Saltar al contenido principal

ETHNOS_APP

Inicio • Búsqueda • Revistas • Lista 0

Mobile Collaborative Heatmapping to Infer Self-Guided Walking Tourists’ Preferences for Geomedia

Datos Bibliográficos

ID22032150
AutoresIori Sasaki (0000-0003-0216-8096, Akita University, autor de correspondencia), Masatoshi Arikawa (0000-0003-3821-4277, Akita University), Min Lu (0000-0003-3397-3637, Akita University), Ryo Sato (0000-0002-8349-4303, Akita University)
Año2023
Volumen12
Número7
Páginas283
Fecha de publicación2023-07-15
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaISPRS International Journal of Geo-Information (JOURNAL)
Identificadores de la revistaISSN: 2220-9964 • E-ISSN: 2220-9964
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi12070283
OpenAlexW4384558108
IdiomaEN
Referencias citadas36

This paper proposes a model-less feedback system driven by tourist tracking data that are automatically collected through mobile applications to visualize the gap between geomedia recommendations and the actual routes selected by tourists. High-frequency GPS data essentially make it difficult to interpret the semantic importance of hot spots and the presence of street-level features on a density map. Our mobile collaborative framework reorganizes tourist trajectories. This processing comprises (1) extracting the location of the user-generated content (UGC) recording, (2) abstracting the locations where tourists stay, (3) discarding locations where users remain stationary, and (4) simplifying the remaining points of location. Then, our heatmapping system visualizes heatmaps for hot streets, UGC-oriented hot spots, and indoor-oriented hot spots. According to our experimental study, this method can generate a trajectory that is more adaptable for hot street visualization than the raw trajectory and a simplified trajectory according to its geometry. This paper extends our previous work at the 2022 IEEE International Conference on Big Data, providing deeper discussions on application for local tourism. The framework allows us to derive insights for the development of guide content from mobile sensor data

Data mining · Geography · Global Positioning System · Information retrieval · Mobile device · Real-time computing · Telecommunications · Tourism · Trajectory · Visualization · World Wide Web · Computer Science · Data Management and Algorithms · Human Mobility and Location-Based Analysis · Video Surveillance and Tracking Methods

  • Trajectory Data Mining

    Open Access•Yu Zheng•ACM Transactions on Intelligent…•2015

  • Articulated Trajectory Mapping for Reviewing Walking Tours

    Open Access•Iori Sasaki, Masatoshi Arikawa et al.•ISPRS International Journal of…•2020

  • Clustering Methods Based on Stay Points and Grid Density for Hotspot Detection

    Open Access•Xiaohan Wang, Zepei Zhang et al.•ISPRS International Journal of…•2022

  • An Economic Development Evaluation Based on the OpenStreetMap Road Network Density

    Open Access•Bo Liu, Yu Shi et al.•ISPRS International Journal of…•2020

  • The Geography of Social Media Data in Urban Areas

    Open Access•Álvaro Bernabeu-Bautista, Leticia Serrano-Estrada et al.•ISPRS International Journal of…•2021

  • Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering

    Open Access•Jiyeon Kim, Youngok Kang•ISPRS International Journal of…•2022

  • Defining a Model for Integrating Indoor and Outdoor Network Data to Support Seamless Navigation Applications

    Open Access•Alexis Richard C Claridades, Jiyeong Lee•ISPRS International Journal of…•2021

  • Presence and digital tourism

    Open Access•David Benyon, Aaron Quigley et al.•AI & Society•2014

  • Big data in tourism research

    Open Access•Jingjing Li, Lizhi Xu et al.•Tourism Management•2018

  • Analysing the spatial-temporal characteristics of bus travel demand using the heat map

    Open Access•Chang Yü, Zhaocheng He et al.•Journal of Transport Geography•2017

Velocidad de citaciónhistorical
Altamente citadoNo
Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae