Number Of Tourists Research Articles

Diachronic and historical evolution of the Al-Hoceima Bay coastline, Moroccan Mediterranean Sea

Al-Hoceima Bay, located on the northern coast of Morocco, holds significant environmental importance. It also faces environmental challenges, including the pressures resulting from urban sprawl and growing number of tourists, as well as the impacts of climate change. The objective of this study is to assess the coastal changes in Al- Hoceima Bay since 1964, considering both natural and human factors. This study is based on the diachronic analysis of aerial photographs taken over a period of 56 years, utilising the digital shoreline analysis system statistical technique to calculate the shoreline’s mobility index for each period. The results demonstrate significant erosion at the Tayth beach (−1.50 m∙y−1) and Souani beach (approximately −1.90 m∙y−1), whereas accretion was experienced at the Sfiha beach at a rate of about +1.11 m∙y−1 and at the Lharch beach at a rate of +0.92 m∙y−1. The mouth of the Nekôr River experienced the highest retreat at −3.15 m∙y−1, followed by Ghiss at −2.00 m∙y−1. These findings indicate the impact of human interventions, such as the construction of the Mohamed Ben Abdelkarim El Khattabi dam on Oued Nekôr since 1981, as well as climate changes that have led to decreased flow, particularly at Oued Ghiss. These combined climatic and anthropogenic impacts have exacerbated erosion and disrupted the sediment balance along the shoreline of Al-Hoceima Bay.

Analisis Peran Media Sosial Dalam Meningkatkan Branding Wisata Jolotundo Glamping And Edu Park Nganjuk

This research aims to analyze the role of social media in enhancing the branding of Jolotundo Glamping and Edupark Nganjuk. The main challenges in improving tourism branding include competition with other destinations, the growth of global connectivity, information technology, and competition among destinations. These factors require effective marketing strategies. Additionally, changes in consumer trends and technology can affect the effectiveness of social media strategies. Social media has significant power in reaching a broad audience and influencing public perception of a destination. In the context of tourism, social media plays a crucial role in increasing widespread popularity and building a positive image of this destination. The data collection techniques used in this research include observation, interviews, and documentation. Data analysis is conducted using qualitative descriptive methods. The research findings show that social media can enhance the branding of Jolotundo Glamping and Edupark through the planning of forming a special social media team, selecting social media platforms, and engaging third parties. The implementation involves having a dedicated social media team, choosing Instagram, TikTok, and YouTube, and collaborating with third parties such as Niken Salindri and Woko Chanel. The impact of social media includes an increase in the number of tourists, a rise in social media engagement, and an increase in the diversity of visitor origins.

Digital Marketing Development Strategy to Increase the Number of Tourists

The increase in number of tourists is inseparable from the strategies used by tourist attractions by utilizing social media and Generation Z as a potential segmentation because they are known as digital natives. As happened to Bigstone Riverside located in Bogor, West Java, which once utilized social media by collaborating with influencers and getting an increase in visitors. Unfortunately, because the strategy used was inconsistent, there was a decline in visitors. This research aims to formulate a digital marketing development strategy for Bigstone Riverside. The selection of this research location was carried out purposively because this tourist destination has the potential to be developed and become a source of income for the surrounding community. This research uses qualitative data sourced from primary data and secondary data. The strategy formulation method used SWOT matrix by analyzing internal and external factors. The results of the strategy in this study are alternative strategies such as creating content that highlights advantages, creating promotions so that tourists share their excitement, conducting digital marketing training for administrators, collaborating with influencers and tourist information accounts, utilizing content generated by UGC users, optimizing SEO, and forming a team.

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

Revealing the pattern of causality between tourist experience and the perception of sacredness at shamanic heritage destinations in Northeast China

