Exploring the spatial patterns of car-based tourist travel in Loch Lomond and Trossachs National Park, Scotland

Abstract

Car transport is a vital element in tourism yet there is a surprising neglect of the role of the car on tourism travel patterns, behaviour and activities. This exploratory paper focuses on the use of itinerary mapping as a useful methodology to identify how aggregate travel flow as well as more detailed individual itineraries can be mapped and understood using a Geographical Information System (GIS). The focus of this exploratory research is the Loch Lomond and the Trossachs National Park, Scotland first National Park established in 2002, where a variety of itinerary patterns are revealed through a map-based questionnaire of visitors using National Park roads (n=749). The implications for planning and policy initiatives are discussed.

Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

“…Yet, empirical studies into tourist mobility have traditionally been rather scarce due to the labor-intensive and often expensive nature of traditional methods such as direct observation (Hartmann, 1988) or personal interviews (Kemperman, Borgers, & Timmermans, 2009). Space-time diaries (Connell & Page, 2008;Janelle, Goodchild, & Klinkenberg, 1988;Lau & McKercher, 2006) shift some of the weight away from the researchers but are often characterized by a low reliability as respondents tend to forget or neglect to register certain activities. Recently, however, tracking technologies offer a more scalable and objective way to capture spatiotemporal behavior in a detailed way (Shoval & Isaacson, 2009).…”

An analysis of visitor movement patterns using travel networks in a large marine park, north-western Australia

“…Such a holistic approach to investigating several modes of travel is novel as previous research has focused on a single type, i.e. travel by car (Connell & Page, 2008) or bus (Becken, 2005). The effect of visit and visitor characteristics was also explored.…”

Spatial and temporal modelling of tourist movements using Semi-Markov processes

“…A Semi-Markov Chain model can estimate the probability of visits at attractions; this data can provide policy-makers with a powerful tool to understand the way in which tourists travel in and around the parks and to allocate resources to meet the greatest needs of tourists (Connell and Page 2008). At Phillip Island, for example, park managers can use the outcomes of the transition probability matrix to estimate the number of tourists who transit from one attraction to another, i.e.…”