Visitor Travel Research Articles

Mapping the Distance: An Analysis of Visitor Travel Distance to German National Parks and Biosphere Reserves

The ecologically valuable and unique natural and cultural landscapes of protected areas are popular nature tourism destinations. From a spatial perspective, travellers visit protected areas by moving from their place of origin to the destination for a temporary stay. This paper analyses the travel distances of day visitors and overnight guests to German national parks and biosphere reserves. National parks position themselves as both regional and national destinations. In contrast, biosphere reserve tourism is more regionally concentrated due to the proximity of biosphere reserves to urban centres, which are the primary places of origin for visitors. On average, visitors travel 278.6 km by car to visit a national park, taking 172.1 minutes (02:52 hours). Opting for public transport increases the average travel distance to 312.5 km, with a travel time of 284.5 minutes (04:43 hours). Visitors to German biosphere reserves travel 208.7 km by car, requiring 134.7 minutes (02:15 hours). When using public transport to visit biosphere reserves, the average distance increases to 234.0 km, with a travel time of 239.1 minutes (03:59 hours). The automated, GIS-supported distance analyses offer an innovative approach for assessing nature tourism catchment areas and the accessibility of protected areas, serving as a basis for effective spatial planning, development, and regional marketing.

MCI모형을 이용한 중국 옌타이시 대형소매점 매출예측 및 상권영향분석

Recent research on GIS analysis, spatial interaction models, and site selection for large retail stores is actively underway in China both domestically and internationally. However, most studies tend to rely on secondary data provided by government agencies, neglecting research based on actual consumer survey data. Therefore, this study aimed to improve methods for analyzing commercial areas of large-scale retail stores in China by proposing the use of consumer data to enhance existing spatial interaction models. Specifically, by conducting surveys among consumers who actually visit large-scale retail stores, basic data such as store visit rates and visitor travel distances were collected to construct the MCI model. Through this approach, the study estimated the visit probability and market share of large-scale retail stores, and analyzed the impact of market environmental changes on commercial areas. Firstly, based on address data from 279 Chinese consumers, the study area was divided into 44 regions. These regions were combined with information on consumer shopping patterns and store details to construct a matrix of interactions between regions and large-scale retail stores. Secondly, important parameters in the MCI model, such as store attractiveness index and distance sensitivity, were estimated through OLS regression analysis. Findings revealed that the store type (regional large-scale retail, urban large-scale retail) did not significantly impact on consumers’ store preferences. Instead, customer visits exhibited an inverse relationship with distance and a direct relationship with store size. These estimated results were consistent with the theoretical perspectives of spatial interaction models. Thirdly, to examine how changes in the retail environment may affect retail businesses within commercial areas, the study focused on changes in sales and market share of surrounding retail businesses when a specific store expanded its area or entered a new region. The results showed that both expanding store area and entering new regions had negative impacts on other existing stores.

Carbon footprint of a laser unit: a study of two centres in the UK.

Climate change has serious consequences for our wellbeing. Healthcare systems themselves contribute significantly to our total carbon footprint, of which emissions from surgical practice are a major component. The primary sources of emissions identified are anaesthetic gases, disposal of single-use equipment, energy usage, and travel to and from clinical areas. We sought to quantify the waste generated by laser surgery which, to our knowledge, has not been previously reported. The carbon footprint of two laser centres operating within the United Kingdom were measured. The internationally recognised Greenhouse Gas Protocol was used as a guiding framework to classify sources of waste and conversion factors issued by the UK government were used to quantify emissions. The total carbon footprints per day at each unit were 299.181 carbon dioxide equivalents (kgCo2eq) and 121.512 kgCO2eq, respectively. We found the carbon footprint of individual laser treatments to be approximately 15 kgCO2eq per procedure. The biggest overall contributor to the carbon footprint was found to be the emissions generated from staff, patient and visitor travel. This was followed by electricity usage, and indirect emissions from physical waste and laundry. The carbon footprint of laser procedures was considerably less than the average surgical operation in the UK. This initial study measures the carbon footprint of a laser center in a clinical setting and allows us to identify where improvements can be made to eventually achieve a net carbon zero health care system.

