Leveraging spatiotemporal big data for sustainable destination development: an interdisciplinary approach

Konstantinos Vogklis ()
Additional contact information
Konstantinos Vogklis: Ionian University

Information Technology & Tourism, 2025, vol. 27, issue 4, No 4, 1010 pages

Abstract: Abstract With the increasing digitization of the global travel landscape, the success of tourism destinations now depends on innovative, data-informed techniques able to reflect the dynamic and multidimensional nature of visitor flows. This destination development case study delves into the pivotal role played by spatiotemporal big data in enabling sustainable governance of tourism, targeting the integration of three interoperable data sources: (a) Internet of Things (IoT) sensing networks, (b) Earth Observation (EO) imagery, and (c) user-generated web sites (e.g., TripAdvisor forums and Google Maps). Each dataset is augmented with geospatial and time-based features, providing a highly resolved portrait of tourism flows, environmental dynamics, and visitor sentiment over time and space. A prototype system has been implemented for the Island of Corfu, blending these feeds into a synergistic platform designed to facilitate real-time governance, long-term planning, and sustainability monitoring. An essential innovation is the application of mixed frequency forecasting such that stakeholders can model and forecast tourism dynamics using data acquired at multiple temporal resolutions (day, week, month) without any delay (nowcasting). Constructed through extensive consultation with regional tourism authorities and stakeholders, the prototype case study illustrates how such a platform can facilitate better-informed decisions, better-invested resources, and long-term resilience. This case illustrates how interdisciplinary-based integration of websites can revolutionize travel governance by bridging multiple digital signals with implementable policy insights.

Keywords: Geospatial data; Big data; Temporal dimension; Tourism governance; Machine learning; Mixed frequency forecasting (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40558-025-00338-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infott:v:27:y:2025:i:4:d:10.1007_s40558-025-00338-y

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/40558

DOI: 10.1007/s40558-025-00338-y

Access Statistics for this article

Information Technology & Tourism is currently edited by Zheng Xiang

More articles in Information Technology & Tourism from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().