For religious heritage tourism, the key to a destination’s reputation lies in enabling visitors to fully experience the sacredness or a unique spiritual experience offered by the site. The authenticity of the tourism experience and perception is the most significant factor influencing visitors’ perception of the sacredness of religious or holy sites. However, there is currently a lack of sufficient references and evidence to explore the relationship between tourists’ authentic experiences and their perception of sacredness in religious heritage tourism. The aim of this study is to elucidate the causal relationship between the characteristics or attributes of tourists’ authentic experiences and their perception of the sacredness of the destination. Furthermore, it seeks to explore resource development and site creation strategies for religious tourism destinations to continuously enhance the sense of sacredness of these places. The Shamanic cultural traditions in the traditional settlements of Northeast China provide a subtly different context for this study. However, the topics discussed have broad applicability in constructing the network of relationships between the characteristics of tourists’ experiences and their perception of sacredness in religious heritage tourism. This study applied the causal decision analysis model based on data mining (RSA-DEMATEL) and established a causal network diagram between core attributes based on clarifying the behavioral rule knowledge (i.e., If…Then… rules) between tourists’ travel experience (conditional attributes) and the perception of place sacredness (decision-making attributes). The study finds that enhancing tourists’ perception of the sacredness of a destination is related to authentic tourism experiences, the ability to observe believers and participate in the religious activities of local residents, landscapes that reflect local traditions and historical culture, as well as the number of tourists and the extent of commercial activities. This research offers further insights into the sustainable development of religious tourism, providing a critical conceptual framework and data analysis model for subsequent related studies, contributing significantly to both theory and practice.

Inundation and evacuation of shoreline populations during landslide-triggered tsunamis: an integrated numerical and statistical hazard assessment

Abstract. The volcanic island of Stromboli (southern Tyrrhenian Sea, Italy) is renowned for its persistent, periodic, low-intensity explosive activity, whose spectacular manifestations attract tens of thousands of tourists every year. However, sporadic more intense major explosive and effusive eruptions and paroxysms pose serious threats to the island. In addition to direct hazards, granular slides of volcanic debris and pyroclastic avalanches, which can rapidly reach the sea and potentially generate tsunamis, are often associated with such unpredictable eruptive activity. Due to the very fast propagation of the tsunami around the island and the consequent short tsunami warning time (ranging from less than a minute to only a few minutes), mitigation efforts and evacuation from the Strombolian coast must be carefully planned. In this paper, we describe a new GIS-assisted procedure that allows us to combine the outputs of an ensemble of 156 pre-computed landslide-generated tsunami hazard scenarios (with variable landslide volume, position, and density), statistical exposure data (i.e. the number of inhabitants and tourists), and digital geographic information to obtain a quantitative (scenario-based) risk analysis. By means of the analysis of the road network and coastal morphology, we develop a model with routes and times to reach a safe area from every pixel in the inundated area and an appraisal of the time needed to escape versus the wave arrival time. This allows us to evaluate and quantify the effectiveness of potential risk mitigation by means of evacuation. The creation of an impact score linking the predicted inundation extent and the tsunami warning signals is intended, in the long term, to be used to predict the intensity of future tsunamis and to adapt evacuation plans accordingly. The model, here applied to Stromboli, is general and can be applied to other volcanic islands. Evacuating an island hosting several thousand tourists every summer with very little warning time underlines the absolute necessity for such mitigation efforts, aimed at informing hazard planners and managers and all other stakeholders.

Protected areas shape the distribution of tourism across rural Europe

Abstract Protected areas (PAs) have expanded rapidly in recent decades to help mitigate the ongoing biodiversity crisis but are under increasing human pressures that jeopardize their conservation effectiveness. Tourism in PAs may galvanize efforts towards biodiversity conservation, but it can also be a major source of threats, leading to multiple adverse social and ecological impacts. Considering the recent Kunming‐Montreal Global Biodiversity Framework, which aims to protect at least 30% of terrestrial ecosystems by 2030, understanding and assessing how and which PAs shape the spatial distribution of tourism is of great importance to biodiversity management and policy. In this study, we used a geospatial data set that describes the location and capacity of tourism accommodation in 81,185 local administrative units (LAUs) covering over 28 countries across rural Europe. After estimating the number of nights spent per LAU, we modelled the influence of PAs on the distribution of tourism throughout Europe while controlling for other social, economic and environmental covariates, but also for spatial autocorrelation. We reveal a positive link between highly protected PAs and the number of nights spent by tourists in LAUs. The attractiveness of these PAs for tourists may pose a conservation paradox, that is, highly protected PAs, which aim to safeguard nature tend to attract a disproportionate number of tourists, which may lead to nature degradation. Read the free Plain Language Summary for this article on the Journal blog.