An analysis of the impact and mechanism of new digital infrastructures on travel consumption behaviours of middle-aged and elderly households in China

ABSTRACT The significant integration of the ‘internet’ with ‘tourism’ has changed visitor travel motivations and customer behaviour. New digital infrastructure currently provides substantial technical support for deep integration between ‘the internet’ and ‘tourism’. In this work, the HLM model is used to match and merge household-level microdata with regional-level data. The goal is to identify the mechanism underlying the impact of China’s new digital infrastructure on the travel consumption habits of Chinese households. Our analysis shows that: (1) Middle-aged and older households will be much more likely to travel, and their travel expenses will also be more intense, as the intensity of new digital infrastructure increases. (2) The development in the degree of new digital Infrastructure will effect the tourism spending behaviour of middle-aged and senior people by boosting their Internet usage.

Workshop Synthesis: Innovations in travel survey collection to support long distance and tourism travel analyses

As researchers and practitioners seek to better understand and proactively plan for long-distance travel demand, they need data that characterizes the breadth and depth of long-distance travel decisions. In this workshop, we reviewed work from five presentations to draft guidance for integrating these sources to support travel analyses. Our workshop specifically considered (a) what data is needed for long distance and tourism travel analyses (including traveler profiles, trip characteristics, external travel, visitor travel, destination choices, mode choices, environmental impacts, economics, etc.), (b) the most appropriate methods for capturing this data, and (c) how to best integrate these different methods.

Changes in the attraction area and network structure of recreation flows in urban green, blue and grey spaces under the impact of the COVID-19 pandemic

Despite increasing attention to the impact of the COVID-19 pandemic on outdoor recreation, there is limited knowledge about its effects on visitor travel across cities for recreation and changes in the recreation response of blue, green and grey spaces within cities. This study examines changes in the attraction area of inter-city recreation flows within Wuhan, China, before and after the COVID-19 pandemic. The results showed a significant decrease in average attraction distance from 861.74 km to 787.77 km after the pandemic, while attraction directions remained stable. Intra-city recreation routes tend to be simplified, with average length decreasing from 40.86 km to 30.52 km and node number of recreation routes reducing from 7.02 to 5.00. However, the proportion of blue nodes in recreation routes increased significantly from 17.45 % to 23.25 %, while the proportion of grey nodes decreased noticeably from 57.36 % to 52.75 %. The proportion of green nodes did not show a significant change, but when the number of nodes exceeded 6.18, the proportion of green nodes slightly decreased below pre-pandemic levels. With the increase in the length of recreation routes, the proportion of grey nodes gradually converged to around 42 % from around 54 % before the pandemic. The total proportion of agglomeration and diffusion nodes increased from 13.71 % to 20.01 %, while the balanced nodes decreased from 8.87 % to 6.93 % after the pandemic. Notably, 35.71 % of the functional nodes before the pandemic remained their original functional type after the pandemic, but 7.14 % of functional nodes converted to weak function. These findings can assist tourism departments and scenic spot managers in developing strategies that balance pandemic control and recreation needs.

