| Title |
Integrating high-frequency data in a GIS environment for pedestrian congestion monitoring |
| ID_Doc |
43903 |
| Authors |
Ruiz-Pérez, M; Ramos, V; Alorda-Ladaria, B |
| Title |
Integrating high-frequency data in a GIS environment for pedestrian congestion monitoring |
| Year |
2023 |
| Published |
Information Processing & Management, 60, 2 |
| DOI |
10.1016/j.ipm.2022.103236 |
| Abstract |
Pedestrian congestion can negatively impact how visitors perceive tourist destinations, as well as turn local residents against the tourism industry. In this sense, urban planning and management principles derived from smart tourism can be enriched by applying suitable spatial analytical tools to monitor flows and congestion. This study puts forward a methodology integrating emerging geospatial data sources into a GIS environment to analyze pedestrian flows through the city of Palma. The information was obtained from devices' geolocation data captured by the free Wi-Fi network (Smart Wi-Fi) during 2019 in the city of Palma, Spain. In order to calibrate the method, fieldwork was undertaken to count individuals in situ; this information was then con-trasted with the raw data provided by the network. The study also assesses congestion in areas based on walkable space and the number of recorded devices. The results show different mobility patterns, which highlight several overloaded and congested areas within the city. The developed methodology demonstrates the usefulness of the method in providing support to decision-making in tourism management, as well as promoting sustainability in tourist destinations. |
| Author Keywords |
Tourism congestion; Overtourism; Wi-Fi; Geolocation; Palma; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000911300300001 |
| WoS Category |
Computer Science, Information Systems; Information Science & Library Science |
| Research Area |
Computer Science; Information Science & Library Science |
| PDF |
https://doi.org/10.1016/j.ipm.2022.103236
|