Search Results (38)

The study of visitor experiences incorporating user-generated content from accommodation platforms is a distinct trend in the geography of tourism. Our research aims to better understand how this content can be instrumentalized in order to assess the viability of Airbnb accommodation ranks based on specific case studies from Greece, Italy, and Spain. The methodological frame we propose is based on two separate tools. First, the users’ reviews are summarized using sentiment analysis techniques, and the positive component is extracted as a separate indicator (predictor). The second step consists of mobilizing geographically weighted regression (GWR) to identify of the potential statistical association between the Airbnb apartments’ ranks and the quantitative outputs of sentiment analysis. The results obtained for 13 case studies are based on more than 4.6 million reviews. They clearly emphasize a gap between the rank proposed by the platform and the positive scores of sentiment analysis for the accommodation units analyzed (88,053). Despite some limitations linked to the quantity of data needed to be integrated into the investigation, the methodological frame can be transferred to other destinations, providing useful information about the potential distortions of tourism markets by the meta-description of accommodation systems. Full article

This paper explores contemporary approaches to preserving and promoting cultural heritage by integrating game elements and advanced technologies, such as Virtual Reality (VR) and Augmented Reality (AR). In an era increasingly shaped by digital innovation, preserving cultural heritage demands new strategies to sustain engagement with historical narratives and artifacts. Emerging technologies like VR and AR offer immersive, interactive experiences that appeal to modern audiences, especially younger generations accustomed to digital environments (Bekele and Champion). Gamification—the use of game design principles in non-game contexts—has gained significant traction in education and cultural heritage, providing new methods for increasing user engagement and retention (Werbach and Hunter). By incorporating gamified features, heritage can be made more accessible, fostering emotional connections and deeper understanding (Huotari and Hamari; Zichermann and Cunningham). This aligns with the shift toward interactive digital storytelling as a tool to transform static heritage presentations into dynamic, participatory experiences (Champion and Rahaman). Central to this research is the conceptualization and development of a mobile application leveraging VR and AR to enhance user engagement and education around cultural heritage. Drawing on the principles of self-determination theory (Deci and Ryan) and empirical findings on gamified learning (Landers and Landers), the application combines educational content with interactive elements, creating an immersive learning environment. By addressing both content accessibility and interactive immersion, this application bridges the gap between traditional heritage preservation and the expectations of a digitally native audience. The recent literature underscores the potential of VR and AR in cultural preservation, emphasizing their ability to transcend physical boundaries, simulate historical environments, and promote active participation (Milgram and Kishino, Addison; Azuma). As virtual environments evolve, platforms like the metaverse expand possibilities for experiencing cultural heritage in spaces free of geographical limitations (Cipresso et al.; Radianti et al.). Such advancements have already demonstrated significant educational and experiential benefits (Wu et al.; Akçayır and Akçayır). This study employs both quantitative and qualitative methods to examine the target group’s attitudes toward gamified technologies for cultural heritage preservation. The initial results indicate substantial interest and willingness among users to engage with applications employing VR and AR. This aligns with findings in the literature that suggest immersive experiences can enhance learning outcomes and foster long-term engagement (Merchant et al.; Speicher et al.). The project has garnered significant recognition, receiving the Rector’s Award for the best scientific paper in the technical field at the University of Zagreb and earning bronze medals at the ARCA Innovation Fair and the INOVA Fair. These accolades underscore the project’s innovative approach and its potential for real-world application. By presenting a robust framework for integrating gamification and immersive technologies into cultural heritage preservation, this paper contributes to the growing discourse on utilizing advanced digital tools to ensure the sustainability and relevance of cultural heritage for future generations. Full article

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map, these characteristics can lead to point clutter, reducing the readability of the map product and misleading users to false interpretations of patterns in the data, e.g., regarding specific clusters or extreme values. With this work, we provide a framework that is able to generalize point data, preserving spatial clusters and extreme values simultaneously. The framework consists of an agent-based generalization model using predefined constraints and measures. We present the architecture of the model and compare the results with methods focusing on extreme value preservation as well as clutter reduction. As a result, we can state that our agent-based model is able to preserve elementary characteristics of point datasets, such as the point density of clusters, while also retaining the existing extreme values in the data. Full article

