Geo-tagged Photos Research Articles

PREDICTING VISUAL AESTHETIC PREFERENCES OF LANDSCAPES NEAR HISTORICAL SITES BY FLUENCY THEORY USING SOCIAL MEDIA DATA AND GIS

There is an interactive relationship between humans and landscapes. Humans inherently assess landscapes by creating spontaneous preferences based on surrounding stimuli. Vision plays a key role in these preferences. Visual preferences are relevant for understanding visual aesthetic liking (VAL), which needs to be evaluated objectively. This study was carried out in Herakleia ad Latmos, comprising Lake Bafa Natural Park and the Latmos-Beşparmak Mountains. The aim of this paper is to predict people’s VAL of historical sites (HS) by applying processing fluency theory to social media data. Among fluency theory metrics, four metrics – visual simplicity, visual symmetry, visual contrast, and visual self-similarity, were used to develop an ordinary least squares (OLS) regression model. Two primary questions are explored in this study: (1) How to quantify spontaneous visits of people near historical sites, and (2) how to estimate preferences of people based on distances to HS regardless of landscape types (either cultural or natural). Results show that people mostly visited three HS out of thirteen historical sites between 2004 and 2020: Kapıkırı Island (HS 1), and the ancient cities of Herakleia (HS 2) and Latmos (HS 3). According to the findings of the OLS regression model, year (t = 8.99, p <.0001), visual simplicity (t = -4.64, p ≤ 0.0001), and visual contrast (t = -2.01, p = 0.04) of the geotagged photos were all statistically significant predictors of VAL. HS 2 had the highest VAL value, followed by HS 1, and HS 3.

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.

Spatial patterns and moving trajectory of inbound tourists based on geo-tagged photos:A case study of Nanjing city

Spatial patterns and moving trajectory of inbound tourists based on geo-tagged photos:A case study of Nanjing city

Examination of Urban Landscape Aesthetics through Mapping Using Geotagged Social Media Data from Flickr

Landscapes are part of the earth’s surface, and include terrestrial, aquatic, natural, and anthropogenic features with differing dominance. The evaluation of Landscape Aesthetics (LA) and scenery is helpful for the understanding landscapes. The mapping of LA is increasingly being recognized in urban and landscape planning. Examining the flow and capacity of LA is an essential element in understanding the sustainability of urban landscapes. An explicit methodological approach was proposed for mapping LA using social media data (geolocated photographs) from Flickr to study the spatial distribution of LA in Xi’an, China. The LA flow and LA capacity were conceptually distinguished and assessed. The LA flow was analyzed with 10,278 geotagged photographs from Flickr to express people’s actual appreciation of LA. The LA capacity was examined by spatial criterion evaluation, combined the weight of landscape features. LA flow and LA capacity differ in both spatial extent and quantity, although there was a spatially explicit balance between them. The results show that LA flow largely occurs in the urban core zone and that LA capacity is concentrated in certain parts of Xi’an due to the imbalanced distribution of historical cultural heritage sites. This research contributes to a better understanding of LA values and the relationships between LA values and landscape characteristics. The results can support the integration of cultural services in landscape planning and land use policy.

Changes in the spatial and temporal characteristics of inbound tourism flows in Tibet based on geotagged photographs

With the further advent of the era of big data, the scale of social media data containing geolocation information is exploding, providing a new source of big data information and perspective for an in-depth study of the changing spatio-temporal and geographical characteristics of the current tourist population. This paper extracts data on popular attractions in the Tibet Autonomous Region using the HDBSCAN algorithm combined with the TF-IDF algorithm based on information on images with geotags shared by users in the Flickr image sharing site from 2005-2018. Social network analysis was used to explore the changes in the spatial and temporal characteristics of inbound tourism flows in Tibet. The results show that: (1) in terms of temporal characteristics, the number of inbound tourists shows obvious off-peak seasons, with relatively high sensitivity to the influence of economic, policy and infrastructure construction factors; (2) in terms of spatial distribution characteristics, the inbound tourism flow in Tibet shows an “axis-scattered” distribution. The core area is centred on Lhasa and extends in three directions: west, north and east along important roads.

