Space-Time Analytics of Tracks for the Understanding of Patterns of Life - Space-Time Integration in Geography and GIScience - page 370

such as one person taking multiple modes of transportation (e.g. bike to a bus stop,
ride a bus, bike to building bike rake, and walk into a building), and when mining
data streams, the movement patterns of individuals can be revealed in real time
(Yan et al. 2011 ). MobiVis is but one tool developed to visualize social-spatial-
temporal patterns of mobile data (Shen et al. 2006 ; Shen and Ma 2008 ). The tool
incorporates heterogeneous network and semantic filtering techniques based on
associated ontology graphs, and the visualization technique of behavior rings that
reveal periodic behavioral patterns of individuals and groups.

20.3 From Trajectory Analysis to Space-Time Analytics

Generally speaking, time geography or trajectory analysis approaches explore
individual tracks over time or collective tracks within a confined area. Most
studies examine geometry, semantics, clusters, classifications, and entity-location
interactions of discrete tracks or attempt to generalize a collection of tracks.
Whether tracks are taken for the same mobile object is seldom considered, and
therefore, methods to reveal routine and incongruent movement patterns are missing
in the literature. Yet, temporal patterns that depict transitions between routine
and incongruent movements reflect shifts in patterns of life and are essential to
many domain applications, in which geospatial distributions of patterns of life and
movement are indicative of individual's spatial social adaption or as a measure of a
population's collective spatial-social pulses.
Under the assumption that people develop routines and follow these routines over
time, their patterns of life form through settling down to the regular activities that
take place in space and time. Movement is the process that people engage in to
get to the target place at the right time, and congruent movement patterns suggest
the formation of space-time routines and therefore, spatial patterns of life. For a
population, the summative characteristics for the progression in forming patterns of
life and spatial differentials of the process show how a society (or a community)
may foster faster or slower adjustments for newcomers and where and when people
are gathered in the society.
Analytics remains an ambiguous term that often serves as an umbrella term for
statistics, data mining, modeling, simulation, and computational methods to dis-
cover and communicate meaningful patterns in data (Shmueli and Koppius 2011 ).
Yet, analytics commonly emphasizes data-centric and data-driven approaches to
tackle massive, streaming, heterogeneous and/or unstructured data, especially in
business management and marketing (Lavalle et al. 2011 ), education (Wagner
2012 ), website use (Marek 2011 ), and many other applications. In this chapter, we
define space-time analytics as methods to simplify the complexity of space-time
data into elements and structures that capture the useful information embedded
in the data. We emphasize elements and structures that together reveal useful
space-time information. Tracks, track segments, stops, and moves are examples of
elements in track analysis. Aggregates or disaggregates of these elements may also