Effects of Maintaining Social Distancing on Travel Intentions During Covid-19: Practical Implications to Tanzania's Tourism Sector

Although social distancing rule seem to be the effective measure to reduce the spread of the virus pandemic related diseases, the effectiveness of this rule varied country wise. The main focus of the current study was to analyze studies that have been done addressing relationship between social distancing and visitor's travel intention during COVID-19 and to come up with a practical implication to Tanzania tourism sector. This was a desk review study and only full-length articles published in academic journals, mainly in tourism and hospitality were used. Data for this study was generated from three main tourism database search engines as EBSCOhost, science direct, and Google scholar using keywords such as “social distancing” “tourism” “COVID-19” “intention to travel” “hospitality”. The search retrieved a total of 7 articles. It was reported that perceptions of COVID-19 and the use of non-pharmaceutical intervention did not have an impact on behavioral intention. Individual desire was proved to have a positive effect on behavioral intention. Furthermore, the behavioral intention was negatively influenced by the affective risk and peoples’ emotions. During virus pandemic related diseases Tanzania need to emphasize on the use of technology and seat blocking strategy in transport sector to make sure that social distancing rule is fully maintained.

Evaluating economic impacts of protected areas in contexts with limited data; the case of three national parks in Iceland

Economic impacts of protected areas (PAs) are receiving more attention in recent years and methodology in this area is advancing. Multiple studies have illustrated that PAs are a potent land use strategy to generate multiple and direct economic benefits. These benefits are driven by tourism as the central economic activity in PAs worldwide. This study takes the case of Snæfellsjökull, Vatnajökull and þingvellir National Parks (NPs) in Iceland, characterized by limited regional economic data and multi-destination and -purpose visitor travel patterns. Its main objective is to advance understanding of the economic impacts related to PAs in the context of limited data availability. Our analysis is based on the widely used Money Generation Model (MGM2) -methodology, localized to the Icelandic context by using Icelandic labour data and national input-output (I–O) tables regionalized using the Flegg Location Quotient (FLQ). We provide a consistent approach for handling multi-destination and -purpose trips, and separating spending data between local and overall impacts. Based on 2019 visitor and economic data, the visitors (N = 2087) spent, on average, $113 per day in the parks and generated estimated total economic impacts between $30–99 MM with 347–1140 jobs generated across the study sites. For example, in Vatnajökull NP's southern region, the jobs supported locally by the park constituted 36% of all the jobs in the municipalities. Combined tax revenue to the state from the three parks was $88 MM. The localized methodology generated similar economic impacts as earlier studies but showed that employment impacts were previously overestimated by the default models. Our approach and findings can be used as a reference for others applying the MGM2 or similar methods, and they support policy development, decision-making and informed discussion between researchers, practitioners in PA and tourism management, municipalities and communities around PAs. Being able to show economic impacts is increasingly important for PAs to ensure sustained funding amid budget cuts and the transition of government bodies to business units. Limitations of the study include a lack of winter data for Vatnajökull and þingvellir NPs and broad categorization of the Icelandic economic data used in the I–O table regionalization. In further research, a comprehensive sustainability analysis is needed to complement the economic impact analysis and site-specific factors could be analysed in more detail.

Utilization of GPS Data Loggers and GIS Analysis for the Evaluation of Interpretive Signage to Influence Visitor Travel Patterns and Enhance Wayfinding in Parks and Protected Areas

Interpretive signage is often used to influence visitor travel patterns and assist in wayfinding in parks and protected areas. However, there are few tools for managers to objectively evaluate the effectiveness of different interpretive signage. This study was conducted to demonstrate the utility of using GPS data loggers as an evaluative tool to determine if signage influences visitor concentrations, visitors’ time allocation, and visitor travel directionality. Researchers distributed GPS data loggers to all travel parties who voluntarily participated. Analysis revealed that GPS data loggers and complimentary GIS analysis were useful for evaluating the influence of different signage treatments on visitor patterns of use, location of use, and timing of use. These findings suggest that managers tasked with interpretation and visitor orientation consider using GPS trackers and GIS to better understand their visitors’ and plan where to focus messaging and interpretive efforts to meet managerial goals and objectives.

