Toward travel pattern aware tourism region planning: a big data approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qiwei Han ◽  
Margarida Abreu Novais ◽  
Leid Zejnilovic
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Design Methodology ◽  
Processing Technique ◽  
Temporal Behavior ◽  
Travel Patterns ◽  
Travel Pattern ◽  
Content Type ◽  
Natural Language Processing Technique ◽  
Spatio Temporal

Purpose The purpose of this paper is to propose and demonstrate how Tourism2vec, an adaptation of a natural language processing technique Word2vec, can serve as a tool to investigate tourism spatio-temporal behavior and quantifying tourism dynamics. Design/methodology/approach Tourism2vec, the proposed destination-tourist embedding model that learns from tourist spatio-temporal behavior is introduced, assessed and applied. Mobile positioning data from international tourists visiting Tuscany are used to construct travel itineraries, which are subsequently analyzed by applying the proposed algorithm. Locations and tourist types are then clustered according to travel patterns. Findings Municipalities that are similar in terms of their scores of their neural embeddings tend to have a greater number of attractions than those geographically close. Moreover, clusters of municipalities obtained from the K-means algorithm do not entirely align with the provincial administrative segmentation.

Identifying tourist profiles to reduce overtourism: the case of a cultural destination

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rocío Martínez Suárez ◽  
José Alberto Castañeda García ◽  
Miguel Ángel Rodríguez Molina
Keyword(s):  
Design Methodology ◽  
Cultural Orientation ◽  
Cultural Tourism ◽  
Important Variable ◽  
Temporal Behavior ◽  
Content Type ◽  
Tourist Flows ◽  
Tourism Destinations ◽  
Spatio Temporal ◽  
Visiting Time

Purpose Knowing the behavior of tourists visiting cultural destinations enables better management of tourist flows, a better understanding of areas with greater tourist density and an opportunity to decongest popular neighborhoods. The purpose of this study is to segment tourists according to their spatio-temporal behavior and identify the primary variables that characterize the resulting segments, which will help urban destinations prevent problems arising from the saturation of tourists in certain areas. Design/methodology/approach To do this, this paper analyzes the behavior of tourists visiting the southeastern Spanish city of Granada, one of the most highly visited cultural tourism destinations. The data analysis used the methodology of sequence alignment which is used to identify segments as a function of their contained elements and the order in which these appear. Findings The results demonstrate the existence of three segments with different behavioral patterns: the “explorer tourists” segment, the “non-traditional cultural tourists” segments and the “typical cultural tourists” segment. These segments show differences in the concentration of their visits. This study discovered that the segments that visit a greater number of destination areas are those with less cultural orientation, higher travel budgets and younger and more frequent visitors. Originality/value In the segmentation not only keep in mind the visited areas, but the order in which they were visited as well. In addition, one should consider the time that each tourist remains in each relevant zone of the destination, given that the visiting time is an important variable to assess the congestion of an area.

A bibliographic study on big data: concepts, trends and challenges

2017 ◽  
Vol 23 (3) ◽  
pp. 555-573 ◽  
Author(s):  
Deepa Mishra ◽  
Zongwei Luo ◽  
Shan Jiang ◽  
Thanos Papadopoulos ◽  
Rameshwar Dubey
Keyword(s):  
Big Data ◽  
Citation Analysis ◽  
Design Methodology ◽  
Future Directions ◽  
Content Type ◽  
Current Trends ◽  
Bibliographic Study

Purpose The purpose of paper is twofold. First, it provides a consolidated overview of the existing literature on “big data” and second, it presents the current trends and opens up various future directions for researchers who wish to explore and contribute in this rapidly evolving field. Design/methodology/approach To achieve the objective of this study, the bibliographic and network techniques of citation and co-citation analysis was adopted. This analysis involved an assessment of 57 articles published over a period of five years (2011-2015) in ten selected journals. Findings The findings reveal that the number of articles devoted to the study of “big data” has increased rapidly in recent years. Moreover, the study identifies some of the most influential articles of this area. Finally, the paper highlights the new trends and discusses the challenges associated with big data. Research limitations/implications This study focusses only on big data concepts, trends, and challenges and excludes research on its analytics. Thus, researchers may explore and extend this area of research. Originality/value To the knowledge of the authors, this is the first study to review the literature on big data by using citation and co-citation analysis.

