Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area

Mou, Jiarui

doi:10.1109/access.2022.3149640

Cited by 13 publications

(11 citation statements)

References 49 publications

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“…This enables researchers to identify the spatial structure and evolution law of the tourism flow (Gu et al, 2023). Multiple studies have explored the spatial network structures of tourist flows using online footprints (Jiansheng & Yanling, 2021;Mou, 2022;Yu, 2023). Aspects of tourist flow and tourist digital footprints are interconnected and are studied through network analysis methods in tourism research (Bondarenko et al, 2021).…”

Section: Literature Review Big Data Tourist Digital Footprint and Loc...mentioning

confidence: 99%

“…Fifthly, The TripAdvisor website stands out as a prominent platform for user-generated content, significantly impacting the decision-making process of prospective travelers (Nolasco-Cirugeda et al, 2022). Recent scholarly attention has been directed towards TripAdvisor as a valuable resource for investigating consumer behavior and preferences, encompassing a range of subjects, including the effects of market positioning (Mou, 2022), decision-making processes of travelers (Litvin & Dowling, 2018), and the impact of factors like accessibility and walkability on the spatial behavior of tourists (Hall & Ram, 2019). Noteworthy, all these platforms have been employed in previous researches and proven to be appropriate contributors of baseline information to address a range of tourism-related issues (Mondo et al, 2020;Martí et al, 2021;Nolasco-Cirugeda et al, 2022;Cebrián & Domenech, 2023;Encalada-Abarca et al, 2024).…”

Section: Literature Review Big Data Tourist Digital Footprint and Loc...mentioning

confidence: 99%

“…Moreover, this work offers a scientific foundation for enhancing and managing tourism spaces, traffic, and service facilities in specific regions (Gu et al, 2023). This approach can also contribute to orientating urban land-use planning (Mou, 2022) and formulating tourism strategies that align with urban revitalization and regeneration (Yubero et al, 2021) to address imbalances in various areas, thus mitigating issues like over-tourism (Nolasco-Cirugeda et al, 2022), gentrification, and uneven distribution of tourist flow towards specific destinations.…”

Section: Introductionmentioning

confidence: 99%

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رصد البصمة الرقمية السياحية ومراكز الأنشطة السياحية من خلال بيانات التوسيم الجغرافي والبيانات المحددة للموقع: مدينة الإسكندرية كدراسة حالة

زيدان,

هاشم

2024

مجلة کلية السياحة والفنادق - جامعة مدينة السادات

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The progression of technology has facilitated the examination of each user interaction and click on electronic devices. Tourists leave a digital "footprint" in most of their activities in the cities they visit; a significant amount of these data is geolocated. Studying the spatial and temporal preferences of individuals and patterns of movement represents a major factor that must be taken into account when designing urban land use policies, as contributions to the study of the spatial behavior of urban tourists in cities are still lacking. Therefore, the present study offers insights into user behavior in urban environments, facilitating the identification and examination of areas where there is a high concentration of tourist activities and enabling the identification of inactive zones within the urban area. The main aim of this research is to introduce a method for monitoring the level of tourist activity pulse in cities by examining User-Generated Content data. The novelty of this study is apparent in several key aspects: firstly, it concentrates on the intersections of eating, shopping, sightseeing, and nightlife activities in a chosen city for the case study; secondly, it presents a novel method for describing urban tourist activity centers and identifying areas of interest from the perspective of users, utilizing Location-Based Social Networking data by employing spatial clustering analysis. Thirdly, the proposed reference framework incorporates LBSN data, serving as a supplementary tool for urban planning and decisionmaking processes aimed at improving city urban dynamics. In this sense, an exploratory study was carried out using a case-study approach in Alexandria, Egypt by employing a GIS program-QGIS, Prizren 3.32 version. The findings of this study have significant implications for tourism destination managers, urban planners and scientists interested in the analysis of urban dynamics, to orientating urban land-use planning and formulating tourism strategies that align with urban revitalization and regeneration to address imbalances in various areas.

