Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set

By Mei-Po Kwan. 2000

497 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6db706fd717658ba065f7ac54dd1ba94054d3ac9

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In Transportation

Citations in the corpus

The [Limited] Impact of Weather on Tourist Behavior in an Urban Destination (2015, methodology): “A similar grid analysis was employed in past research such as Kwan (2000) and Shoval (2008).”
Gis methods in time‐geographic research: geocomputation and geovisualization of human activity patterns (2004, methodology): “…activity-travel data collected by GPS are available and used in the geo-visualization environment, the researcher can examine the detailed characteristics of an individual’s travel pattern, since actual travel routes can be revealed by these kinds of data (Kwan, 2000c; Kwan and Lee, 2004).”
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP) (2006, methodology): “When time is a key attribute in the data analysis, strategies applied to understanding the time component include sequencing methods (either animated or interactive ) and three-dimensional approaches where time is visualized as the third dimension over a two-dimensional map, e.g., [37].”
Spatial statistics for urban analysis: A review of techniques with examples (2004, methodology): “However, before reducing these dimensions for formal analysis, Kwan (2000) argues that visualization and exploration of movement patterns can prove useful to reveal space-time interaction structures.”
Visual analytics of movement: An overview of methods, tools and procedures (2013, background): “The most common types of display for the visualization of movements of discrete entities are static and animated maps [53][11] and interactive space-time cubes [37][34][32] with linear symbols representing trajectories.”
Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010, unknown): “For the short term and everyday mobility, more ﬂexible methods such as various registers and indirect databases (Raymer et al. , 2007), satellite-based methods (Chen et al. , 2006), or modern sensing technologies (Kwan, 2000; Eagle and Pentland, 2005; Shoval, 2007) are needed.”
Activity-based models of travel demand: promises, progress and prospects (2014, unknown): “Similar work includes Kwan (2000), Wu and Miller (2001), Weber and Kwan (2002), Miller (1999, 2005a, 2005b), Neutens et al. (2008), Yu and Shaw (2008), Shaw and Yu (2009) and Miller and Bridwell (2009).”
Emergency response after 9/11: the potential of real-time 3D GIS for quick emergency response in micro-spatial environments (2005, unknown): “These include interactive 3D geovisualization ( Kwan, 2000; Kwan & Lee, 2003), multi-dimensional GIS modeling (Raper, 2000), rule-based methods, agent-based modeling, pattern recognition and spatial data mining algorithms (Abler & Richardson, 2003; Galloway, 2003; Miller & Han, 2001).”
Applications of 3D City Models: State of the Art Review (2015, background): “Other analyses where researchers and practitioners have used 3D visualisation include human activity [172], wind fields [173], and air quality data [173–175].”
Geo-Narrative: Extending Geographic Information Systems for Narrative Analysis in Qualitative and Mixed-Method Research (2008, background): “Recent studies have also explored the use of 3D GIS methods for representing and visualizing people’s life paths based on the time-geographic framework (e.g., Kwan 1999, 2000, 2002a, 2007b, 2008; Kwan and Lee 2004; Ren and Kwan 2007).”