Conducting Visitor Travel Surveys: A Transit Agency Perspective

Visitors represent an important potential market for transit. Unfortunately, since visitors generally lack local phone numbers and residential addresses, they constitute a hard-to-survey population, and relatively little is known about their travel behaviors and preferences. Transit agencies seeking to better understand and better serve this market will need to conduct a visitor travel survey. Surprisingly few visitor travel surveys have been undertaken, and none have been designed expressly to meet the needs of transit agencies. This research addresses this gap in the public transportation planning literature by identifying and exploring the key issues a transit agency needs to consider to carry out an effective visitor travel survey.

Conceptualizing Tourist Journey: Qualitative Analysis of Tourist Experiences on TripAdvisor

ABSTRACT This study aims to conceptualize tourist journey through analyzing tourist experiences. In this study, the connotation and important criteria of the Eiffel Tower visitor travel experiences were identified using a qualitative approach. The statistical population of this study includes the visitors who posted online reviews on TripAdvisor. The data was extracted using the python program script. Data analysis was performed using the content analysis method with the help of the Maxqda software. The results show that considering structural and scenic attractions, person- environment fit and supplementary need factors in the Pre-trip stage; environmental, social, and service space factors in the entrance stage; Value for money and payment methods factors in the payment stage can affect tourist experiences and their behavioral intentions. This study is a pioneer work to qualitatively create a model for the concept of the tourist journey by examining the experience of tourists. Theoretical and practical implications are discussed.

Carbon dioxide emissions from international air transport of people and freight: New Zealand as a case study

International aviation accounts for over 2% of global emissions, but was not subject to Kyoto Protocol liabilities, and was not directly addressed by the Paris Agreement. Calculating emissions associated with individual countries is complicated, with data that is publicly available and free to access often being difficult to obtain. In this paper, a case study is presented where commercially sensitive fuel uplift is used to calculate New Zealand specific emissions factors of 0.81 kg CO2 per tonne km (CO2 per t-km) for short-haul and 0.79 kg CO2 per t-km for long-haul international aviation. This was used to estimate international aviation CO2 emissions associated with New Zealand in 2017 to be 8.4 Mt CO2 in total (2 significant figures, rounded down), with international visitor travel to and from New Zealand accounting for 4.3 Mt CO2, New Zealand residents’ international travel for 2.6 Mt CO2, exports for 0.72 Mt CO2, and imports for 0.89 Mt CO2 (all 2 significant figures, rounded up). Results show the fleet of aeroplanes which serviced New Zealand between 2007 and 2017 has become, on average, less efficient due to changes in operational factors such as seating density.

Identifikasi Pengunjung Pada Pantai Panrita Lopi, Kecamatan Muara Badak

Panrita Lopi Beach is one of the natural tourist destinations that needs to be surveyed by visitors in order to identify and determine strategies for developing tourist attractions and also marketing in Kutai Kartanegara Regency. This study aims to analyze the characteristics of tourists and travel patterns of tourists who visit and to determine the perception of the level of tourist satisfaction with infrastructure products, public facilities and tourism facilities in this tourist destination. The main variables used in segmenting comprise: Geographic Segmentation, Demographic Segmentation, Psychographic Segmentation, and Behavior-based Segmentation. This research is a type of survey research with a qualitative descriptive approach. I carried out data collection from primary data sources and secondary data sources. The sample used in this study is a non-probability sampling technique by accidental sampling, namely a sampling technique based on chance, anyone who coincidentally meets a researcher can be used as a sample, if it is deemed that the person who was met is suitable as a data source. Data that has been collected from research results related to typology (characteristics, travel patterns) and visitor perceptions can be analyzed descriptively qualitatively. The analyzed data comes from surveys through questionnaires that have been given to visitors, including data on visitor profiles, visitor travel patterns, and visitor perceptions about this Panrita Lopi beach.

