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Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions
Ghent University, Belgium.
University of Georgia, USA.
Augsburg University, Germany.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.ORCID iD: 0000-0002-2337-2486
2021 (English)In: Computers, Environment and Urban Systems, ISSN 0198-9715, E-ISSN 1873-7587, Vol. 90, article id 101712Article in journal (Refereed) Published
Abstract [en]

The growing ubiquity of location/activity sensing technologies and location-based services (LBS) has led to a large volume and variety of location-based big data (LocBigData), such as location tracking or sensing data, social media data, and crowdsourced geographic information. The increasing availability of such LocBigData has created unprecedented opportunities for research on urban systems and human environments in general. In this article, we first review the common types of LocBigData: mobile phone network data, GPS data, Location-based social media data, LBS usage/log data, smart card travel data, beacon log data (WiFi or Bluetooth), and camera imagery data. Secondly, we describe the opportunities fueled by LocBigData for the realization of smart cities, mainly via answering questions ranging from “what happened” and “why did it happen” to “what's likely to happen in the future” and “what to do next”. Thirdly, pitfalls of dealing with LocBigData are summarized, such as high volume/velocity/variety; non-random sampling; messy and not clean data; and correlations rather than causal relationships. Finally, we review the state-of-the-art research trends in this field, and conclude the article with a list of open research challenges and a research agenda for LocBigData research to help achieve the vision of smart and sustainable cities.

Place, publisher, year, edition, pages
Elsevier , 2021. Vol. 90, article id 101712
Keywords [en]
Location-based big data (LocBigData), Smart cities, Data analytics, State-of-the-art review, Research agenda, Geodata
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hig:diva-37050DOI: 10.1016/j.compenvurbsys.2021.101712ISI: 000702393800016Scopus ID: 2-s2.0-85114783412OAI: oai:DiVA.org:hig-37050DiVA, id: diva2:1597004
Available from: 2021-09-23 Created: 2021-09-23 Last updated: 2021-10-15Bibliographically approved

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