94 submissions, 31%;
Overall Acceptance Rate 270 of 873 submissions, 31%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)9
  • Downloads (Last 6 weeks)5
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2020)Google Popular Times: towards a better understanding of tourist customer patronage behaviorTourism Review10.1108/TR-10-2018-015276:3(533-569)Online publication date: 4-Sep-2020
  • (2019)A ranking method for location-based categorical data in smart citiesProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3360291(453-460)Online publication date: 29-Oct-2019
  • (2019)Extraction and Exploration of Business Categories SignaturesBig Social Data and Urban Computing10.1007/978-3-030-11238-7_6(90-104)Online publication date: 23-Jan-2019
  • (2018)Predicting Waiting Time in Public Service Qeues Using Participative and GPS Sensing with SmartphonesProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3264669(319-322)Online publication date: 16-Oct-2018
  • (2018)Analysis of Urban Regions Popularity Using FoursquareProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243119(379-386)Online publication date: 16-Oct-2018
  • (2017)Three Decades of Business Activity Evolution in Curitiba: A Case StudyAnnals of Data Science10.1007/s40745-017-0104-54:3(307-327)Online publication date: 23-Mar-2017

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media