Search results

Due to rapid development, historic city areas are faced with urbanization damage to their characteristic urban identity besides physical deterioration and economic decay. The purpose of this paper is to address the following questions: What are the constituent elements of locality for historic areas? How does one classify historic areas according to locality elements? What are the characteristics of each kind of historic area? How does one identify to-be-protected locality elements according to different historic areas to realize sustainable development?

As a historic cultural city with a building history of over 3,000 years, Beijing has a myriad of distinctive historic areas, of which 367 were selected as the research samples. This paper classifies historic areas into the following four categories: distinctive areas, permanent areas, adaptive areas and inherited areas by analyzing the locality elements of 8,905 geo-tagged photos related to Beijing historic areas. The correlation among locality elements – the basis for joint protection – is also examined by Pearson’s correlation analysis.

The results are as follows: the reaction degree of carrier elements is generally higher than that of information elements, of which the representative architecture is the main constituent element of locality; folk customs, traditional activities and other intangible cultural heritage in historic areas receive only slight attention and need to be further stressed; controlled by non-human factors, permanent elements bear a high degree of autocorrelation; and emerging tourism and business activities have, to some extent, grown into constituent parts of the locality elements in historic areas.

This paper seeks to strike a dynamic balance between city renewal and historic area protection, providing a reference for understanding the dynamics of locality.

The purpose of this paper is to present a low-cost, highly mobile system for performing street-level imaging. Street-level imaging and geo-location-based services are rapidly growing in both popularity and coverage. Google Street View and Bing StreetSide are two of the free, online services which allow users to search location-based information on interactive maps. In addition, these services also provide software developers and researchers a rich source of street-level images for different purposes – from identifying traffic routes to augmented reality applications. Currently, coverage for Street View and StreetSide is limited to more affluent Western countries with sparse coverage throughout south-east Asia and Africa. In this paper, we present a low-cost system to perform street-level imaging targeted towards the congested, motorcycle-dominant south-east Asian countries. The proposed system uses a catadioptric imaging system to capture 360-degree panoramic images which are geo-located using an on-board GPS. The system is mounted on the back of a motorcycle to provide maximum mobility and access to narrow roads. An innovative backwards remapping technique for flattening the images is discussed along with some results from the first 150 km which have been captured from Southern Vietnam.

The design was a low-cost prototype design using low-cost off-the-shelf hardware with custom software and assembly to facilitate functionality.

The system was shown to work well as a low-cost omnidirectional mapping solution targeted toward sea-of-motorbike road conditions.

Some of the pictures returned by the system were unclear. These could be improved by having artificial lighting (currently only ambient light is used), a gyroscope-stabilised imaging platform and a higher resolution camera.

This paper discusses a design which facilitates low-cost, street-level imaging for a sea-of-motorcycle environment. The system uses a catadioptric imaging approach to give a wide field of view without excessive image storage requirements using dozens of cameras.

This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image.

This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study.

Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination.

The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

This paper aims to answer the following four research questions: Where do tourists gaze at the destination? What do tourists gaze at the destination? How do tourists gaze differently? Why do tourists gaze differently referring to relevant theory?

With a computer vision approach, this study illustrated a series of maps that reflect where and what do tourists gaze at and compared the differences in the visual perceptions among Asian, European and North American tourists in Hong Kong.

The findings confirm that the “tourist gaze” is influenced by geographical and cultural conditions. The conclusions provided three types of implementations for destination management strategies and advocated a high engagement with computer vision technology.

In theory, this study proves that the “tourist gaze” is influenced by geographical and cultural conditions. The study’s methodological contribution lies in applying advanced technology of visual content analysis for big data relevant to the issue of the tourist gaze. Practically, the finding that has not been achieved via previous questionnaire surveys will serve as a reference for tourism recommendations and precision marketing. In addition, its practical contribution is that it offers a means by which to explore tourists’ perceptions of destinations and understand the attractiveness of destinations to tourists.