Determinants of visitor climate change risk perceptions in Acadia National Park, Maine, USA

Nature-based tourism is one of the most economically important industries in the state of Maine, USA. Climate change impacts are projected to affect important tourism assets in Maine, which could result in behavioral shifts related to destination selection, seasonal visitation, and activity participation. Risk perceptions can be important predictors in visitor travel decisions. Recent tourism studies have focused on the effects of climate impacts on risk perceptions, but few have examined the social-psychological drivers of climate change risk perceptions. Drawing on social-psychological theories, we address this gap by understanding visitor climate change risk perceptions in Maine. We surveyed visitors to Acadia National Park in the summer of 2018 to assess the impact of socio-demographics, cognition, experience, and socio-cultural factors on visitor climate change risk perceptions. We used two-stage cluster probability sampling and intercepted 1317 visitors on site; 480 participants completed the online follow-up survey. Using hierarchical regression, we explained 45.5% of the variance in visitors’ climate change risk perceptions at a nature-based tourism destination. Visitors identifying as female, having higher levels of belief in climate change, more first-hand experience with climate impacts, and a higher altruistic values orientation amplified risk perceptions. Understanding determinants of climate change risk perceptions within an outdoor recreation setting has implications for offering high quality visitor experiences while maintaining the integrity of the natural resource base upon which visitation relies. Risk perceptions can be important predictors of visitor travel behavior. Climate change is expected to impact nature-based tourism visitor experiences and travel decisions, natural and cultural resources that serve as attractions, and visitor safety in protected areas. Understanding the drivers of climate change risk perceptions are important for managing visitors and their recreation behaviors while providing satisfactory tourism experiences. If park managers and other tourism stakeholders want to convey information about climate change with the goal of influencing perceptions and behaviors, we suggest that they focus on visitors’ past experiences with climate change impacts and appeals to altruistic values, rather than solely providing climate change facts. Understanding how to motivate visitor compliance with park policies and visitor resource use guidelines will be critical in maintaining positive visitor experience in nature-based tourism settings and protecting the integrity of natural and cultural resources from changes in visitation, such as increased tourist numbers. • Climate change risk perceptions will likely influence visitor behaviors. • We sampled visitors to Acadia National Park to measure social-psychological factors contributing to climate change risk perceptions. • Identifying as female, belief in climate change, experience with climate impacts, and altruistic values increased visitors' risk perceptions.

Health care's response to climate change: a carbon footprint assessment of the NHS in England

SummaryBackgroundClimate change threatens to undermine the past 50 years of gains in public health. In response, the National Health Service (NHS) in England has been working since 2008 to quantify and reduce its carbon footprint. This Article presents the latest update to its greenhouse gas accounting, identifying interventions for mitigation efforts and describing an approach applicable to other health systems across the world.MethodsA hybrid model was used to quantify emissions within Scopes 1, 2, and 3 of the Greenhouse Gas Protocol, as well as patient and visitor travel emissions, from 1990 to 2019. This approach complements the broad coverage of top-down economic modelling with the high accuracy of bottom-up data wherever available. Available data were backcasted or forecasted to cover all years. To enable the identification of measures to reduce carbon emissions, results were disaggregated by organisation type.FindingsIn 2019, the health service's emissions totalled 25 megatonnes of carbon dioxide equivalent, a reduction of 26% since 1990, and a decrease of 64% in the emissions per inpatient finished admission episode. Of the 2019 footprint, 62% came from the supply chain, 24% from the direct delivery of care, 10% from staff commute and patient and visitor travel, and 4% from private health and care services commissioned by the NHS.InterpretationThis work represents the longest and most comprehensive accounting of national health-care emissions globally, and underscores the importance of incorporating bottom-up data to improve the accuracy of top-down modelling and enabling detailed monitoring of progress as health systems act to reduce emissions.FundingWellcome Trust.

