Density-Based Spatial Clustering and Ordering Points Approach for Characterizations of Tourist Behaviour
暂无分享,去创建一个
Daniel Ochoa | Sidharta Gautama | Ivana Semanjski | Casper Van Gheluwe | Jorge Rodríguez-Echeverría | Harm IJben | S. Gautama | Daniel Ochoa | I. Šemanjski | Jorge Rodríguez-Echeverría | Harm IJben
[1] Isabelle Frochot,et al. A benefit segmentation of tourists in rural areas: a Scottish perspective , 2005 .
[2] Nico Van de Weghe,et al. Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data , 2018, Sensors.
[3] Anil K. Jain,et al. Validity studies in clustering methodologies , 1979, Pattern Recognit..
[4] Rein Ahas,et al. Evaluating passive mobile positioning data for tourism surveys: An Estonian case study , 2008 .
[5] Giles M. Foody,et al. Crowdsourced geospatial data quality: challenges and future directions , 2019, Int. J. Geogr. Inf. Sci..
[6] Peter H. Verburg,et al. Crowdsourcing geo-information on landscape perceptions and preferences: A review , 2019, Landscape and Urban Planning.
[7] Filip Biljecki,et al. Transportation mode-based segmentation and classification of movement trajectories , 2013, Int. J. Geogr. Inf. Sci..
[8] Albert-László Barabási,et al. Understanding individual human mobility patterns , 2008, Nature.
[9] Keith C. Clarke,et al. Big Spatiotemporal Data Analytics: a research and innovation frontier , 2019, Int. J. Geogr. Inf. Sci..
[10] Apichon Witayangkurn,et al. Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest , 2019, Sustainability.
[11] Avory Bryant,et al. RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates , 2018, IEEE Transactions on Knowledge and Data Engineering.
[12] Hans-Peter Kriegel,et al. DBSCAN Revisited, Revisited , 2017, ACM Trans. Database Syst..
[13] Krzysztof Janowicz,et al. Extracting and understanding urban areas of interest using geotagged photos , 2015, Comput. Environ. Urban Syst..
[14] Jie Gao,et al. Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data , 2019, Sensors.
[15] Bo Hu,et al. Segmentation by craft selection criteria and shopping involvement. , 2007 .
[16] Frank Witlox,et al. Integrating Big Data into a Sustainable Mobility Policy 2.0 Planning Support System , 2016 .
[17] Beibei Li,et al. Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced Content , 2011, Mark. Sci..
[18] Thomas Spangenberg,et al. Development of a mobile toolkit to support research on human mobility behavior using GPS trajectories , 2014, J. Inf. Technol. Tour..
[19] Huy Quan Vu,et al. Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos. , 2015 .
[20] Derya Birant,et al. ST-DBSCAN: An algorithm for clustering spatial-temporal data , 2007, Data Knowl. Eng..
[21] Simone Leao,et al. Validating crowdsourced bicycling mobility data for supporting city planning , 2019 .
[22] Yihong Yuan,et al. Evaluating gender representativeness of location-based social media: a case study of Weibo , 2018, Ann. GIS.
[23] Catherine T. Lawson,et al. A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..
[24] Sharyn Rundle-Thiele,et al. Segmentation: A tourism stakeholder view , 2009 .
[25] P. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .
[26] Ingrid Moerman,et al. Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium , 2014 .
[27] Jonathan Z. Bloom,et al. MARKET SEGMENTATION: A Neural Network Application , 2005 .
[28] J. Freese,et al. Comparing data characteristics and results of an online factorial survey between a population-based and a crowdsource-recruited sample , 2014 .
[29] Ulrike Gretzel,et al. Tracking tourists’ travel with smartphone-based GPS technology: a methodological discussion , 2017, J. Inf. Technol. Tour..
[30] Yunhao Liu,et al. Human Mobility Enhances Global Positioning Accuracy for Mobile Phone Localization , 2015, IEEE Transactions on Parallel and Distributed Systems.
[31] R. Ahas,et al. Seasonal tourism spaces in Estonia: Case study with mobile positioning data , 2007 .
[32] Cao Jing,et al. Approaches for scaling DBSCAN algorithm to large spatial databases , 2000 .