Search Results (66)

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article

Personalized itinerary recommendations are essential as many people choose traveling as their primary leisure pursuit. Unlike model-based and optimization-based methods, sequential-pattern-mining-based methods, which are based on the users’ previous visiting experience, can generate more personalized itineraries and avoid the difficulties caused by the two methods. Although sequential-pattern-mining-based methods have shown promise in generating personalized itineraries, the following three challenges remain. First, they often overlook user diversity in time and category preferences, leading to less personalized itinerary suggestions. Second, they typically evaluate sequences only by POI preference, ignoring crucial factors of optimal visiting times and travel distance. Third, they tend to recommend feasible but not optimal itineraries without exploring extended combinations that could better meet user constraints. To solve the difficulties above, a novel personalized itinerary recommendation system for social media is proposed. First, the user preference, which contains time and category preferences, is generated for all users. Users with similar preferences are clustered into the same group. Then, the sequential pattern mining algorithm is adopted to create frequent sequential patterns for each group. Second, to evaluate the suitability of an itinerary, we define the itinerary score according to the considerations of the POI preference, time matching, and travel distance. Third, based on the tentative itineraries generated from the sequential pattern mining process, the Sequential-Pattern-Mining-based Itinerary Recommendation (SPM-IR) algorithm is developed to create more candidate itineraries under user-specified constraints. The top-N candidate sequences ranked by the proposed itinerary score are then returned to the target user as the itinerary recommendation. A real-life dataset from geotagged social media is implemented to demonstrate the benefits of the proposed personalized itinerary recommendation system. Empirical evaluations show that 94.82% of the generated itineraries outperformed real-life itineraries in POI preference, time matching, and travel-distance-based itinerary scores. Ablation studies confirmed the contribution of time and category preferences and highlighted the importance of time matching in itinerary evaluation. Full article

Media platforms provide an effective way to gauge public perceptions, especially during mass disruption events. This research explores public responses to the 2023 Ohio train derailment event through Twitter, currently known as X, and Google Trends. It aims to unveil public sentiments and attitudes by employing sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and topic modeling using Latent Dirichlet Allocation (LDA) on geotagged tweets across three phases of the event: impact and immediate response, investigation, and recovery. Additionally, the Self-Organizing Map (SOM) model is employed to conduct time-series clustering analysis of Google search patterns, offering a deeper understanding into the event’s spatial and temporal impact on society. The results reveal that public perceptions related to pollution in communities exhibited an inverted U-shaped curve during the initial two phases on both the Twitter and Google Search platforms. However, in the third phase, the trends diverged. While public awareness declined on Google Search, it experienced an uptick on Twitter, a shift that can be attributed to governmental responses. Furthermore, the topics of Twitter discussions underwent a transition across three phases, changing from a focus on the causes of fires and evacuation strategies in Phase 1, to river pollution and trusteeship issues in Phase 2, and finally converging on government actions and community safety in Phase 3. Overall, this study advances a multi-platform and multi-method framework to uncover the spatiotemporal dynamics of public perception during disasters, offering actionable insights for real-time, region-specific crisis management. Full article

Overcrowding in national parks and protected areas can cause irreversible damage to the environment, compromising the quality of soil, water, wildlife, and vegetation. Thus, it is critical for park managers to have detailed information on visitor activities and spatial dynamics in order to prioritise actions capable of mitigating undesirable impacts in the most frequently visited areas. In this article, we use georeferenced trails and photographs from the Wikiloc and Flickr web platforms to determine the spatial visitation patterns in the Atlantic Islands of Galicia National Park (AINP) from 2008 to 2023. Maps showing trail usage intensity and the distribution of photographs according to land use allowed us to identify the most frequented land uses by visitors and the areas of highest tourist pressure within the AINP. The results show that distribution patterns vary between platforms. Shrubland (37%) and marine cliffs (27%) were the most photographed land uses by visitors, while artificial areas (14%) were the most frequented by Wikiloc users. Cíes island emerges as the most popular tourist destination, as evidenced by the greater number of trails and photographs compared to Ons, Sálvora, and Cortegada. This study shows how social media data, specifically trails and geotagged photographs from Wikiloc and Flickr, can support and complement the monitoring of visitor use and impact in protected areas. Full article

