Smart City Gnosys
Smart city article details
| Title | Mining Tourists’ Movement Patterns In A City |
|---|---|
| ID_Doc | 37102 |
| Authors | Elvas L.B.; Nunes M.; Afonso J.A.; Helgheim B.I.; Francisco B. |
| Year | 2024 |
| Published | Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 540 LNICST |
| DOI | http://dx.doi.org/10.1007/978-3-031-49379-9_6 |
| Abstract | Although tourists generate a large amount of data (known as “big data”) when they visit cities, little is known about their spatial behavior. One of the most significant issues that has recently gained attention is mobile phone usage and user behavior tracking. A spatial and temporal data visualization approach was established with the purpose of finding tourists’ footprints. This work provides a platform for combining multiple data sources into one and transforming information into knowledge. Using Python, we created a method to build visualization dashboards aiming to provide insights about tourists’ movements and concentrations in a city using information from mobile operators. This approach can be replicated to other smart cities with data available. Weather and major events, for instance, have an impact on the movements of tourists. The outputs from this work provide useful information for tourism professionals to understand tourists’ preferences and improve the visitors’ experience. Management authorities may also use these outputs to increase security based on tourists’ concentration and movements. A case study in Lisbon with 4 months data is presented, but the proposed approach can also be used in other cities based on data availability. Results from this case study demonstrate how tourists tend to gather around a set of parishes during a specific time of the day during the months under study, as well as how unusual circumstances, namely international events, impact their overall spatial behavior. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. |
| Author Keywords | data analytics; Internet of Things; location data; mobile phone sensing; smart cities; tourist behaviour |
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