Evaluation of classifying geotagged photos aiming at analysis of tourist behavior

Evaluation of classifying geotagged photos aiming at analysis of tourist behavior

Recognition and Classification of Homogeneous Landscape With Visitor–Employed Photography and Cloud Image Annotation API—An Example of the Riverscape in Nihonbashi, Tokyo, Japan

Effective classification of landscape photographs is a vital step in data processing and environment analysis. With the popularity of crowdsourcing geo-information, an increasing number of studies have used geotagged photographs to visualize how people perceive and interact with destinations and explore the aesthetic, cultural, and recreational value of the areas. In recent years, machine-learning algorithms for image recognition have dramatically improved the efficiency of the assignment of keywords and provide possibilities for the automatic classification of numerous photographs. However, the applicability of such methods for the practical landscape classification is still not clear, especially for the photographs presenting a homogeneous landscape that has similar characteristics. This study developed a semi-automatic classifier for homogeneous landscape photographs by using Google Cloud Vision API and multi-level hierarchical clustering. The classifier was applied to the classification of urban riverscape photographs, which is a typical example of homogeneous landscapes in Nihonbashi, Tokyo, Japan. The riverscapes can be classified into 9 characteristic groups by the classifier and the visual impression of these groups matches well with our intuitive feeling. A confusion matrix showed that the overall accuracy was 82.61%, indicating a strong agreement between the classifier and manual classification. Therefore, the classifier is practical for classifying homogeneous riverscape photographs. Such methodology also provides the possibility of public participation in the assessing process, which, in turn, contributes to urban tourism management.

Mapping cultural ecosystem services using social media data: The case of Datça peninsula

Peyzaj planlamada cesitli karar verme olcekleri, somut ve ekonomik katkiya dâhil olmayan ekosistem hizmetlerinin degerinin olculmesini ve gorsellestirilmesini gerektirir. Kulturel peyzajlarin basarili bir sekilde surdurulmesi ve yonetilmesi icin soyut ve kulturel nitelikteki hizmetlerin, bir baska ifadeyle kulturel ekosistem hizmetlerinin mekânsal dagiliminin anlasilmasi gerekmektedir. Kulturel ekosistem hizmetlerinin sosyal medya verileri kullanilarak modellenmesi, mevcut planlama ve politika araclarina entegre edilebilecek bir yaklasim olusturabilir. Datca Yarimadasi’nda yurutulen bu calismada, uygulama programlama arayuzu (API) araciligiyla Flickr sosyal fotograf agindan indirilen cografi etiketli fotograflar kullanilmis ve kulturel ekosistem hizmetleri haritalanmistir. Fiziksel, deneysel, entelektuel ve sanatsal/ilham verici degerler kapsaminda incelenen fotograflarin; %57’si estetik, %20,1’si sanatsal/ilham verici, %15,4’u kulturel miras ve kimlik degeri, %6,9’u rekreasyonel deger ve %0,7’si ruhani ve dini degerleri gostermektedir. Fotograflara ait cografi etiketler, Kernel yogunluk tahmin modeli ile analiz edilmistir. Analiz sonucunda, kulturel miras ve kimlik degerini yansitan fotograflarin Knidos Antik Kenti ve yakin cevresinde; rekreasyonel deger, estetik deger ve sanatsal/ilham verici degerleri yansitan fotograflarin eski Datca’nin bulundugu Datca Yarimadasi yerlesiminde yogunlasma potansiyeline sahip oldugu ayni zamanda bu potansiyelin yarimada boyunca mekânsal varyasyona sahip olabilecegi gorulmektedir. Ruhani ve dini degerlerin yarimadanin merkezinde yalnizca bir noktada yogunlasabilecegi dikkat cekmektedir. Bu calisma, ekosistem hizmetlerinin haritalanmasi ile ilgili calisan arastirmacilara ulasmayi hedeflemekte ve kulturel degerlerin surdurulebilirliginde peyzaj yonetimi kapsaminda referans olusturabilecek bir yontem akisi sunmaktadir.

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.