研究一方面使用计算机视觉深入学习模型对游客照片内容进行识别, 比较了亚洲、欧洲和北美游客在香港不同空间场景的视觉感知差异。另一方面, 研究借助ArcGIS软件对游客凝视地点和内容差异进行了具体可视化分析。

这项研究有四个研究子问题：

• （1） 游客在哪里凝视？

• （2） 游客凝视了什么？

• （3） 游客凝视内容有什么不同？

  （4） 为什么游客凝视不同？

（1） 游客在哪里凝视？

（2） 游客凝视了什么？

（3） 游客凝视内容有什么不同？

（4） 为什么游客凝视不同？

不同游客在旅游目的地的“凝视”存在差异, 差异表征具体体现在地点选择和内容偏好等维度。同时, 研究结果显示计算机视觉技术在旅游研究领域呈现较好的应用潜力。

理论上, 本研究佐证了”游客凝视”受地理和文化条件影响的理论。技术上, 本研究探索了视觉分析技术在游客凝视议题上应用, 为旅游目的地感知评估提供了新的视角。应用层面, 研究结论为旅游目的地精准营销提供了参考。

Resumen

Con un enfoque de visión artificial, este estudio ilustra una serie de mapas que reflejan dónde y qué miran los turistas, y compara las diferencias en las percepciones visuales entre los turistas asiáticos, europeos y norteamericanos en Hong Kong.

El estudio tiene cuatro preguntas de investigación:

• (1) ¿Dónde miran los turistas en el destino?

• (2) ¿Qué miran los turistas en el destino?

• (3) ¿Cómo miran los turistas de forma diferente?

• (4) ¿Por qué los turistas miran de forma diferente en referencia a la teoría pertinente?

(1) ¿Dónde miran los turistas en el destino?

(2) ¿Qué miran los turistas en el destino?

(3) ¿Cómo miran los turistas de forma diferente?

(4) ¿Por qué los turistas miran de forma diferente en referencia a la teoría pertinente?

Las conclusiones confirman que la “mirada del turista” está influida por las condiciones geográficas y culturales. Las conclusiones aportan tres tipos de aplicaciones para las estrategias de gestión de destinos y abogan por un alto compromiso con la tecnología de visión artificial.

En teoría, este estudio demuestra que la “mirada del turista” está influenciada por las condiciones geográficas y culturales. La contribución metodológica del estudio radica en la aplicación de tecnología avanzada de análisis de contenido visual para big data relevante para el tema de la mirada del turista. En la práctica, los hallazgos que no se han logrado a través de encuestas anteriores servirán de referencia para las recomendaciones turísticas y el marketing de precisión. Además, su contribución práctica es que ofrece un medio para explorar las percepciones de los turistas sobre los destinos, y comprender el atractivo de los mismos para los turistas.

Improvements and portability of technologies and smart devices have enabled a rapid growth in the amount of user-generated media such as photographs and videos. Whilst various media generation and management systems exist, it still remains a challenge to discover the right information, for the right purpose. This paper aims to propose an approach to reverse geocoding by cross-referencing multiple geospatial data sources to enable the enrichment of media and therefore enable better organisation and searching of the media to create an overall picture about places.

The paper presents a system architecture that incorporates the proposed approach to aggregate several geospatial databases to enrich geo-tagged media with human readable information, which will further enable the goal of creating an overall picture about places. The approach enables the semantic information relating to point of interest.

Implementation of the proposed approach shows that a single geospatial data source does not contain enough information to accurately describe the high-level geospatial information for geocoded multimedia. However, fusing several geospatial data sources together enables richer, more accurate high-level geospatial information to be tagged to the geocoded multimedia.

The contribution in this paper shows that high-level geospatial information can be retrieved from many data sources and fused together to enrich geocoded multimedia which can facilitate better searching and retrieval of the multimedia.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

The purpose of this paper is to provide a review of hospitality and tourism studies that have used social media analytics to collect, examine, summarize and interpret “big data” derived from social media. It proposes improved approaches by documenting past and current analytic practice addressed by the selected studies in social media analytics.