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback’s significant yet underexplored potential in understanding public library patronage across Taiwan’s six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with social network analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan’s National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience. Full article

With the development of mobile applications, location-based services (LBSs) have been incorporated into people’s daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named $GLPS$. On the user side, $GLPS$ uses Geohash encoding technology to convert the user’s location coordinates into a string code representing a rectangular geographic area. $GLPS$ uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user’s exact location. On the anonymous server side, the scheme takes advantage of Geohash codes’ geospatial gridding capabilities and GL-Tree’s effective location retrieval performance to generate a k-anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user’s location. We experimentally tested the performance of $GLPS$ and compared it with three schemes: $Casper$, $GCasper$, and $DLS$. The experimental results and analyses demonstrate that $GLPS$ has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association. Full article

The sense of place is a multidimensional construct that evokes an emotional commitment to a specific geographic setting. It can be a crucial aspect of cultural ecosystem services. While social media has gained popularity as a tool for assessing ecosystem services, its effectiveness in capturing a sense of place, its impact on cultural ecosystem services, and its role in the landscape design process remains less certain. This study investigates the role of urban parks in shaping the sense of place by analyzing user-generated content from a specific social media platform (Twitter). We gathered tweets from 30 diverse urban parks in Chicago, covering various park types, sizes, shapes, and management styles. Our analysis reveals multiple facets of the sense of place associated with urban parks. We suggest that a sense of place is not solely rooted in the attachment to physical surroundings but also in the personal experiences individuals encounter within these spaces. Residents residing near parks tend to develop a sense of ownership and responsibility, leading to stronger emotional bonds with their environment. Urban parks foster community engagement, enhance social cohesion, and offer opportunities for nature-based experiences. Furthermore, this study underscores the significance of diverse park features, accessibility, and size in bolstering place attachment. Our research demonstrates the potential for geoinformation analysis in the geodesign process as a cost-effective and scalable approach for understanding the person–place connection. Full article

In the era of GeoAI, Geospatial Intelligent Question Answering (GeoIQA) represents the ultimate pursuit for everyone. Even generative AI systems like ChatGPT-4 struggle to handle complex GeoIQA. GeoIQA is domain complex IQA, which aims at understanding and answering questions accurately. The core of IQA is the Question Classification (QC), which mainly contains four types: content-based, template-based, calculation-based and method-based classification. These IQA_QC frameworks, however, struggle to be compatible and integrate with each other, which may be the bottleneck restricting the substantial improvement of IQA performance. To address this problem, this paper reviewed recent advances on IQA with the focus on solving question classification and proposed a comprehensive IQA_QC framework for understanding user query intention more accurately. By introducing the basic idea of the IQA mechanism, a three-level question classification framework consisting of essence, form and implementation is put forward which could cover the complexity and diversity of geographical questions. In addition, the proposed IQA_QC framework revealed that there are still significant deficiencies in the IQA evaluation metrics in the aspect of broader dimensions, which led to low answer performance, functional performance and systematic performance. Through the comparisons, we find that the proposed IQA_QC framework can fully integrate and surpass the existing classification. Although our proposed classification can be further expanded and improved, we firmly believe that this comprehensive IQA_QC framework can effectively help researchers in both semantic parsing and question querying processes. Furthermore, the IQA_QC framework can also provide a systematic question-and-answer pair/library categorization system for AIGCs, such as GPT-4. In conclusion, whether it is explicit GeoAI or implicit GeoAI, the IQA_QC can play a pioneering role in providing question-and-answer types in the future. Full article

Rice is the main staple for more than half of the world’s population. In Ghana, rice is the fastest growing food commodity, and it is consumed by almost every household. However, yields continue to be low, as the pace of adoption of new varieties is low. The low rate of adoption has been attributed to failure of modern breeding to incorporate preferred traits for end users. This study thus employed an innovative set of breeding tools, the G+ tools, in identifying gendered trait preferences to develop a robust product profile through a mixed-method approach. The assertion that “men focus more on production and marketing related traits as women focus on production and cooking qualities” was also ascertained. Descriptive, inferential and content analyses were conducted, and the results indicate ecological differences in varietal choices. Production and marketing traits are jointly preferred by the gender groups. However, women and young women paid attention to post-harvest and cooking quality traits. The gender impact scores generated indicated there are tradeoffs in the traits preferred. These findings highlight the significance of recognizing geographical differences and gender heterogeneity in relation to varietal and trait preferences. In conclusion, the outcomes emphasize the necessity of gender-sensitive breeding work that considers the various needs and trait priorities of targeted men and women rice farmers in breeding decisions for a robust rice product profile. Full article