Ecotourism spatio-temporal models to identify visitation patterns across the Indian Himalayan Region

Visitation patterns across protected areas of Uttarakhand, India reflect a unique amalgamation of natural, cultural and religious aspects of vulnerable mountain tourism destinations. These aspects act as predictors in determining visitation rates and identifying destination specific tourism models. Using the mixed methods approach based on field surveys, geotagged photographs and InVEST recreation tool, this study (1) maps the spatio-temporal movement of visitors across ten sampled protected areas of Uttarakhand (2) identifies predictor variables contributing to visitation across Binsar Wildlife Sanctuary (BWLS) of Uttarakhand (3) explores the visitors’ decision to recreate at specific locations across BWLS based on four tourism models and (4) determines the prevalent tourism model across BWLS. The visitation pattern assessment suggest Rajaji National Park as the most visited protected area in Uttarakhand, but the highest density of photographed location existed within the Corbett National Park. That, visitation to National Park outnumbers visitation to wildlife sanctuaries in Uttarakhand and preferably over the months of March–April (Spring) and October–November (Winter). Binomial logistic regression analysis on Flickr generated photographs reveals community-based ecotourism as the prevalent tourism model across Binsar. Human settlements and natural water springs of the sanctuary were key predictors affecting visitation in Binsar. The grounded nature of this research presents the ecotourism interacting variables addressing spatial movement of visitors across protected areas of critical importance, to manage tourism destinations sustainably.

Mapping destination images and behavioral patterns from user-generated photos: a computer vision approach

ABSTRACT Destination image studies were traditionally based on questionnaire surveys, but the recent rise of user-generated content and social media big data analytics provide new opportunities for advancing tourism research. This study adopts one of the latest artificial intelligence computer vision technologies to identify the differences in the perceived destination image and behavioral patterns between residents and tourists from user-generated photos. Data were mined from Flickr, which yields 58,392 relevant geotagged photos taken in Hong Kong. The findings reveal that the perceptual differences between the two groups lay on seven types of perceptions. The differences in spatial distribution and behavioral trajectory were visualized through a series of maps. This study provides new insights into the destination image which has implications for the tourism promotion and spatial development of the destination.

Less-Known Tourist Attraction Discovery Based on Geo-Tagged Photographs

Most existing studies of tourist attraction recommendations have specifically emphasized analyses of popular sites. However, recommending such spots encourages crowds to flock there in large numbers, making tourists feel uncomfortable. Furthermore, some studies have discovered that quite a few tourists dislike crowded destinations and prefer to avoid them. A ready solution is discovery and publicity of less-known tourist attractions. Especially, this study specifically examines discovery of less-known Japanese tourist destinations that are attractive and merit increased visits. Using this approach, crowds can not only be dispersed from popular tourist attractions, but more diverse spots can be provided for travelers to choose from. By analyzing geo-tagged photographs on Flickr, we propose a formula that incorporates different aspects such as image quality assessment (IQA), comment sentiment, and tourist attraction popularity for ranking tourist attractions. We investigate Taiwanese and Japanese people to assess their familiar Japanese cities and remove them from ranking results of tourist attractions. The remaining spots are less-known tourist attractions. As reported from results of verification experiments, most less-known tourist attractions are known by only a few people. They appeal to participants. Additionally, we examined some factors that might affect respondents when they decide whether a spot is attractive to them or not. This study can benefit tourism industries worldwide in the process of discovering potential tourist attractions.

An Exploratory Analysis of Geotagged Photos From Instagram for Residents of and Visitors to Vienna

This study uses geotagged photos from Instagram to identify differences between the popular places in Vienna for residents and visitors. Moreover, we explore whether geotagged data can be useful in determining tourism demand in Vienna. The spatial analysis of 627,632 geotagged photos reveals the top-50 locations in Vienna for all-, local-, and visiting-Instagram users based on three popularity indicators (numbers of likes, comments, and photos). The results show that the top locations unique to local users are closely linked to activities residents usually pursue or location types they usually visit at their place of dwelling. In using geotagged photos to predict actual tourist arrivals to Vienna, we conclude that only the popularity indicators number of likes and number of comments based on the location ID “Vienna, Austria” for visitors to Vienna should be used and not the number of photos, since this indicator does not automatically generate engagement.