Studies from the past 18 years were identified and collected from five international electronic bibliographic databases. Social media analytics-related terms and keywords in the titles, keywords or abstracts were used to identify relevant articles. Book chapters, conference papers and articles not written in English were excluded from analysis. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guided the search, and Stieglitz and Dang-Xuan’s (2013) social media analytics framework was adapted to categorize methods reported in each article.

The research purpose of each study was identified and categorized to better understand the questions social media analytics were being used to address, as well as the frequency of each method’s use. Since 2014, rapid growth of social media analytics was observed, along with an expanded use of multiple analytic methods, including accuracy testing. These factors suggest an increased commitment to and competency in conducting comprehensive and robust social media data analyses. Improved use of methods such as social network analysis, comparative analysis and trend analysis is recommended. Consumer-review networks and social networking sites were the main social media platforms from which data were gathered; simultaneous analysis of multi-platform/sources of data is recommended to improve validity and comprehensive understanding.

This is the first systematic literature review of the application of social media analytics in hospitality and tourism research. The study highlights advancements in social media analytics and recommends an expansion of approaches; common analytical methods such as text analysis and sentiment analysis should be supplemented by infrequently used approaches such as comparative analysis and spatial analysis.

本文对酒店旅游学科中采用社交媒体数据分析的文献进行梳理。本文通过审阅其相关分析方法的文献来提出分析方法的改进策略。

样本数据包括过去18年中五个国际在线文献索引库中的文献。搜索通过标题、关键词、或者摘要中出现社交媒体数据分析等相关字样的文章。书章节、会议文章、以及非英文文章未被收录在索引中。系统回顾和文献综述的方法（PRISMA）指导本文文献索引, Stieglitz和Dang-Xuan（2013）社交媒体数据分析框架作为本文文献分类的方法。

本文汇报了每篇文献的研究目的以及系统归类以更好理解社交媒体数据分析的研究问题以及每种方法的使用频率。自2014年起, 社交媒体数据分析快速增长, 以及其他相关分析方法, 包括精度测试（accuracy testing）。这些结果表明更多全面、稳定的分析方法需求增强以及竞争激烈。本文推荐使用改良方法, 比如社交网络分析法、比较分析、趋势分析等。消费者评价网络和社交网站成为主要社交媒体网络数据的提供平台。本文推荐多源数据应该同步分析以提高有效性和全面性的理解。

本文是首篇酒店旅游领域中对社交媒体数据分析的系统文献回顾型文章。本文强调了社交媒体数据分析的先进性以及扩展其方法的全面性；常见分析方法比如文本分析和情感分析应该结合非常见的分析方法比如比较分析法和空间分析法进行系统分析。

关键词 – 关键词 对比分析, 情感分析, 用户原创内容,社交媒体分析, 主题模型, 空间分析, 文本分析文章类型 文献综述

The purpose of this study is to present the state-of-the-art scientific research in the intersection between smart tourism and social networks to better understand the role of the latter (such as Facebook and Twitter) in the development of the smart paradigm in tourism, e.g. how the relationship between smart tourism and social networks has been observed by authors, or the techniques and analytical tools that are most broadly used to take advantage of available data in social networks to feed a smart logic in the management of tourism companies and destinations.

A systematic search in scientific databases (such as Web of Science and Scopus) was performed, with December 2018 as a closing date. Once duplications were eliminated and the adequacy the outputs obtained verified properly, a total of 61 documents were considered for this study, most of which were journal articles.

The characterization of research in this area with both a bibliometric and bibliographic analysis.

The presentation of a detailed picture on the content of the research carried out in the abovementioned intersections: evolution over time; most prolific authors, institutions and countries; journals, conferences and publishers that have paid more attention to this topic; type of research performed, objects of study, methodologies used and main contributions.

The proposal of a research agenda in the field under study. To the best of the authors’ knowledge, there is no a similar work carried out in this field.