This study explores Instagram’s influence on sensory and experiential engagement with architecture in the digital age. Using a phenomenological approach, we studied the impact of Instagram’s visual features, such as filters, geotagging, and hashtags, on user interactions and perceptions of architectural spaces. The research demonstrates that Instagram transforms traditional architectural experiences into dynamic visual narratives that integrate real and virtual elements, altering our understanding of space and time. While acknowledging that architectural experience encompasses form, function, and historical context, this paper specifically focuses on Instagram’s role in mediating perceptual experiences. By analyzing user engagement patterns and content trends, the study highlights how Instagram shapes architectural design practices and the creation of spaces tailored for digital interaction. This study offers a comprehensive view of the complex relationship between digital media and architectural perception, identifying both the opportunities and challenges presented by the platform in influencing our understanding of architectural spaces. Full article

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space. Full article

Graffiti relies on social instrumentation for its creation on spatial structures. It is questioned whether different mechanisms exist to transfer social and spatial hierarchies under a new model for better engagement, management, and governance. This research aims to replace physical graffiti using augmented reality (AR) in smartphones. Contact-free AR graffiti starts with the creation of 3D graffiti; this is followed by an AR cloud platform upload, quick response (QR) code access, and site deployment, leading to the secondary reconstruction of a field scene using smartphone screenshots. The working structure was created based on the first 3D reconstruction of graffiti details as AR models and second 3D reconstruction of field graffiti on different backgrounds using a photogrammetry method. The 3D graffiti can be geotagged as a personal map and 3D printed for collections. This culture-engaged AR creates a two-way method of interacting with spatial structures where the result is collected as a self-governed form of social media. The reinterpreted context is represented by a virtual 3D sticker or symbolized name card shared on the cloud. The hidden or social hierarchy was reinterpreted by a sense of ritual without altering any space. The application of digital stickers in AR redefines the spatial order, typology, and governance of graffiti. Full article

Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates. Full article

Landscape preference emerges from the dynamic interaction between individuals and their environment and plays a pivotal role in the preservation and enhancement of the Chinese Grand Canal’s scenery. As a vast linear heritage, employing conventional methods for analyzing landscape preferences can be resource-intensive in terms of both time and labor. Amid the rapid advancement of Big Data and Artificial Intelligence (AI), a cognitive framework for understanding the Chinese Grand Canal’s landscape preferences has been developed, encompassing two primary aspects: the characteristic features of landscape preference and its spatial organization. Geotagged photographs from tourism media platforms focused on the Yangzhou Ancient Canal were utilized, and the EasyDL deep learning platform was employed to devise a model. This model assesses current landscape preferences through an analysis of photographic content, element composition patterns, and geospatial distribution, integrating social network and point density analyses. Our findings reveal that the fusion of Yangzhou Ancient Canal and classical gardens creates a sought-after ‘Canal and Watercraft Remains’ landscape. Tourists’ preferences for different landscape types are reflected in the way the elements are combined in the photographs. Overall, landscape preferences are dense in the north and sparse in the south. Differences in tourists’ perceptions of the value of and preferences for heritage sites lead to significant variations in tourist arrivals at different sites. This approach demonstrates efficiency and scalability in evaluating the Chinese Grand Canal landscape, offering valuable insights for its strategic planning and conservation efforts. Full article