ESTIMATING HEIGHTS OF BUILDINGS FROM GEOTAGGED PHOTOS FOR DATA ENRICHMENT ON OPENSTREETMAP

Abstract. To reconstruct 3D building models, building footprints and heights are essential information. From OpenStreetMap (OSM), we can easily obtain footprints. However, building height is usually missing. In order to yield the height information of building in OSM, this paper proposes a geometric method to estimate building height from geotagged photographs. This method explores the geometric relationship between the perspective centre of geotagged photos and buildings. Through matching photos and OSM, building height can be estimated according to the ratio of height to width of building. The proposed method can be divided into three parts. First, automatic geometric correction of photos is realized by using vanishing point tracking. After that, a semi-automatic scene search method is proposed to match the geotagged photograph and OSM. In this step, geographic coordinates of photos are used to locate a photographic scene. According to the edge of the building in the photos, corresponding footprints in OSM can be found. Finally, based on the length of the associated edge in the building footprint in OSM, the height of building can be calculated. Using Flickr photos and OSM in London, we experiment with the proposed method. The robustness of the geometric model has been verified. Experiments show that the proposed method is pertinent as the estimated height has expressed a proper ratio with its width, which is the same as the corrected photos. In particular for automatic geometric correction, which can achieve the same good results as the correction of manual operation.

Assessment of volunteered geographic information for vegetation mapping.

Vegetation mapping requires extensive field data for training and validation. Volunteered geographic information in the form of geotagged photos of identified plants has the potential to serve as a supplemental data source for vegetation mapping projects. In this study, we compare the locations of specific taxa from the iNaturalist platform to locations identified on both a fine-scale vegetation map and high-resolution ortho-imagery in open-canopy shrubland in San Clemente Island, CA. Due to positional uncertainty associated with the iNaturalist observations, as well as the presence-only nature of the data, it was not possible to perform a traditional accuracy assessment. We instead measured the distance between the location recorded by an iNaturalist observer for a given taxon and the closest mapped individual of that taxon. This distance was within 10m for a majority of the observations (64%). When comparing the iNaturalist location to the closest individual detected through image interpretation, 87% of the observations were within 10m. The discrepancy in agreement between the vegetation map and imagery is likely due to mapping errors. While iNaturalist data come with important limitations, the platform is an excellent resource for supporting vegetation mapping and other ecological applications.

A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

Grouping of Tourism Objects Using Geotagged Photo with Hierarchical Clustering Method in Bantul and Sleman

Bantul and Sleman regency has many tourism object that can attract tourists. However, tourism objects in this area are still poorly known by many people. When tourists visit tourism objects, they like to take pictures with a smartphone. The geotagging process can be done when taking photos using a smartphone. This data consists of latitude and longitude coordinates, though they can also include altitude, distance, and place names. The purpose of this study is to visualize photos of tourism objects based on location and provide detailed information related to the geotagged photo. The method used in this study is agglomerative hierarchical clustering. Grouping photos based on a hierarchy that starts from the village, district and regency. The results of this study are an application that can help users to receive information about tourism objects and display the location of photos with google maps. Users only need to upload a geotagged photo, to get this information. This application testing will be carried out by visitors of tourism objects in the Bantul and Sleman regency.

The traces of ecotourism in a digital world: spatial and trend analysis of geotagged photographs on social media and Google search data for sustainable development