Attendance patterns of provisioned Australian humpback dolphins (Sousa sahulensis) in Tin Can Bay, Australia – Further indication of male bonding and alliance

Tin Can Bay, Queensland, is one of four free-ranging dolphin feed provisioning sites in Australia and the only one featuring Australian humpback dolphins (Sousa sahulensis). The site attracts the same 5–10 dolphins a year, most of which return regularly, and some which have been in attendance since the 1990s. Historical records were obtained for the period March 2010 to March 2021 covering dolphin attendance to the provisioning station including dolphin name, arrival time, and departure time. Tourist numbers were also obtained which included the total number and how many of that total directly took part in provisioning. Attendance of dolphins to the provisioning site has increased over the years both in frequency and total numbers. There was evidence of group cohesion between four particular dolphins (one mother-offspring pair and one male pair). The strongest pairing was seen between two males, indicating bond formation in adult male humpback dolphins, which may be indicative of an alliance. Specific events were also looked at in detail including cyclones, flooding COVID-19 restrictions, which reduced dolphin and/or human attendance.

THE IMPACT OF IRANIAN FILMS AND TELEVISION SERIES ON TURKISH AND AZERBAIJANI INBOUND TOURISTS

Recently, Iran has employed different methods to attract international tourists, with film tourism emerging as one of the most effective strategies, owing to the export of television (TV) series and films. With this tool and approach, Iran increased the number of tourists and became a unique country to visit owing to the shared language, culture, and special geographic location with over 15 countries, including two independent and disconnected states in Iraq, Azerbaijan, Kurdistan, and Nakhichevan, alongside 27 heritage sites. Iran achieved remarkable success in exporting TV series and films. The ‘film-induced tourism effect’, which refers to the increase in demand for travel to Iran, is widely thought to be one of the most significant outcomes of exporting TV series and films. This relatively new field explores how TV series and films affect travel demand. The main purpose of this study is to analyse the impact of TV series broadcasting in other countries with cultural similarities such as language. For this, we examine the influence of Turkish and Azerbaijani citizens visiting Iran. The results indicate that visitors from both countries were affected by Iranian TV series post-2018.

Evaluating tourism scenarios within the limit of acceptable change framework in Barcelona

This article introduces an expanded Limit of Acceptable Change model, enhancing the traditional approach by incorporating various tourism scenarios and a broader range of indicators. This approach allows to gain a more comprehensive understanding and foresee the effects of diverse tourist profiles and intensities of use in Barcelona. It considers factors like motivation, origin, and accommodation type, and evaluates their effects on key sustainability indicators. The study reveals that significant shifts in tourism patterns are required to impact these indicators noticeably, emphasizing the need to consider both tourist numbers and typologies in sustainable tourism management. This approach equips destination planners with a valuable tool for strategic decision-making and long-term planning.

The Dynamics of Tourism, Economic Growth, and CO2 Emissions in Uzbekistan: An ARDL Approach

In this study, we examined the impact of key macroeconomic variables on the sustainability of tourism in Uzbekistan using open data from the World Bank from 2000 to 2023. The selected variables for the study include the number of international tourists, GDP, CO2 emissions, and exchange rates. Considering the importance of climate issues in ensuring sustainable tourism, the study combines these factors using the autoregressive distributed lag (ARDL) model to assess their long-term relationships. We chose this model due to its flexibility in dealing with variables of different integration orders. The results of the bounds testing procedure confirm the existence of a long-term cointegration relationship between the dependent variable (international tourist arrivals) and the selected macroeconomic indicators. Specifically, the study results indicate that GDP growth, exchange rate fluctuations, and CO2 emissions positively affect tourist arrivals to Uzbekistan. The findings of this study underscore the importance of infrastructure development and macroeconomic stability in attracting international tourists while emphasizing the need for sustainable international practices to mitigate environmental impacts. This study contributes to understanding the complex interrelationship between tourism development and macroeconomic variables in the context of developing countries and aids decision-making in this area.

A Novel Approach for Forecasting Tourist Arrivals Using Web Search Data and Artificial Intelligence

