Geography Reference
In-Depth Information
which is enriched by information on the respondent's profile, and ongoing activity,
time use, and attitude, a distinction which continues to validate this methodology
in a number of contexts. Legacy datasets gathered using surveys are known to have
(non fatal) sources of inaccurate or incomplete responses, which in general have
been documented only to a limited degree. This paper is concerned with using GIS
technologies to more fully interrogate a case study database (tourists travel survey)
so as to identify: (i) the level of uncertainty in given responses from individuals,
(ii) the pattern of missing data and (iii) the degree to which such datasets can be
enhanced by models using concepts found in time-geography.
Keywords Tracking individual movement
Spatial-temporal data quality
Space-time constraints
Tourists itinerary
WCTFS
West Coast New Zealand
1 Introduction
Mobility is a key factor in many processes and activities, and it has both positive
and negative impacts on society and environment. A better understanding of
movement is increasingly valued in fields of research such as transportation,
planning, ecology, community health, environmental management, biosecurity,
disaster management and surveillance. Although disciplines look at movement in
different ways and seek to ask a variety of questions, a common acknowledgement
is that it is a complex phenomenon for description and analysis, and to be well
understood it needs data including background information on the nature of the
moving entity as well as on its tracked position in space and time. A growing
desire to create rich datasets with these parameters has led to an increased interest
in data collection, but to date this has had an emphasis on electronic devices, most
typically the basic GPS. There is a legion of projects obtaining basic movement
data (x, y, t), a growing number integrating these with spatial information of the
local geographies, but yet far fewer integrating rich human datasets that require
human responses as part of the process. Pioneers are working on linking GPS and
diary information for this purpose via the unified technology of a smartphone
(Halifax STAR Project
2007
; Raento et al.
2009
), but for various reasons the
traditional survey methods of the form-based questionnaire or diary sustain an
inhabited niche. This chapter is fundamentally about the performance of traditional
survey methods applied to movement data collection, and how this might be
improved by retrospectively auditing their data in terms of the probable accuracy
of given responses.
Such 'rich' movement surveys typically seek information from a respondent on
the route of their journey (usually by a sequence of stops), timing of arrival at (or
departure from) visited places, the duration of presence there, the activity while
stopped and the reason for stopping. Other variables such as expenditure and
source of knowledge about the place may be appended in an effort to understand