论电子世界中的生态旅游业的发展途径：空间趋势分析带有地理标记的图片和谷歌搜索数据摘要研究目的本论文旨在探索生态旅游业在电子世界中是如何随着时间而显示出来的，文章样本为带有地理标记的图片和互联网搜索数据。本文使用生态旅游图片和谷歌趋势搜索数据来评估游客对生态旅游的感知，通过对关键要素的分类，审视生态旅游和可持续发展的关系，以及衡量生态旅游基地的受欢迎程度等方法。研究设计/方法/途径本论文作者从Flickr.com上搜集地理标记图片以及从谷歌趋势上下载谷歌搜索数据。样本分析通过内容、趋势、空间上的综合分析，来开发生态旅游类别和游客对生态旅游的感知。首先，我们研究了社交媒体上的生态旅游地理标记图片以理解游客对生态旅游的感知情况，以此搭建了关键生态旅游要素的类别体系，和衡量生态旅游基地的受欢迎程度。第二，我们通过使用互联网搜索数据，检测了生态旅游如何与可持续发展相连接，以及研究了搜索数据中的趋势。第三，我们使用了GIS软件来操作空间分析，对图片的空间-时间改变和游客对世界的观点做了可视化处理。研究结果本论文确立了三项生态旅游感知的基本主题以及13项生态旅游要素类别。生态旅游互联网随着时间演化，根据谷歌趋势上的定义，被大致地展现出来。本论文研究结果表示生态旅游地理位置和游客足迹与生态旅游谷歌搜索的热门区域不全是完全吻合的。研究原创性/价值本论文使用地理标记图片和互联网搜索数据将生态旅游发展趋势近十年的变化描画出来。全球生态旅游趋势的评估对全球可持续旅游发展和实际游客感知方面做出重要见解启示。生态旅游趋势的分析作为一种战略方法，对UN可持续发展目标的时间起到评估作用。本论文针对游客的真实感知和意见，游客如何选择和感知自然景观，这对于很多群体，比如旅游行业和政府，都有着重要意义。

On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study

Social media continues to grow, permanently capturing our digital footprint in the form of texts, photographs, and videos, thereby reflecting our daily lives. Therefore, recent studies are increasingly recognising passively crowdsourced geotagged photographs retrieved from location-based social media as suitable data for quantitative mapping and assessment of cultural ecosystem service (CES) flow. In this study, we attempt to improve CES mapping from geotagged photographs by combining natural language processing, i.e., topic modelling and automated machine learning classification. Our study focuses on three main groups of CESs that are abundant in outdoor social media data: landscape watching, active outdoor recreation, and wildlife watching. Moreover, by means of a comparative viewshed analysis, we compare the geographic information system- and remote sensing-based landscape organisation metrics related to landscape coherence and colour harmony. We observed the spatial distribution of CESs in Estonia and confirmed that colour harmony indices are more strongly associated with landscape watching and outdoor recreation, while landscape coherence is more associated with wildlife watching. Both CES use and values of landscape organisation indices are land cover-specific. The suggested methodology can significantly improve the state-of-the-art with regard to CES mapping from geotagged photographs, and it is therefore particularly relevant for monitoring landscape sustainability.

A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining

Abstract Personalized travel package recommendation for tourists is an important task in the e-Tourism field. With the excessive growth of data over the internet, it has become difficult to find relevant information among all of the Points-of-Interest (PoIs) for tourists. Recommendation Systems (RSs) are the essential tools to tackle the information overload problem and suggesting relevant items to the users. But, due to differences in users’ interests, dynamic contexts and sequential travel patterns in their behaviors, the existing recommender systems could not provide accurate recommendations to the users. Moreover, due to the lack of sufficient historical data about users in tourism area, cold start and data sparsity problems lead to more challenges for recommending precise and reliable suggestions. In this paper, a personalized travel package recommendation approach is proposed to generate dynamic recommendations to the users by taking into account the multidimensional data of time, location, users’ implicit ratings, tourists’ characteristics and their sequential travel movement patterns based on the geo-tagged photos. Therefore, we combine four different recommendation methods including, Collaborative Filtering (CF), Context-Awareness (CA), Demographic-Based (DB) and Sequential Pattern Mining (SPM) to ameliorate the quality of the suggestion results. Further, a dynamic contextual modelling approach is used to incorporate contexts information into the recommendation procedure to cope with the context pre/post filtering problems, and provide dynamic travel recommendation to the tourist based on his/her current contexts information. Furthermore, this approach extends the CF method by using multidimensional data and an asymmetric similarity scheme for score function to alleviate the limitation of CF and improve the accuracy of predicted recommendations. Moreover, the proposed approach recommendations will be optimized by applying the personalized PoIs to the sequential travel patterns and personalizes the travel package recommendations for each user based on his/her interests. The experimental results based on Flickr dataset illustrate the effectiveness and quality of the proposed approach over state-of-the-art approaches under cold-start and data sparsity conditions by contextualizing users’ interests, utilizing demographic information, applying the asymmetric similarity scheme and using the sequential pattern mining.