Data Collection on Personal Movement using Mobile ICTs: Old Wine in New Bottles?

Data Collection on Personal Movement using Mobile ICTs: Old Wine in New Bottles?

Martin Lee-Gosselin (Université Laval, Canada), Sean T. Doherty (Wilfrid Laurier University, Canada) and Amer Shalaby (University of Toronto, Canada)
DOI: 10.4018/978-1-61520-769-5.ch001
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This chapter examines the current status of data collection methods employing location/time-aware devices to observe evolving patterns of spatio-temporal behaviour, including patterns that are affected by ICTs. Drawing mostly on transport research, it is suggested that two streams of development have emerged: a “passive” stream that maximises the automatic interpretation of positioning data, and an “active” stream, that is using increasingly sophisticated mobile computing devices and/or the Internet to engage respondents in the validation, interpretation and enhancement of their own data. Recent and future developments are described that promise to go beyond simply using technologies to carry out conventional travel surveys: rather, some new classes of data may be obtained, notably because of common or overlapping interests with other fields, such as public health research. There are, however, some ethical and public acceptability constraints that must be respected.
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The Initiation Of Ict-Aided Data Collection Methods

While the use of ICTs to understand vehicular traffic is a relatively mature art, the same cannot be said of related methods to understand human activity in time and space. For more than 20 years, increasingly miniaturised electronic sensor and computing technologies have made it possible to collect timed microbehavioural data on human subjects and to record the data in portable loggers. For example, in the public health and sports medicine literatures, one finds citations of research into free activity using portable accelerometers going back to 1984 or earlier (Matthew, 2005), and as we discuss below, the measurement of physical activity is a rapidly expanding field of research that is taking advantage of the potential of multiple sensors.

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