AI and libraries: trends and projections

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adetoun A. Oyelude
Keyword(s):  
Language Processing ◽  
Service Provision ◽  
Design Methodology ◽  
Human Robot Interaction ◽  
Human Intelligence ◽  
Library Service ◽  
Robot Interaction ◽  
Content Type ◽  
New Horizons ◽  
Advanced Technologies

Purpose This paper aims to focus on the trends and projection for future use of artificial intelligence (AI) in libraries. AI technologies is the latest among the technologies being used in libraries. The technology has systems that have natural language processing, machine learning and pattern recognition capabilities that make service provision easier for libraries. Design/methodology/approach Systematic literature review is done, exploring blogs and wikis, to collect information on the ways in which AI is used and can be futuristically used in libraries. Findings This paper found that uses of AI in libraries entailed enhanced services such as content indexing, document matching, content mapping content summarization and many others. AI possibilities were also found to include improving the technology of gripping, localizing and human–robot interaction and also having artificial superintelligence, the hypothetical AI that surpasses human intelligence and abilities. Originality/value It is concluded that advanced technologies that AI are, will help librarians to open up new horizons and solve challenges that crop up in library service delivery.

Big data analysis of trends in the second-hand apparel market: a comparison between 2014 and 2019

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sanghee Kim ◽  
Hongjoo Woo
Keyword(s):  
Big Data ◽  
Design Methodology ◽  
Semantic Network ◽  
Distribution Channels ◽  
Subject Area ◽  
Future Research ◽  
Semantic Network Analysis ◽  
Content Type ◽  
The Past ◽  
Network Patterns

Purpose According to the perspective of evolutionary economic theory, the marketplace continuously evolves over time, following the changing needs of both customers and firms. In accordance with the theory, the second-hand apparel market has been rapidly expanding by meeting consumers’ diverse preferences and promoting sustainability since 2014. To understand what changes in consumers’ consumption behaviors regarding used apparel have driven this growth, the purpose of this study is to examine how the second-hand apparel market product types, distribution channels and consumers’ motives have changed over the past five years. Design/methodology/approach This study collected big data from Google through Textom software by extracting all Web-exposed text in 2014, and again in 2019, that contained the keyword “second-hand apparel,” and used the Node XL program to visualize the network patterns of these words through the semantic network analysis. Findings The results indicate that the second-hand apparel market has evolved with various changes over the past five years in terms of consumer motives, product types and distribution channels. Originality/value This study provides a comprehensive understanding of the changing demands of consumers toward used apparel over the past five years, providing insights for retailers as well as future research in this subject area.

Optimisation of resource management in construction projects: a big data approach

2019 ◽  
Vol 16 (2) ◽  
pp. 82-93 ◽  
Author(s):  
Habeeb Kusimo ◽  
Lukumon Oyedele ◽  
Olugbenga Akinade ◽  
Ahmed Oyedele ◽  
Sofiat Abioye ◽  
...  
Keyword(s):  
Big Data ◽  
Resource Management ◽  
Construction Industry ◽  
Construction Projects ◽  
Design Methodology ◽  
Key Factors ◽  
Content Type ◽  
Management Processes ◽  
Qualitative Research Methodology ◽  
The Uk

Purpose The purpose of this paper is to identify challenges faced in resource management in the UK construction industry and to propose some solutions to these problems. Design/methodology/approach Based on a qualitative research methodology, 14 experts from the UK construction industry were chosen to be participants in the study. The participants were equally divided into two focus groups to discuss resource management using five projects as case studies. Thematic analysis of the discussion reveals seven key factors that affect resource management. Findings The results show that most of the problems identified are due to poor data management processes and the practice of having data in silos. Overcoming this challenge requires the adoption of big data approaches for resource management to allow the integration of large and different forms of data. Originality/value This study seeks to bring to the fore challenges faced in resource management by the UK construction industry and to outline some solutions to address them.

scholarly journals Influence of Big Data in managing cyber assets

2019 ◽  
Vol 9 (4) ◽  
pp. 503-514
Author(s):  
Amit Mitra ◽  
Kamran Munir
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Design Methodology ◽  
Insurance Industry ◽  
Manufacturing Industries ◽  
Time Data ◽  
Content Type ◽  
Innovative Leadership ◽  
Data Flows ◽  
Desk Research

Purpose Today, Big Data plays an imperative role in the creation, maintenance and loss of cyber assets of organisations. Research in connection to Big Data and cyber asset management is embryonic. Using evidence, the purpose of this paper is to argue that asset management in the context of Big Data is punctuated by a variety of vulnerabilities that can only be estimated when characteristics of such assets like being intangible are adequately accounted for. Design/methodology/approach Evidence for the study has been drawn from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries. Findings By examining the extant literature, the authors traced the type of influence that Big Data has over asset management within organisations. In a context defined by variability and volume of data, it is unlikely that the authors will be going back to restricting data flows. The focus now for asset managing organisations would be to improve semantic processors to deal with the vast array of data in variable formats. Research limitations/implications Data used as evidence for the study are based on interviews, as well as desk research. The use of real-time data along with the use of quantitative analysis could lead to insights that have hitherto eluded the research community. Originality/value There is a serious dearth of the research in the context of innovative leadership in dealing with a threatened asset management space. Interpreting creative initiatives to deal with a variety of risks to data assets has clear value for a variety of audiences.

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