Social Media is one of the largest platforms to voluntarily communicate thoughts. With increase in multimedia data on social networking websites, information about human behaviour is increasing. This user-generated data are present on the internet in different modalities including text, images, audio, video, gesture, etc. The purpose of this paper is to consider multiple variables for event detection and analysis including weather data, temporal data, geo-location data, traffic data, weekday’s data, etc.

In this paper, evolution of different approaches have been studied and explored for multivariate event analysis of uncertain social media data.

Based on burst of outbreak information from social media including natural disasters, contagious disease spread, etc. can be controlled. This can be path breaking input for instant emergency management resources. This has received much attention from academic researchers and practitioners to study the latent patterns for event detection from social media signals.

This paper provides useful insights into existing methodologies and recommendations for future attempts in this area of research. An overview of architecture of event analysis and statistical approaches are used to determine the events in social media which need attention.

This study aims to conduct an empirical investigation of differing perceptions of nine types of urban space and nine visual elements among tourists in destination using a computer vision (CV) approach.

The data for this study was extracted from YFCC 100 M dataset. Nine types of urban space in Beijing were initially identified using a scene recognition model. Subsequently, a semantic segmentation model was applied, which yielded substantial evidence relating to nine visual elements that were used to elicit differing perceptions among tourists from different continents.

Tourists from three continents had different perceptions about corridors, old buildings, overlooks and traffic spaces, reflecting their cultural convention. Asians, Europeans and North Americans diversely gazed at the landscape element of buildings, foliage, sky and people in urban space. All those provided evidence to contribute to the tourist gaze theory's construction.

This study firstly depicted how tourists perceive the tourism symbol of urban space. The novel approach of employing two CV models offer methodological insights to tourism research relevant to visual perception.

游客对城市空间的感知:计算机视觉途径

本研究采用计算机视觉方法, 探究游客对旅游目的地九种城市空间类型及九种视觉元素的感知差异。

本研究数据提取自YFCC 100M图片数据集。首先, 利用场景识别模型识别了游客图片中的九种城市空间类型。其次, 应用语义分割模型识别了游客图片的九个视觉元素。这些分析结果被用于探究不同大洲游客的视觉感知差异。

来自不同大洲的游客对城市空间有不同的感知偏好。亚洲人更喜欢拍摄自己与著名的城市建筑, 欧洲人和北美人更喜欢自然元素, 如水、树叶和天空。不同大洲游客对视觉元素的偏好佐证了旅游凝视理论。

本研究选取了独特的城市空间为研究对象, 来验证游客凝视理论。此外, 两种计算机视觉模型为旅游研究提供了新的方法论视角。

La percepción de los turistas del espacio urbano: Un enfoque de vision artificial

Resumen

Los datos para este estudio se extrajeron del conjunto de datos YFCC 100 M. Inicialmente se identificaron nueve tipos de espacio urbano en Pekín mediante un modelo de reconocimiento de escenas. Posteriormente, se aplicó un modelo de segmentación semántica, que aportó pruebas sustanciales en relación con nueve elementos visuales que se utilizaron para suscitar percepciones diferentes entre turistas de distintos continentes.

El objetivo de este estudio es llevar a cabo una investigación empírica sobre las diferentes percepciones de nueve tipos de espacio urbano y nueve elementos visuales entre los turistas en destino, utilizando un enfoque de visión artificial (CV).

Los turistas de tres continentes tenían percepciones diferentes sobre los pasillos, los edificios antiguos, los miradores y los espacios de tráfico, lo que refleja su convención cultural. Los asiáticos, los europeos y los norteamericanos observaron de forma diversa el elemento paisajístico de los edificios, el follaje, el cielo y las personas en el espacio urbano. Todos ellos aportaron pruebas para contribuir a la construcción de la teoría de la mirada turística.

Este estudio describe por primera vez cómo los turistas perciben el símbolo turístico del espacio urbano. El novedoso enfoque de emplear dos modelos de vision artificial ofrece conocimientos metodológicos para la investigación del turismo relacionados con la percepción visual.