Exploring the Differences Between Tourists and Locals in Urban Settings Through Multi-labeled Geotagged Photos: The Case of Tokyo

Understanding the behaviors of both locals and tourists is essential for good city planning, especially in tourism-dependent cities. This study aimed to explore the disparities between the two groups on the basis of their geotagged photos taken in Tokyo during the last decade (2009–2019). The photos were collected from the photosharing platform Flickr. Locals and tourists were then identified. Next, a transfer-learning-based convolutional neural network model was developed to multi-label photos into eight general categories reflecting the major frequented activities/locations, including nature, amusement, and culture. Additional information was assigned to these records, including distances to various nearest points of interest. Qualitative and quantitative methods were used to investigate the differences between locals and tourists. Results showed that tourists have a strong preference for amusement while locals are attracted to nature. In contrast to tourists who are not followed by job obligations, locals’ photos are mostly taken during the weekends. Given their familiarity with the area, locals tend to cover a wider spatial extent compared to tourists who are concentrated near the Yamanote railway loop line connecting most of the tourist attractions.

A New Approach to Mapping Cultural Ecosystem Services

According to various sources, Southern Morocco has stood out as an outstanding tourist destination in recent decades, with global appeal. Dakhla City, including Dakhla Bay, classified by the Convention on Wetlands in 2005 as a Wetland of International Importance, offers visitors various entertainment opportunities at many city sites. Therefore, human activity and social benefits should be considered in conjunction with the need to safeguard the ecosystems and maintain the Ecosystem Services (ES). This study aims to provide an overview of the tourism dynamics and hotspots related to cultural ecosystem services in Dakhla Bay. The landscape attributes are used along with an InVEST model to detect the distribution of preferences for the Cultural Ecosystem Services (CESs), map the hotspots, and identify the spatial correlations between features such as the landscape and visiting rate to understand which elements of nature attract people to the locations around the study area. Geotagged photos posted to the Flickr™ website between 2005 and 2017 were used to approximate the number of tourist visits. The results showed that tourism suffered several dips in 2005–2017 and that tourist visits are currently rising. Additionally, an estimated annual tourist visit rate shows that tourism in Dakhla Bay has been growing steadily by 2%.

A Tourist Attraction Recommendation Model Fusing Spatial, Temporal, and Visual Embeddings for Flickr-Geotagged Photos

The rapid development of social media data, including geotagged photos, has benefited the research of tourism geography; additionally, tourists’ increasing demand for personalized travel has encouraged more researchers to pay attention to tourism recommendation models. However, few studies have comprehensively considered the content and contextual information that may influence the recommendation accuracy, especially tourist attractions’ visual content due to redundant and noisy geotagged photos; therefore, we propose a tourist attraction recommendation model for Flickr-geotagged photos which fuses spatial, temporal, and visual embeddings (STVE). After spatial clustering and extracting visual embeddings of tourist attractions’ representative images, the spatial and temporal embeddings are modeled with the Word2Vec negative sampling strategy, and the visual embeddings are fused with Matrix Factorization and Bayesian Personalized Ranking. The combination of these two parts comprises our proposed STVE model. The experimental results demonstrate that our STVE model outperforms other baseline models. We also analyzed the parameter sensitivity and component performance to prove the performance superiority of our model.

Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data

Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.

Using Flickr Geotagged Photos to Estimate Visitor Trajectories in World Heritage Cities

World tourism dynamics are in constant change, as well as they are deeply shaping the trajectories of cities. The “call effect” for having the World Heritage status has boosted tourism in many cities. The large number of visitors and the side effects, such as the overcrowding of central spaces, are arousing the need to develop and protect heritage assets. Hence, the analysis of tourist spatial behaviour is critical for tackling the needs of touristified cities correctly. In this article, individual visitor spatiotemporal trajectories are reconstructed along with the urban network using thousands of geotagged Flickr photos taken by visitors in the historic centre of the World Heritage City of Toledo (Spain). A process of trajectory reconstruction using advanced GIS techniques has been implemented. The spatial behaviour has been used to classify the tourist sites offered on the city’s official tourist map, as well as to identify the association with the land uses. Results bring new knowledge to understand visitor spatial behaviour and new visions about the influence of the urban environment and its uses on the visitor spatial behaviour. Our findings illustrate how tourist attractions and the location of mixed commercial and recreational uses shape the visitor spatial behaviour. Overflowed streets and shadow areas underexplored by visitors are pinpointed.