Geography Reference
In-Depth Information
3.1. The Need for GIS
Technological improvements introduced in third generation public bicycle
systems permit continuous monitoring of traffic flows of public bikes
(Shaheen et al. 2010). As such, operators of public bicycle systems are able to
gain access to real-time usage data of their networks. Collected via mobile
devices or other ICT-based measures, this information can then improve our
understanding of individual traveller's behaviour, offer real-time travel
information, and also present personalised location-based services. Moreover,
fine-grained data on the status of shared bicycles also enables an empirical
measurement of the impacts of proposed system improvements or policy
changes (e.g. fare restructuring) as well as the results of force majeure on the
system (e.g. flooding).
A number of studies have utilised stock or usage data to explore spatial
and temporal patterns. For example, examining Barcelona's shared bicycling
system ‗Bicing', Froehlich, Neumann and Oliver (2009) investigated user
behaviour across stations in relation to location, neighbourhood, and time of
day. Still in the European context, Borgnat et al. (2009) predicted the number
of bikes hired per hour in Lyon's community bicycle program to describe the
daily and weekly patterns. The prediction method involved several explanatory
factors such as the number of subscribed users, the time of the week, the
occurrence of holidays or strikes, and weather parameters. However, methods
to identify and visualise spatio-temporal patterns (i.e. location and times when
frequency of bike use is particularly high) based on flow or trip data have not
been adequately examined in past studies, particularly, within the Australian
context. There is, therefore, an imperative need to better understand the
location, time and reasons for these individual uses to inform strategies to
ensure a more successful public bicycle implementation.
The Brisbane's CityCycle dataset was used to conduct GIS analysis of
transport dynamics on urban areas. The CityCycle data contains trip level
information in the form of an origin-destination matrix. There are total of 150
CityCycle stations distributed in the Brisbane CBD and its immediate
surrounding suburbs. The rows represent origin stations and the columns
destination stations along with individual counts of transitions. Analysis of this
type of dataset in raw form is not generally viable given that it consists of a
large matrix of numbers with no geographic information included. This is
particularly the case in multivariate situations where the difference in origin-
destination matrices conditional on other variables (for example, hour of day)
is of interest. The argument for the role of exploratory spatial data analysis has
