Geography Reference
In-Depth Information
Figure 8. Vertical Bézier curve 3D visualization of hourly phone-call flow patterns
across cells in a day. (Green dots are locations of mobile base stations; each arc
represents an OD flow linking two mobile cells).
Such information flow patterns are strongly related daily human activity
rhythms and working-social connections, as well as geographical contexts
including urban land-use types and spatial distributions of home-job locations
(Ratti et al., 2006; Gao et al., 2013b).
In order to understand the dynamic ―source-sink‖ structures of informa-
tion landscape (Liu et al., 2012), we calculated the phone call net-balance-flow
for each mobile cell by subtracting the outgoing call volume from the
incoming call volume in each hour. Figure 9 shows the time-series plot of
phone-call net flow among all cells. Each line represents the net flow pattern
for a specific Voronoi mobile coverage area. In Figure 10, it is clear to see
dynamic spatial distributions of the ―source‖ areas (red color) which have
more outgoing phone calls and the ―sink‖ areas (blue color) which have more
incoming phone calls in different hours. The yellow cells mean that the net-
balance call flow in that hour is zero. One can interactively interpret the call
flow patterns in the 3D-GIS environment or sense the dynamic urban phone-
call landscape under the animation mode.
4.3. The Spatio-Temporal Autocorrelation Patterns
of Phone Calls
The study of spatio-temporal autocorrelation structure of mobile phone
calls in urban space can help to understand the citizens' mobile communica-
tion patterns and urban structures. In order to investigate how the spatial auto-
correlation structure changes throughout the day in this city, the phone-call
volume was aggregated into the Voronoi cells by hour at first.