The development of economic activity has been matched by growth in the tourism industry. According to information, the tourism industry is growing and both the number of domestic and international tourists visiting each year is expanding. Because of this quick expansion, there are now critical complications with the management of tourism, such as predicting the arrivals for travel, particularly when a lot of people are visiting appealing locations for particular periods. The proposed Artificial Fish Swarm Optimized Dynamic Gated Recurrent Unit (AFSO-DGRU) approach transforms the forecasting of demand for tourism by utilizing intelligence from swarms to improve forecasts and strategically adapting to fluctuating visitor structures. It ensures accurate and dynamic responses even during times of uncertainty when demand is high. The study used Google Trends to collect data from searches on the web and examine trends in tourist’s interest and demand for travel. By combining innovative artificial intelligence (AI) algorithms with realtime online search data, this study presents a novel way to improve the accuracy of visitor arrival predictions. The proposed method performs better than the existing methods to utilize the parameters such as mean absolute deviation called MAE (42.01), mean square error denoted by MSE (3059.85), mean absolute percentage error defined MAPE (1.34), and RMSPE or root mean square percentage error (1.43). This research utilizes web search data and AI to improve the accuracy of forecasting tourist arrivals, offering valuable insights for understanding tourism trends.

Determining Tourism Area Using TOPSIS Analysis in the Dampier Strait Conservation Area

The Dampier Strait regional conservation area is one of the conservation areas included in the Raja Ampat Regency marine conservation area, which has high potential for developing tourism activities. The most important aspect in the use of natural resources for tourism purposes is their suitability to support tourism activities. The challenge faced is the difficulty if the potential for beach tourism, snorkeling and diving to be developed simultaneously. The aim of this research is to determine areas for ecotourism and recommend appropriate locations. Data analysis is determining ecotourism areas using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is used because it is able to rank the selected alternatives that are closest to the positive ideal and furthest from the negative ideal solution. The criteria used are tourist carrying capacity, accommodation, accessibility, number of international tourists, number of domestic tourists and suitability area. The results of this TOPSIS analysis are that Sauwandarek, Marandanweser and Arborek are areas that can be used as an alternative area for carrying out beach tourism, snorkeling and diving activities. Areas that can be developed into ecotourism activities are Yeben Kecil and Tanjung Putus.

Analysis of the Influence of the Tourism Sector on the Level of Labor Absorption in Central Lombok Regency 2002-2022

This study aims to determine the influencenumber of hotels, number of tourists, number of tourist attractions, and number of restaurants on the level of labor absorption in Central Lombok Regency both simultaneously and partially. This study uses secondary data taken from the official website of the Central Statistics Agency of Central Lombok Regency and the Culture and Tourism Office in 2002-2022. This study uses a data analysis method in the form of multiple linear regression analysis using the SPSS application as an analysis tool. The results of this study indicate that the variables of the number of hotels, number of tourists, number of tourist attractions, and number of restaurants simultaneously affect the level of labor absorption in Central Lombok Regency. Partially, the variable number of hotels has a negative and significant effect, the number of tourists has a negative and insignificant effect, the number of tourist attractions has a positive and insignificant effect and the number of restaurants has a positive and significant effect on the level of labor absorption in Central Lombok Regency in 2002-2022.

Focus Group Discussion and Workshop on Exploring the Tourism Potential of Jumpinang River Tubing Pasuruan

This community service program aims to improve the quality of management and promotion of Jumpinang River Tubing tourist destinations in Pasuruan through the use of digital technology. Implementation methods include problem identification, website creation, and guiding and safety training for local communities. This process was carried out for 3 months, with core activities in the form of Focus Group Discussion (FGD), website development, and technical training. The results of the program show that the community's capacity is increased in the management of tourist destinations, as well as the ease of promotion through digital platforms. The implication of this program is the increase in the number of tourist visits and the strengthening of the sustainability of tourism management by the community.

Pengaruh Sektor Pariwisata terhadap Pengangguran di Provinsi Bali

The tourism sector plays an important role in the economy of Bali Province. The aim of this research is to analyze the influence of the number of tourist visits and length of stay on unemployment in Bali Province. This research design uses an associative quantitative method which aims to determine the relationship between the number of tourist visits and length of stay on the number of unemployed in Bali Province in 2004-2023. The number of observations in this study was 20 years. The data analysis technique in this research is to use multiple linear regression analysis. The results of the analysis show that the number of tourist visits has a negative and significant effect on the number of unemployed in Bali Province in 2004-2024. Length of stay is negative and significant on the number of unemployed in Bali Province in 2004-2024. The number of unemployed in Bali Province in 2004-2024 can be explained by 73.5 percent by the variables of number of tourist visits and length of stay. Although the number of tourist visits and length of stay have a significant influence on the unemployment rate in Bali, the challenges faced in the tourism sector require a more comprehensive approach.

