Geoscience Reference
In-Depth Information
Yoo (2010: 217) has also called for researchers to embrace the ubiquitous impact of computing in
everyday life and suggests 'it is more meaningful to think about computing as a verb than computers
as a noun'. As with many approaches to mobile interaction design, Yoo describes the requirements
for a more experiential form of computing influenced by the philosophies of Merleau-Ponty (1996)
and emphasises the importance of the direct first-person experience of reality. This view of comput-
ing is contrasted with a more representational view, where the desktop computer is a central tool in
handling collections of digital entities and relationships, which form a conceptualisation of the real
world - but remote from that real world. There are interesting implications here for certain forms
of GC in that mobile devices allow digital representations and model outputs to be assessed in the
context of their real-world counterparts, something which will be discussed in more detail in the
next section.
Capturing a richer picture of a person's in-field experience involves the measurement of physical
properties of a spatial location as only the first step, with the next challenge being to acknowledge
a greater
sense of place
and to represent 'the memories, experiences and patterns of behaviour we
associate with that locale' (Ciolfi, 2004: 38). The concept of place has been seen as providing more
meaning to a spatial location by 'endowing it with value' (Tuan, 2001), not only through naming
(Edwardes, 2009) but by attempting to understand the place values used by people in appreciating,
enjoying and valuing the environment (Ehrenfeld, 1993; Norton and Hannon, 1997; Kruger and
Jakes, 2003). Concepts of place can also include aspects of a person's context relating to acceptable
behaviour given the local culture or the type of clothes worn and the language which is spoken,
none of which are necessarily tied to space. Rather it is the place itself which provides the context
for these behaviours.
The focus on place-centric and not user-centric design in mobile computing has been described
by Messeter (2009: 32) as place-specific computing (PSC), where 'the designed functionality of
systems and services, as well as information provided by these systems and services, are inherently
grounded in and emanating from the social and cultural practices of a particular place'. Examples
include methods for supporting club-hopping culture, related to the consumption of music, and
there are clear implications not just for more sophisticated approaches to LBS but for developing
computational approaches that allow such notions of space and place to be explored. The scale at
which place has relevance is also seen to vary hugely depending on the nature of the activity, and so
tools to support interactions occurring at a range of spatial scales need to be considered, where the
idea of local can mean more than a geometric buffer zone centred on the coordinate of the user's
current location.
In the context of LBS, various studies have considered approaches to spatial query that go
beyond pure location, often with an aim to provide more geographically relevant results (Raper,
2007; Reichenbacher, 2009). Specific examples include determining the area that could be visited
within a certain time frame as in the WebPark project (Mountain and MacFarlane, 2007) or, in an
urban context, the likely visibility of key landmarks (Bartie et al., 2008). Various tools such as the
Mscape authoring environment (Stenton et al., 2007) have supported experimentation in the use of
alternative spatial footprints for triggering information, but there is no reason why such zones of rel-
evance should be seen as fixed geometries, and the development of tools that will allow for dynamic,
adaptive and perhaps indeterminate zones of relevance to be modelled would go some way towards
exploring more place-centric computing.
In terms of LBGC, the spatial footprint of the area of immediate relevance to the user could be
determined by an even broader set of space-based and potentially place-based contexts. Also in
many cases, there is a broader zone of influence relating to the whole geographical dataset under
study. In some ways, this classification of spatial scales relates to the way raster-based algorithms
are sometimes classified into local, focal, zonal and regional (de Smith et al., 2013). Figure 15.2
offers a classification of three spatial scales seen as relevant to LBGC, here using a graphical back-
drop of a river catchment although the concept is seen as generic.
