Geography Reference
In-Depth Information
Claramunt, 2004; Ratti et al., 2006; González et al., 2008; Kang et al., 2012a,
2012b; Liu et al., 2012; Yuan et al., 2012; Gao et al., 2013b, 2013c; Shen et
al., 2013). In general, the mining and analyzing processes of such spatio-
temporal data require combined qualitative-quantitative approaches which
involve data extraction and analytics, statistical inference and geovisualization.
We present a spatio-temporal analytical framework (Figure 1) which combines
STV, STKDE and STAA for understanding spatial-temporal patterns (both
individual and aggregated) hidden in the Big Geo-Data. Each of them has
different characteristics and data-format requirements. In the processing, the
raw data were converted into different data structures for various analytical
purposes. In the following part, we will discuss the roles of different spatio-
temporal analytics for the presented research.
2.1. Spatio-Temporal Visualization Techniques for Trajectory
and Flow
By utilizing the power of human vision, previous studies have demonstra-
ted the effectiveness of geovisualization in spatial data exploration and know-
ledge discovery (MacEachren and Kraak, 2001; Kwan, 2004; Guo et al. 2005;
Andrienko et al., 2008).
Figure 1. A spatio-temporal analytical framework for identifying human mobility
patterns and urban dynamics.