Designing an advanced system for destination management: a case study of Northern Indiana

PurposeThe purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics.Design/methodology/approachThis study first proposes the conceptual framework for an advanced destination management system and then illustrates the core components of the proposed system using real-world data from Northern Indiana. In this study, search interests, devices used and other forms of website use derived from online clickstream data were merged with visitor demographic and tripographic information obtained from an online survey to develop an analytic model used to describe the core market structure.FindingsKey demographic factors (e.g. gender, age and income), search interests, referred websites, the number of total sessions, temporal aspects and spatial aspects of visitor travel provide essential information defining the structure and dynamics of the visitor marketing in Northern Indiana.Originality/valueThe process and data used in this study provide a “proof of concept” for developing highly personalized marketing systems, which can substantially improve the competitiveness of a destination management organization.

Identifying macroscopic features in foreign visitor travel pathways

Human travel patterns are commonly studied as networks in which the points of departure and destination are encoded as nodes and the travel frequency between two points is recorded as a weighted edge. However, because travelers often visit multiple destinations, which constitute pathways, an analysis incorporating pathway statistics is expected to be more informative over an approach based solely on pairwise frequencies. Hence, in this study, we apply a higher-order network representation framework to identify characteristic travel patterns from foreign visitor pathways in Japan. We expect that the results herein are mainly useful for marketing research in the tourism industry.

The Utility of GIS for Identifying Areas of Recreation Conflict for Targeted Interpretive Messaging

Past research has extensively studied interpretive messaging and visitor conflict within parks and protected areas. However, comprehensive understanding of how to identify trailside interpretive sign locations is lacking. The purpose of this study was to introduce an approach using geographic information systems (GIS) that supplements decision-making regarding sign placement. The study site was Grand Canyon National Park’s Rim to Rim (R2R) corridor. To identify sign placement locations, two analytical phases were conducted. First, GPS data loggers were distributed to visitors and their travel patterns were analyzed for spatial behaviors and spatial interactions that are known to influence the likelihood of recreation conflict. Specifically, locations with a high variance of visitor travel speeds and locations with concentrated visitor use were identified. Second, geographic data were analyzed to identify locations for a combination of features that together influence the likelihood of recreation conflict. Specifically, popular bidirectional trail segments with significant elevation change were identified. We reported these locations and areas using GPS coordinates for evaluation by future research. This research was a necessary step towards comprehensively understanding how to identify locations for interpretive signs.

Gut Microbes Take It to the Next Level? First Insights Into Farnesoid X Receptor Agonists of Microbial Origin.

The intestinal microbiome and its metabolites contribute to host (patho)physiology through signaling via host bile salt receptors(1) . To assess the overall chemical effects of the microbiome at the level of an entire animal, Quinn et al. conducted untargeted metabolome analysis of 29 different organs of germ-free and specific pathogen-free (SPF) mice. [Quinn RA, Melnik AV, Vrbanac A et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 2020;579:123-129] Unique microbial and metabolome profiles were evident along the GI tract.

Spatiotemporal variables to understand visitor travel patterns: A management-centric approach

Managers of parks and protected areas can use information about visitor travel patterns to conserve the biophysical resource and provide opportunities for quality visitor experiences. Visitor travel patterns are affected by the unique context of each park and protected area and can be assessed in a multitude of ways. Consequently, researchers have used numerous operationalizations and associated methods to understand visitor travel. However, during previous investigations, management input has not been fully taken into consideration to identify which spatiotemporal variables are the most important for understanding visitor travel patterns. In this study, the research team conducted semi-structured interviews and surveyed managers at Theodore Roosevelt National Park to determine which spatiotemporal variables were deemed the most important for understanding visitor travel patterns at the park, and distributed GPS data loggers to day visitors. Next, the spatiotemporal variables identified by managers were used to cluster travel groups using data collected by the GPS data loggers. Travel attributes of these groups were compared to gain more understanding of visitor travel patterns. Lastly, 3D geovisualizations that are intuitive and easy to understand were created for management purposes. A significant finding produced by this research was that managers at Theodore Roosevelt National Park identified three temporal variables as being the most important for understanding visitor travel patterns: total time spent at attraction areas, total time spent at the visitor center, and total time spent in the park.