Cluster analysis of microscopic spatio-temporal patterns of tourists’ movement behaviors in mountainous scenic areas using open GPS-trajectory data

Liu, Wenbao; Wang, Bingxue; Yang, Yang; Mou, Naixia; Zheng, Yunhao; Zhang, Lingxian; Yang, Tengfei

doi:10.1016/j.tourman.2022.104614

Cited by 37 publications

(21 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More tourist amounts and dwell time mean greater tourist exposure. According to previous studies, tourist amounts can be obtained via the sampling survey, GPS tracking survey ( 32 ), Weibo data mining ( 33 ). Dwell time of tourists in assess units can be roughly graded into different scales through evaluating the landscape value and environmental capacity of tourism resources, or be precisely calculated by using an Agent-Based Model (ABM) ( 34 ).…”

Section: Methodsmentioning

confidence: 99%

Tourist risk assessment of pollen allergy in tourism attractions: A case study in the Summer Palace, Beijing, China

Zhou

Dai

Liu

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

Pollen allergy has already been an increasingly prominent ecosystem disservice in tourism attractions. However, few studies have assessed the tourist risk of pollen allergy through integrating multidisciplinary knowledge of ecology, medicine, phenology, and risk management. Basing on the conceptual framework of risk assessment proposed by UNISDR, we first established an index system of pollen-allergy risk for tourists in attractions and outlined assessment methods 18 available indexes were put forward to cover three aspects: hazard of plant allergen, tourist vulnerability, and resilience of assessment units. Subsequently, taking the Summer Palace as the case study area, we conducted a tourist risk assessment of pollen allergy. Values of nine available indexes were obtained via ecological investigation, phenological observation, and data mining of visitors' logs on Sina Weibo. Risk levels of spring pollen allergy for tourists in different assessment units were revealed by combining the green zone allergenicity index model and three-dimensional risk assessment matrix. The results showed that: (1) There were seven primary pollen-allergenic plants in the Summer Palace, including Platycladus orientalis, Sabina chinensis, Salix babylonica, Pinus tabulaeformis, Populus tomentosa Carr, Morus alba L. and Fraxinus chinesis, among which Platycladus orientalis and Salix babylonica were the highest allergenic. (2) Among 18 spots, tourists faced the highest risk level of pollen allergy in spring at three spots, namely the Hall of Serenity, Hall of Benevolence and Longevity, and Gallery of Literary and Prosperity. (3) The two routes of the Long Corridor and Longevity Hill scored high on the risk level. (4) Among four areas, risk levels of the Front-hill and Rear-hill areas were high. Given the increasing spatial-temporal uncertainty of pollen allergy and tourist behaviors under global warming and urbanization, the related monitoring should be strengthened in the future. Furthermore, the dynamic and improved assessment of pollen-allergy risk should be institutionalized and be integrated into the evaluation of tourism experience quality. Tourism administration should make full use of relevant assessment results and conduct more effective risk communication.

show abstract

Section: Methodsmentioning

confidence: 99%

Tourist risk assessment of pollen allergy in tourism attractions: A case study in the Summer Palace, Beijing, China

Zhou

Dai

Liu

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…For the former, current studies are mainly based on GPS data and other information. Liu et al [13] utilized GPS trajectory data to examine micro spatial-temporal movement characteristics of tourists at China's Mount Hua, subsequently employed Markov chains and clustering analysis to categorize tourists, and then illustrated their spatial-temporal travel behaviours. By integrating GPS tracking devices, questionnaires, and the kmeans clustering algorithm, the study divided tourists into three distinct travel patterns [14].…”

Section: The State-of-the-art Of Investigation Into Travel Characteri...mentioning

confidence: 99%

Coupling travel characteristics identifying and deep learning for demand forecasting on car‐hailing tourists: A case study of Beijing, China

Liu,

Wang

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Online car‐hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car‐hailing tourists is relatively lacking at this stage. Based on multi‐source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short‐term memory–convolutional neural network (BO‐LSTM‐CNN) method to conduct multi‐task online car‐hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO‐LSTM‐CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi‐source information related to weather, space, and online car‐hailing attributes. Extracting online car‐hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car‐hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel.

show abstract

“…Schmücker and Reif (2022) collected data for three holiday destinations in Germany, passive mobile data and passive global positioning systems (GPS) data are compared with reference data from the destinations for twelve weeks in the summer of 2019. Liu et al (2022) conducted research on destination planning, marketing, and resource management. This study uses open GPS-trajectory data to analyze the microscopic spatio-temporal patterns of tourists' movement behaviors in Mount Huashan in China.…”

Section: Introductionmentioning

confidence: 99%

Cartography for Sustainable Tourism of Cultural Tourism Attractions Around Sawaswareesrimaram Temple, Dusit District, Bangkok

WAIYASUSRI,

KULPANICH,

WORACHAIRUNGREUNG

et al. 2023

GTG

View full text Add to dashboard Cite

This research was conducted to develop tourist attractions, especially cultural tourism attractions in the community area around Sawaswareesrimaram Temple, Dusit district, Bangkok. The objective is to develop spatial potential by applying geographic information systems for mapping local communities in location determination, planning, and spatial development. The methodology was obtained from field surveys by recording the coordinates of cultural tourist attractions with Global Positioning System (GPS) and using geographic information systems for mapping. It also adopts the principle of analyzing the density of attractions with Kernel Density. The results reveal that there were 4 types of cultural attractions in Dusit District: Institution, Palace and Museum, Religious places, and Restaurant – Street food. The distribution pattern of most cultural attractions was in the south-western area of the study area, with the density of cultural attractions greater than 1 km2. Cultural attractions in the form of Restaurant and Street food were found to have a very high density in the Sri Yan area, showing a level greater than 1 km2 along Sam Sen road and Nakhon Chai Si road. As for Thewet district, there was a density level of 0.2-0.6 km2, which were all distributions of street food, dense at the intersection of roads. In summary, the mapping can provide local people with a tool for planning cultural sites, to enhance their livelihoods, love and cherish their homeland, and increase their income, leading to future tourism developments.

show abstract

Cluster analysis of microscopic spatio-temporal patterns of tourists’ movement behaviors in mountainous scenic areas using open GPS-trajectory data

Cited by 37 publications

References 60 publications

Tourist risk assessment of pollen allergy in tourism attractions: A case study in the Summer Palace, Beijing, China

Tourist risk assessment of pollen allergy in tourism attractions: A case study in the Summer Palace, Beijing, China

Coupling travel characteristics identifying and deep learning for demand forecasting on car‐hailing tourists: A case study of Beijing, China

Cartography for Sustainable Tourism of Cultural Tourism Attractions Around Sawaswareesrimaram Temple, Dusit District, Bangkok

Contact Info

Product

Resources

About