Revisiting city tourism in the longer run: an exploratory analysis based on LBSN data

Encalada-Abarca, Luis; Ferreira, Carlos Cardoso; Rocha, Jorge

doi:10.1080/13683500.2023.2182669

Cited by 11 publications

(10 citation statements)

References 65 publications

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“…It is one of the most prevalent sources because tweets enable free, geotagged, real-time information. (Encalada-Abarca et al, 2024), thus valuable information on the tourist footprint can potentially be retrieved from tweets and sender profiles (Yubero et al, 2021). Actually, using Twitter's API (application programming interface) is the primary method for obtaining data from the social media platform for scientific purposes (Provenzano et al, 2018;Sontayasara et al, 2021).…”

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

confidence: 99%

“…These data facilitate the visualization of segmented variables and the identification of Points of Interest (POIs) or Areas of Interest (AOIs) within urban centers (García-Palomares et al 2015;Li et al 2018). Furthermore, the analysis of LBSN data, supported by dynamic georeferenced User-Generated Content (UGC) and metadata, proves beneficial for investigating disparities and imbalances such as those arising from over tourism (Nolasco-Cirugeda et al, 2022;Encalada-Abarca, et al, 2024) or the uneven distribution of tourist flows towards specific tourist areas, leading to issues like visitor overcrowding in specific tourist zones (Jiansheng &Yanling, 2021). This phenomenon elucidates the issue of overcrowding or clustering of visitors, including tourists and local inhabitants (Jiansheng & Yanling, 2021), in areas with particular tourist attractions while neglecting other dispersed urban tourist spots offering diverse activities and amenities.…”

Section: Introductionmentioning

confidence: 99%

“…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 (Martí et al, 2021); secondly, it presents a novel method for describing and characterizing urban tourist activity centers and identifying Areas of Interest (AOIs) from the perspective of users, utilizing Location-Based Social Networking (LBSN) data (Encalada-Abarca, et al, 2024). Consequently, it becomes possible to recognize the venues preferred by tourists, enabling the verification of urban areas that attract tourists' attention and providing guidance for the city's development based on patterns of user activity (Nolasco-Cirugeda et al, 2022).…”

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: Introductionmentioning

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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“…Description involves plotting observations over time to reveal patterns, while explanation explores relationships between variables. Prediction focuses on forecasting future values, and control utilizes time series to enhance control over physical or economic systems.Possible applications span from land use-cover [3,4] and agriculture changes [5,6], tourism [7,8], socioeconomic vulnerability [9], epidemiology [10], and health [11]. This chapter delves into advanced approaches for time series analysis.…”

mentioning

confidence: 99%

“…Possible applications span from land use-cover [3,4] and agriculture changes [5,6], tourism [7,8], socioeconomic vulnerability [9], epidemiology [10], and health [11]. This chapter delves into advanced approaches for time series analysis.…”

mentioning

confidence: 99%

Introductory Chapter: Time Series Analysis

M. Viana,

Oliveira,

Rocha

2024

Time Series Analysis - Recent Advances, New Perspectives and Applications

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Time series, defined as sequentially observed data points over time [1], find applications across diverse domains such as economics and engineering. The statistical analysis of time series is crucial, and Chatfield's taxonomy identifies six main categories: Economic and Financial Time Series, Physical Time Series, Marketing Time Series, Process Control Data, Binary Processes, and Point Processes.To effectively categorize time series, consideration of features like seasonality, trend, and outliers is essential [1]. Seasonality reflects recurring patterns over time intervals, while trend represents a systematic linear or nonlinear component. Outliers are observations distant from others, often indicating anomalies. The categorization and analysis of time series are pivotal for drawing meaningful inferences from the diverse structures encountered in engineering, science, sociology, and economics [2].The objectives of time series analysis encompass description, explanation, prediction, and control. Description involves plotting observations over time to reveal patterns, while explanation explores relationships between variables. Prediction focuses on forecasting future values, and control utilizes time series to enhance control over physical or economic systems.Possible applications span from land use-cover [3,4] and agriculture changes [5,6], tourism [7,8], socioeconomic vulnerability [9], epidemiology [10], and health [11]. This chapter delves into advanced approaches for time series analysis. Time series analysisTime Series Analysis (TSA) involves constructing predictive models that generate a target variable or label based on sequential observations across a defined period, that is, data that is time-dependent. The analysis of time series involves studying the relationships between variables that change over time [12].There are two types of time series: deterministic and stochastic. Deterministic time series can be predicted with certainty based on previous experience, while stochastic time series have random fluctuations [13]. Time series analysis is widely used in various fields such as economics, finance, and health research [12,14]. It helps in identifying patterns, forecasting future values, and understanding the underlying dynamics of the data [14][15][16]. As such, it has applications in various domains and is important for making inferences about the future based on past observations [12,13,15,17,18].

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Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach

Ding,

Zheng,

Zhang

et al. 2024

Humanit Soc Sci Commun

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Tourist movement patterns among attractions are complex and variable, and understanding such patterns can help manage tourist destinations more effectively. However, previous studies on tourist movement utilising complex networks have not explored the network motif approach comprehensively. Therefore, we adopted a network motif approach using social media data to extract and analyse motifs in a city network. This study analyses the attractions corresponding to the nodes in each motif, revealing the connection patterns between these attractions. We also discuss motifs between attractions with different types and titles. Popular attractions play a significant role in a local network while other attractions serve distinct functions within the network. This study’s findings enhance the significance of network motifs in examining tourist movement and deepen the understanding of recurring movement patterns between attractions. Moreover, they assist managers in developing policy tools for intelligent tourism destination marketing and planning that cater to tourists’ needs.

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Revisiting city tourism in the longer run: an exploratory analysis based on LBSN data

Cited by 11 publications

References 65 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

Introductory Chapter: Time Series Analysis

Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach

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