In this Web 2.0 era,various and massive tourist experiences and reviews presented on social networks have become important information for tourism research.In this paper,we apply social media to explore and study the ...In this Web 2.0 era,various and massive tourist experiences and reviews presented on social networks have become important information for tourism research.In this paper,we apply social media to explore and study the tourism industry of Bamako,Mali.Over 2000 reviewers and their comments about Bamako’s hotels and restaurants from TripAdvisor and Facebook were collected.Also,we integrate official tourism statistic data and field surveying data into the online review dataset.Data mining and statistic method are used to analyze the data for purpose of exploring the characteristics about tourism industry in Bamako.And we find that:(i)Most tourists are coming to Bamako for business purpose,and they incline to choose the hotels with better service and security condition;(ii)Comments on social media would greatly affect travelers’choice on hotels;(iii)Most travelers are satisfied about Bamako’s accommodation services.展开更多
Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as lo...Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.展开更多
基金This work was supported by the National Key R&D Program of China(grant number 2017YFB0503700)the National Nature Science Foundation of China(grant number 41501439).
文摘In this Web 2.0 era,various and massive tourist experiences and reviews presented on social networks have become important information for tourism research.In this paper,we apply social media to explore and study the tourism industry of Bamako,Mali.Over 2000 reviewers and their comments about Bamako’s hotels and restaurants from TripAdvisor and Facebook were collected.Also,we integrate official tourism statistic data and field surveying data into the online review dataset.Data mining and statistic method are used to analyze the data for purpose of exploring the characteristics about tourism industry in Bamako.And we find that:(i)Most tourists are coming to Bamako for business purpose,and they incline to choose the hotels with better service and security condition;(ii)Comments on social media would greatly affect travelers’choice on hotels;(iii)Most travelers are satisfied about Bamako’s accommodation services.
基金supported by the National Key R&D Program of China(grant number 2019YFC0507401)the National Natural Science Foundation of China(grant number 42371325).
文摘Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.