Agenda 2030 and Sustainable Development Goals (SDGs) are critical pieces of climate change communication. #FridaysForFuture (FFF) is one of the movements with the most coverage. This paper analyzes the network structure generated in Twitter by the interactions created by its users about the 23 September 2022 demonstrations, locates the most relevant users in the conversation based on multiple measures of intermediation and centrality of Social Network Analysis (SNA), identifies the most important topics of conversation regarding the #FridaysForFuture movement, and checks if the use of audio-visual content or links associated with the messages have a direct influence on the engagement. The NodeXL pro program was used for data collection and the different structures were represented using the Social Network Analysis method (SNA). Thanks to this methodology, the most relevant centrality measures were calculated: eigenvector centrality, betweenness centrality as relative measures, and the levels of indegree and outdegree as absolute measures. The network generated by the hashtag #FridaysforFuture consisted of a total of 12,136 users, who interacted on a total of 37,007 occasions. The type of action on the Twitter social network was distributed in five categories: 16,420 retweets, 14,866 mentions in retweets, 3151 mentions, 1584 tweets, and 986 replies. It is concluded that the number of communities is large and geographically distributed around the world, and the most successful accounts are so because of their relevance to those communities; the action of bots is tangible and is not demonized by the platform; some users can achieve virality without being influencers; the three languages that stood out are English, French, and German; and climate activism generates more engagement from users than the usual Twitter engagement average. Full article

This study examines the process of creating successful, engaging, interactive, and activity-based online educational materials, while taking the cognitive aspects of learners into account. The quality of online educational materials has become increasingly important in the recent period, and it is crucial that content is created that allows our students to learn effectively and enjoyably. In this paper, we present the milestones of curriculum creation and the resulting model, the criteria of selecting online learning environments, technical requirements, and the content of educational videos, interactive contents, and other methodological solutions. In addition, we also introduce some principles of instructional design, as well as a self-developed model that can be used to create effective online learning materials and online courses. There was a need for a self-developed, milestone-based, practice-oriented model because the models examined so far were too general and inadequate to meet the needs of a decentralized developer team, who work on different schedules, with significant geographical distances between them and do not place enough emphasis on taking cognitive factors into account. In these processes, special attention should be paid to having a clear and user-friendly interface, support for individual learning styles, effective multimedia, ongoing assistance and tracking of students’ progress, as well as interactivity and responsive appearance. Full article

Technological breakthroughs have offered innovative solutions for smart parking systems, independent of the use of computer vision, smart sensors, gap sensing, and other variations. We now have a high degree of confidence in spot classification or object detection at the parking level. The only thing missing is end-user satisfaction, as users are forced to use multiple interfaces to find a parking spot in a geographical area. We propose a trustless federated model that will add a layer of abstraction between the technology and the human interface to facilitate user adoption and responsible data acquisition by leveraging a federated identity protocol based on Zero Knowledge Cryptography. No central authority is needed for the model to work; thus, it is trustless. Chained trust relationships generate a graph of trustworthiness, which is necessary to bridge the gap from one smart parking program to an intelligent system that enables smart cities. With the help of Zero Knowledge Cryptography, end users can attain a high degree of mobility and anonymity while using a diverse array of service providers. From an investor’s standpoint, the usage of IPFS (InterPlanetary File System) lowers operational costs, increases service resilience, and decentralizes the network of smart parking solutions. A peer-to-peer content addressing system ensures that the data are moved close to the users without deploying expensive cloud-based infrastructure. The result is a macro system with independent actors that feed each other data and expose information in a common protocol. Different client implementations can offer the same experience, even though the parking providers use different technologies. We call this InterPlanetary Smart Parking Architecture NOW—IPSPAN. Full article

