Travel Reference
In-Depth Information
related to the various problems associated with the traditional methods employed in the
gathering of information on tourists' spatial and temporal behaviour. The most common
problems relate to the level of accuracy and/or the validity of the data collected. This dearth
of research is especially troubling given that it is widely recognised that the movement of
tourists has profound implications for infrastructure and transport development, tourism
product development, marketing strategies, the commercial viability of the tourism industry
and the management of the social, environmental and cultural impacts of tourism. Past
research has focused primarily on the movement of tourists between destinations or from
source markets to destination areas, applying concepts of distance decay, market access and
the valuation of time.
Methodological problems have prevented most researchers from undertaking similar
studies of smaller areas, such as urban destinations. In recent years, the rapid development and
availability of small, cheap and reliable tracking devices has led to a growing volume of spatial
research in general and in tourism studies in particular (see also Hall,
Chapter 21 o
f this
volume). GPS devices offer researchers the opportunity of continuous and intensive high-
resolution data collection in time (seconds) and space (metres) for long periods of time; this
was never possible before in spatial research (Shoval and Isaacson, 2010). In recent years a
growing body of work has demonstrated the effi cac y of usi ng t rack i ng tech nolog ies to ex plore
leisure and tourist activities; see for example Ahas
et al.
, 2007, 2008; Arrowsmith and Chhetri,
2003; Harder
et al.
, 2008; Modsching
et al.
, 2008; Shoval, 2007; Shoval and Isaacson, 2007a,
2007b, 2010; Spek, 2008).
New methods for data analysis that enable time geography
quantitative analysis
The introduction of GIS in tourism research about a decade ago created a spectrum of oppor-
tunities for handling time-space data and enabled advanced analysis of time-space data.
However, one fundamental problem in space-time analysis is the aggregation of space-time
paths to create generalised types composed of varied activities in order to identify patterns
fashioned on a quantitative basis while taking into account the sequential element. This
problem could not be dealt with using GIS software. Previous attempts with quantitative
pattern aggregation methods, mainly by transport researchers (for examples see Schlich and
Axhausen, 2003) did not manage to tackle the issue of the sequential element. Understanding
the sequence of activities in space and time allows one to understand an additional integral
dimension of activity and to recognise patterns that exist within this dimension.
One example of analysis that has very promising potential for creating typologies of
tourists based on their spatial behaviour while taking into account the sequence of locations
can be seen in SAM. These methods, which have been used since the 1980s, were introduced
to the social sciences by Abbott (1995) and Wilson (1998) and to the spatial sciences by
Shoval and Isaacson (2007b) and Wilson (2008). The methods, which have developed with
time and have been refi ned to more accurately compare sequences, have tremendous potentia l
as a tool for creating typologies of tourists by analysing their spatial activity.
Figure 22.2
presents the outcome of such an analysis that identifi ed a typical group of visitors to the Old
City of Akko in Israel. It demonstrates the average time-space path of a group of visitors and
is calculated taking into account the time and the order in which they visited the different
parts of the city.
The introduction of tracking devices, including GPS, and analytic software, such as GIS
software and SAM, has created new opportunities to obtain and analyse accurate information
