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Israel (a small historic city) and Hong Kong. Tchetchik
et al.
(2009) demonstrated how high-
resolution time-space data recorded by GPS units could be used to segment visitors to the Old
City of Acre heritage site in Israel.
Mobile phones also provide signifi cant opportunities for analysing tourist behaviour over
various scales (Asakura and Iryob, 2007). Girardin
et al.
(2008, 2009) used the distribution
and density of digital footprints to examine the attractiveness of urban space to visitors in a
case study of the area of the New York City Waterfalls public art project. Tiru
et al.
(2010a,
2010b) discuss the operation of a mobile-positioning-based online tourism monitoring tool
that uses as source data mobile operators' log fi les in which the starting locations of foreign
roaming clients' call activities have been stored. (The database is anonymous and the identity
of phones, phone owners and phone numbers is strictly protected pursuant to EU directives.)
Operators' data was evaluated to evaluate the extent of repeat visitation to Estonia by mobile
phone users (Tiru
et al.
, 2010b), thereby providing tourism data that was otherwise unavail-
able from other data sources.
Conclusion: which way to turn?
The mathematical modelling of movement over space has often been criticised because of the
perspective that 'most types of spatial modelling efforts are fatally fl awed because they fail to
account for the complex attitudes, preferences and tastes of individuals. These latter attributes
are infl uenced not only by personal circumstances and characteristics, but also by the cultural,
social and political milieu in which individuals make spatial decisions' (Fotheringham
et al.
,
2000: 214). Nevertheless, Fotheringham
et al.
(2000) argue that such criticisms are based on
what spatial interaction models once were rather than what they are now. Indeed, it is
becoming increasingly clear that spatial analysis can be usefully integrated with more qualita-
tive methods so as to incorporate both spatial and cultural 'turns' (Latham, 2003; Hall, 2011).
Indeed, this is also being illustrated by the extent to which user-generated content can also
provide insights into tourist behaviour and activities. As Latham (2003: 1993) notes, 'human
geography needs to be more imaginative, pluralistic, and pragmatic in its attitude towards
both (a) methodology and (b) the kinds of fi nal research accounts it produces.'
Issues of methodological preference aside, there is also 'no intrinsic need for this sub-
disciplinary apartheid' between mathematical modelling and critical geography (Clarke and
Wilson, 2001: 30). Spatial analysis and geographic information science provides an extremely
sound basis to identify differences in relative access to resources within calls for a people-
oriented GIS (Miller, 2005a). Space-time activities, including access to leisure and tourism
opportunities, are not evenly distributed (Hall, 2005b, 2010). Spatial analysis therefore
provides an empirical means to illustrate differences in access that occur as a result of class,
socio-economic and demographic differences, ethnicity and gender (Kwan, 1998, 1999;
Janelle and Hodge, 2000; Miller, 2005a, 2005b), in a manner which may be more compelling
and better understood by policy-makers than those derived from qualitative measures based
upon small sample sizes (Hall, 2010).
Future issues facing spatial analysis and GIS in tourism arguably refl ect the broader issues
surrounding the fi eld (Miller, 2005a; Goodchild, 2010). Nevertheless, following Clarke and
Wilson (2001), several key factors emerge. First, there is a 'recognition that model-based
analysis is but part of a wider process of management and planning rather than the central
feature of planning' (Clarke and Wilson, 2001: 36). Therefore, spatial analysis still needs to
be placed within the broader context of tourism planning and policy and the different inter-
ests it may serve (Hall, 2008b). Second, the quality and quantity of spatially referenced