The given article focuses on the benefit of harvested Ambient Geographic Information (AGI) as complementary data sources for severe weather events and provides methodical approaches for the spatio-temporal analysis of such data. The perceptions and awareness of Twitter users posting about severe weather patterns were explored as there were aspects not documented by official damage reports or derived from official weather data. We analysed Tweets regarding the severe storm event Friederike to map their spatio-temporal patterns. More than 50% of the retrieved >23.000 tweets were geocoded by applying supervised information retrievals, text mining, and geospatial analysis methods. Complementary, central topics were clustered and linked to official weather data for cross-evaluation. The data confirmed (1) a scale-dependent relationship between the wind speed and the societal echo. In addition, the study proved that (2) reporting activity is moderated by population distribution. An in-depth analysis of the crowds’ central topic clusters in response to the storm Friederike (3) revealed a plausible sequence of dominant communication contents during the severe weather event. In particular, the merge of the studied AGI and other environmental datasets at different spatio-temporal scales shows how such user-generated content can be a useful complementary data source to study severe weather events and the ensuing societal echo. Full article

The Coronavirus Disease 2019 (COVID-19) pandemic has so far been the most severe global public health emergency in this century. Generally, citizen science can provide a complement to authoritative scientific practices for responding to this highly complex biological threat and its adverse consequences. Several citizen science projects have been designed and operationalized for responding to COVID-19 in Iran since the infection began. However, these projects have mostly been overlooked in the existing literature on citizen science. This research sheds light on the most significant online citizen science projects to respond to the COVID-19 crisis in Iran. Furthermore, it highlights some of the opportunities and challenges associated with the strengths and weaknesses of these projects. Moreover, this study captures and discusses some considerable insights and lessons learned from the failures and successes of these projects and provides solutions to overcome some recognized challenges and weaknesses of these projects. The outcomes of this synthesis provide potentially helpful directions for current and future citizen science projects—particularly those aiming to respond to biological disasters such as the COVID-19 pandemic. Full article

Floods are threats seriously affecting people’s lives and property globally. Risk analysis such as flood susceptibility assessment is one of the critical approaches to mitigate flood impacts. However, the inadequate field survey and lack of data might hinder the mapping of flood susceptibility. The emergence of user-generated content (UGC) in the era of big data provides new opportunities for flood risk management. This research proposed a flood susceptibility assessment model using UGC as a potential data source and conducted empirical research in Ji’an County in China to make up for the lack of ground survey data in mountainous-hilly areas. This article used python crawlers to obtain the geographic location of the floods in Ji’an City from 2016 to 2019 from social media, and the state-of-the-art MaxEnt algorithm was adopted to obtain the flood occurrence map. The map was verified by the flood data crawled from reliable official media, which achieved an average AUC of 0.857% and an overall accuracy of 93.1%. Several novel indicators were used to evaluate the importance of conditioning factors from different perspectives. Land use, slope, and distance from the river were found to contribute most to the occurrence of floods. Our findings have shown that the proposed historical UG C-based model is practical and has good flood-risk-mapping performance. The importance of the conditioning factors to the occurrence of floods can also be ranked. The reports from stakeholders are a great supplement to the insufficient field survey data and tend to be valuable resources for flood disaster preparation and mitigation in the future. Finally, the limitations and future development directions of UGC as a data source for flood risk assessment are discussed. Full article

Tourists’ experiential perceptions and specific behaviors are of importance to facilitate geographers’ and planners’ understanding of landscape surroundings. In addition, the potentially significant role of online user generated content (UGC) in tourism landscape research has only received limited attention, especially in the era of artificial intelligence. The motivation of the present study is to understand international tourists’ online reviews of Mt. Huangshan in China. Through a state-of-the-art natural language processing network (BERT) analyzing posted reviews across international tourists, our results facilitate relevant landscape development and design decisions. Second, the proposed analytic method can be an exemplified model to inspire relevant landscape planners and decision-makers to conduct future researches. Through the clustering results, several key topics are revealed, including international tourists’ perceptual image of Mt. Huangshan, tour route planning, and negative experience of staying. Full article