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CityPulse: A Platform Prototype for Smart City Social Data Mining

Author

Listed:
  • Maria Giatsoglou

    (Aristotle University of Thessaloniki)

  • Despoina Chatzakou

    (Aristotle University of Thessaloniki)

  • Vasiliki Gkatziaki

    (Aristotle University of Thessaloniki)

  • Athena Vakali

    (Aristotle University of Thessaloniki)

  • Leonidas Anthopoulos

    (Department of Business Administration, TEI of Thessaly)

Abstract

Cities experience radical shifts from conventional areas of fragmented services and interactions, to whole-of-service and end-to-end providers, while their citizens are empowered primarily via social networking applications with geotagging capabilities. This work is motivated by the fact that the exploitation of a (smart) city’s social networking and collective awareness can lead to improvements in the citizens’ daily life and assist city’s crowd-wise policy and decision making. This challenging objective requires appropriate platforms which will not only offer analytics of the city’s social networking data threads but also aggregation and visualization of these data for revealing and highlighting latent information in terms of the city’s emerging topics and trends. The proposed CityPulse is a modular platform for offering smart city services based on social data analysis in the context of a city. CityPulse is based on the main principle that a carefully designed backend system supports appropriate data storage, aggregation and analysis methodologies, while the derived results are exposed through Web service interfaces to ensure interoperability with various smart city applications that serve the needs of various city stakeholders. Here, we indicatively describe a generic mobile front end interface that demonstrates the functionalities that can be implemented based on CityPulse results derived by geolocated social data mining. We also demonstrate the results of CityPulse’s application on an representative smart city case study which indicate that it can effectively capture and summarize social media user activities within the city and deliver useful latent information to interested city communities in an comprehensive, flexible manner.

Suggested Citation

  • Maria Giatsoglou & Despoina Chatzakou & Vasiliki Gkatziaki & Athena Vakali & Leonidas Anthopoulos, 2016. "CityPulse: A Platform Prototype for Smart City Social Data Mining," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 7(2), pages 344-372, June.
    • Handle: RePEc:spr:jknowl:v:7:y:2016:i:2:d:10.1007_s13132-016-0370-z
    DOI: 10.1007/s13132-016-0370-z
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    References listed on IDEAS

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    1. Robert G. Hollands, 2008. "Will the real smart city please stand up?," City, Taylor & Francis Journals, vol. 12(3), pages 303-320, December.
    2. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Kummitha, Rama Krishna Reddy, 2019. "Smart cities and entrepreneurship: An agenda for future research," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    2. Olszewski, Robert & Wendland, Agnieszka, 2021. "Digital Agora – Knowledge acquisition from spatial databases, geoinformation society VGI and social media data," Land Use Policy, Elsevier, vol. 109(C).
    3. Higinio Mora & Raquel Pérez-delHoyo & José F. Paredes-Pérez & Rafael A. Mollá-Sirvent, 2018. "Analysis of Social Networking Service Data for Smart Urban Planning," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    4. Minda Hu & Mayank Kejriwal, 2022. "Measuring spatio-textual affinities in twitter between two urban metropolises," Journal of Computational Social Science, Springer, vol. 5(1), pages 227-252, May.

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