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Section: Literature Review Big Data Tourist Digital Footprint and Loc...mentioning

confidence: 99%

Section: Literature Review Big Data Tourist Digital Footprint and Loc...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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رصد البصمة الرقمية السياحية ومراكز الأنشطة السياحية من خلال بيانات التوسيم الجغرافي والبيانات المحددة للموقع: مدينة الإسكندرية كدراسة حالة

زيدان,

هاشم

2024

مجلة کلية السياحة والفنادق - جامعة مدينة السادات

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“…Therefore, the study of tourist travel patterns using data from web photo databases with geotagged information has real-world applications [8], [9]. This information can be used to analyze the spatiotemporal behavioral characteristics of tourists [10], [11], and destination resource allocation [12].…”

Section: Introductionmentioning

confidence: 99%

Behavior Analysis of Photo-Taking Tourists at Attraction-Level Using Deep Learning and Latent Dirichlet Allocation in Conjunction With Kernel Density Estimation

Iqbal,

Renjie,

2024

IEEE Access

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User-generated content (UGC) on social media platforms plays a significant role in conveying individual sentiments that are effective in predicting image sentiments by evaluating its contents to excavate the behaviour and cognition of image producers at attraction-level tourist destinations. In this study, we aim at the attraction-level study of the spatiotemporal behavior of photo-taking tourists. We used the deep convolutional neural network model DeepSentiBank (DSB) for sentiment prediction of Flickr photos. Then, Latent Dirichlet Allocation (LDA) was employed to categorize these sentiment predictions according to the noun content of specified distinct groups. Next, Kernel Density Estimation (KDE) was used as a spatial analysis tool to determine the spatial distribution characteristics of tourists' behaviors. The photo-taking behavioural patterns of different tourist types are analyzed from the individual preferences of tourists and depicted in terms of both visual semantics and spatiotemporal behavioral features. It is observed that the proportion of landscape preferences of the thematic tourists are not significantly affected by seasons but by the aggregate activities of thematic tourists that significantly vary in autumn. The findings obtained from the analysis hold immense significance in the realm of tourism and hospitality management which can play a significant role in the development of facility management, regional tourism, and prospects.

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“…Dijital ayakizleri ile kişilik tahminleri yapılabilmekte (Lambiotte ve Kosinski, 2014;Kosinski, 2013) ve kariyer yaşantısı şekillenebilmektedir (Kelly vd., 2013;Pritchard, 2013). Aynı zamanda dijital izler, reklam (Turow, 2015) ve turizm (Girardin vd., 2013;Mou, 2022;Cao vd., 2022) sektörlerinde de bireylerin tercihlerini, tüketimlerini belirleyebilmek adına kullanılmaktadır. Bu ifadelerden, dijital ayakizlerinin pek çok farklı amaçla kullanılabildiği anlaşılmaktadır.…”

Section: Introductionunclassified

A Study on Digital Footprint Awareness and Management Levels of Individuals

KOÇYİĞİT,

ÖZKUL

2023

Süleyman Demirel Üniversitesi Vizyoner Dergisi

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Bu çalışmanın amacı, pek çok dijital cihaz ve teknolojiyle sarmalanmış günümüzde dijital ayakizinin önemine vurgu yapmak, bireylerin dijital ayakizi yönetimi ve farkındalık düzeylerini incelemektir. Buna yönelik olarak literatürdeki veri toplama araçları incelenerek çalışmanın amacına uygun olarak bir anket formu tasarlanmıştır. Tasarlanan anket formunda bireylerin dijital ayakizi yönetimi ve dijital ayakizi farkındalık düzeylerini incelemeyi hedefleyen ifadelerin yanı sıra çalışmaya derinlik kazandırabilmesi adına dijital ortamları kullanım durumları ve kişisel bilgilerini paylaşma düzeylerine de yer verilmiştir. Veri toplama sürecinde, sosyo-demografik özellikler ile birlikte beş bölüm ve 47 ifadenin yer aldığı anket formu kullanılmıştır. 500 katılımcının yer aldığı araştırma sonucuna göre bireylerin dijital ayakizi farkındalığının ve yönetim düzeyinin yüksek olduğu görülmüştür. Dijital ayakizi farkındalığı ve dijital ayakizi yönetim düzeyleri sosyo-demografik özelliklere göre değişkenlik göstermektedir.

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Extracting Network Patterns of Tourist Flows in an Urban Agglomeration Through Digital Footprints: The Case of Greater Bay Area

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 13 publications

References 49 publications

رصد البصمة الرقمية السياحية ومراكز الأنشطة السياحية من خلال بيانات التوسيم الجغرافي والبيانات المحددة للموقع: مدينة الإسكندرية كدراسة حالة

رصد البصمة الرقمية السياحية ومراكز الأنشطة السياحية من خلال بيانات التوسيم الجغرافي والبيانات المحددة للموقع: مدينة الإسكندرية كدراسة حالة

Behavior Analysis of Photo-Taking Tourists at Attraction-Level Using Deep Learning and Latent Dirichlet Allocation in Conjunction With Kernel Density Estimation

A Study on Digital Footprint Awareness and Management Levels of Individuals

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