In the pursuit of sustainable national park management, park managers need to understand the interests and activities of their diverse visitors in order to conserve the natural environment and offer a better visitor experience. This study aimed to examine the effectiveness of using non-geotagged social media data from posts by park visitors for park management in comparison with geotagged data, which has been studied more extensively. We compared (1) visitors’ sociodemographic characteristics between geotagged and non-geotagged social media users through an onsite survey in Nikko National Park, Japan, and (2) the content of geotagged and non-geotagged photos shared within the study area on X (formerly Twitter). Our results showed that visitors in their 30s and 40s and foreign visitors had a greater tendency to use geotags. Non-geotagged photos more frequently and deeply capture nature-based activities and interests, including activities on trails, such as mountain climbing and hiking, and an interest in diverse animals and plants and landscapes that are less accessible. These findings indicate that non-geotagged social media data may have less age and nationality bias and advantages over the more widely-used geotagged data in capturing various nature-based experiences offered by national parks. Leveraging both geotagged and non-geotagged data can enable park managers to implement sustainable practices catering to a broader range of visitor interests and activities, contributing to the overarching goal of sustaining the natural environment while also enriching the visitor experience within national parks. Full article

Social media data, such as photos and status posts, can be tagged with location information (geotagging). This geotagged information can be used for urban spatial analysis to explore neighborhood characteristics or mobility patterns. With increasing rural-to-urban migration, there is a need for comprehensive data capturing the complexity of urban settings and their influence on human experiences. Here, we share an urban image stimulus set from the city of Lisbon that researchers can use in their experiments. The stimulus set consists of 160 geotagged urban space photographs extracted from the Flickr social media platform. We divided the city into 100 × 100 m cells to calculate the cell image density (number of images in each cell) and the cell green index (Normalized Difference Vegetation Index of each cell) and assigned these values to each geotagged image. In addition, we also computed the popularity of each image (normalized views on the social network). We also categorized these images into two putative groups by photographer status (residents and tourists), with 80 images belonging to each group. With the rise in data-driven decisions in urban planning, this stimulus set helps explore human–urban environment interaction patterns, especially if complemented with survey/neuroimaging measures or machine-learning analyses. Full article

Recreational ecosystem services (RESs) are the diverse recreational opportunities provided by nature to humans, which contribute to the improvement of public health and social well-being. The use of online social media is an efficient method for quantifying public perceptions of recreational ecosystem services (RESs) delivered by a given landscape. With the continuously changing demand for nature-focused outdoor recreational activities since COVID-19, camping has become the fastest-growing outdoor leisure activity in megacities and a key indicator for how people perceive and value the RESs provided by the landscape. Such unexpected changings triggered by COVID-19 have further led to an imbalance between demand and supply, which results in fierce conflicts in urban green space management. This study presents a spatial pattern analysis of how people perceive RESs in a megacity-scale case study of Beijing using geo-tagged camping notes posted on Little Red Book (LRB). We employed these camping notes in the context of a megacity to (i) map public camping behaviors patterns in urban green spaces, (ii) evaluate spatial clusters of high/low RESs, and (iii) investigate the relationship between RESs, local landscape features, and gender through correspondence analysis. Our results show that considerable spatial clustering of camping behaviors was observed in both suburban and urban green spaces. However, suburbs revealed a substantially higher RES value than central urban areas. In addition, water bodies were discovered to have remarkably low RES, while grassland and urban forests were found to have a close link with higher RES. In addition, significant gender preferences have been discovered, where female visitors prefer to camp in grassland, and male visitors favor bare ground and urbanized regions. Our findings would assist decision-makers in optimizing urban green space planning and management, adapting to fast-changing public camping demands in the context of the post-COVID-19 era. Findings also contribute to the literature by applying spatial analysis of social media data to understand public outdoor recreation activities and perceived value for megacities’ green space management. Full article

The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics. Full article

During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities. Full article

Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic congestion of eleven world cities. The presented framework and workflow are termed as SocialMedia2Traffic. Traffic congestion was predicted at four tile spatial resolutions and compared with Uber Movement data. The overall precision of the forecast for highly traffic-congested regions was approximately 81%. Different data processing steps including ways to aggregate data points, different proxies and machine learning approaches were compared. The lack of a universal definition on a global scale to classify road segments by speed bins into different traffic congestion classes has been identified to be a major limitation of the transferability of the framework. Overall, SocialMedia2Traffic further improves the usability of the tested feature space for traffic prediction. A further benefit is the agnostic nature of the social media platform’s approach. Full article