The Impact of CHSE (Cleanliness, Health, Safety, and Environmental Sustainability) on Tourist Satisfaction Levels and Revisit Intentions in Dusun Semilir, Semarang Regency

There was a drastic decline in the number of tourists in 2020 and 2021 due to the COVID-19 virus. This situation encourages tourist destination managers to find and formulate the best strategies to survive and recover from this difficult situation. One of the policies implemented by the manager is the implementation of CHSE. The purpose of this study is to investigate the relationship between CHSE and the satisfaction and intention of tourists to return to Semilir Hamlet. This research is included in the quantitative category. Data sources were obtained through interviews and questionnaire distribution. The sampling method used was purposive sampling, resulting in 108 respondents. The data analysis technique was carried out using Smart PLS software. The results showed that the CHSE variable had a significant influence on the intention to return to visit. In addition, the CHSE variable also contributes positively and significantly to tourist satisfaction. Furthermore, the level of tourist satisfaction has a positive and significant effect on the intention to return to Semilir Hamlet. The results of the analysis show that the CHSE variable has a positive and significant impact on the intention to visit again through tourist satisfaction. Thus, it can be concluded that the implementation of CHSE has a great influence on tourist satisfaction and their intention to visit Semilir Hamlet again.

Research on tourism demand forecast based on BP neural network -- Taking the number of tourists in Mulan Scenic spot in Huangpi District for example

With the development of the economy and society, the public's demand for tourism is getting higher and higher, followed by the environmental carrying capacity of the destination, the bearing capacity of traffic reception facilities, and the psychological carrying capacity of tourism residents being affected by different degrees. This paper took the analysis of the number of tourists in Mulan Scenic Spot in Huangpi District for the May Day holiday as an example, combined with the BP neural network algorithm, forecast the tourism demand, and put forward the local government's suggestions on holiday tourism management based on the data research.

Forecasting Hotel Occupancy Rates in Bali Province using the SARIMAX Method with Tourist Data as an Exogenous Variable

Tourism is a crucial economic sector in Bali, Indonesia. Sustainable tourism management requires an understanding of the dynamics between tourist numbers and hotel occupancy levels. This study uses the SARIMAX (Seasonal Autoregressive Integrated Moving Average) method to estimate between the two indicators and reveals a positive correlation between the two indicators. The SARIMAX model effectively captures seasonal patterns and external factors, providing accurate forecasts and supporting tourism management in Bali. Monthly data from 2010 to 2023 were analyzed. Accurate estimates can help tourism stakeholders in formulating appropriate management strategies to optimize the tourism sector. Implementing the right strategy can help ensure the preservation of the local environment and culture, as well as long-term economic benefits for Bali. From the data we use the SARIMAX (6,1,0) (1,1,0)12 model with an AIC value of 1920.553 and a MAPE value of 27%.

The Coupling Coordination Relationship and Driving Factors of the Digital Economy and High-Quality Development of Rural Tourism: Insights from Chinese Experience Data

Globally, rural tourism development faces challenges such as inadequate infrastructure, insufficient marketing resources, and unreliable service quality, all of which limit its potential. However, digital technology offers unprecedented opportunities to address these barriers. China’s experience in integrating digital technology into rural tourism provides a valuable case study for understanding the digitalization of rural tourism. This study constructs an index system to assess the coupling coordination relationship between the digital economy and the high-quality development of rural tourism (HQDRT). By employing methods such as the entropy method, coupling coordination degree model, obstacle factor model, and geographic detector, the study examines the evolution of this coupling coordination relationship and its driving mechanisms across 31 provinces (including regions and municipalities) in China from 2012 to 2021. The findings reveal that (1) The development of the digital economy generally lags behind that of the rural tourism, but the coupling coordination relationship between the two is steadily improving. (2) The level of coupling coordination increases from west to east, with spatial distribution patterns evolving from ‘antagonism’ to ‘adaptation’ and then to ‘coordination’ as they move eastward. Most provinces belong to the ‘adaptation’ type. (3) From a nationwide perspective, the primary obstacles impeding the development of the digital economy include an insufficient internet penetration rate, which consequently leads to underdeveloped internet finance development and telecommunications industry development. The major barriers to the HQDRT stem from an inadequate number of tourists and a lack of physical infrastructure. (4) Population density, consumer spending, and R&D are significant drivers of the coupling coordination relationship, with the interaction between urbanization rates and other factors generally weakening the degree of coupling.