Developing a Method for Visualizing Population Movements

Developing a Method for Visualizing Population Movements

Matthew Kwan (RMIT University, Australia), Colin Arrowsmith (RMIT University, Australia) and William Cartwright (RMIT University, Australia)
Copyright: © 2015 |Pages: 17
DOI: 10.4018/978-1-4666-8465-2.ch008
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Abstract

This chapter describes a technique for visualizing the movements of a population in a region at a point in time. It is suitable for cases where a large population is spread throughout the region and can move in all directions, for example the population of a large city. By repeatedly clustering movement vector arrows it can visually summarize the movements of millions of individuals, and do so with moderate computing resources. The technique is designed to work with data captured from mobile phone networks, but other sources of data can also be used.
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Background

Whenever a mobile phone communicates with a cell tower as part of a billable event, a record is created by the carrier. Billable events include making a call, sending an SMS, and accessing the Internet, and records of these events are used to calculate the customer's bill at a later date. The records also contain a cell ID, which identifies the cell tower the phone was communicating with.

Billable events are, in theory, available from all carriers. For example, in 2010 German politician Malte Spitz filed suit against Deutsche Telekom to release all the records they held relating to his account (Cohen, 2011). The resulting data, consisting of 35,830 records from September 2009 to February 2010, was then made publicly available on the Zeit Online website (Biermann, 2011). Deutsche Telekom was recording around 200 records per day, of which 168 contained spatial information in the form of a cell ID, along with its latitude and longitude, and the bearing of the cell's antenna (if it was directional). Roughly 24 percent of the records were generated by SMS, 13 percent by voice calls, and 54 percent by GPRS Internet access. The remaining records were not identified.

As a full-time politician Spitz may have used his mobile phone a lot more often than the average user, since other studies looking at billing data, e.g. (Calabrese et al., 2010), report an average of only 2.9 records per subscriber per day, over 60 times fewer than Spitz. However, mobile phone users are increasingly accessing the Internet from their handset, and this will create more records since each access is recorded separately.

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