Toward a kinetic-based probabilistic time geography
Date
2014Keywords
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Abstract
Time geography represents a powerful framework for the quantitative analysis of individual movement. Time geography effectively delineates the space–time boundaries of possible individual movement by characterizing movement constraints. The goal of this paper is to synchronize two new ideas, probabilistic time geography and kinetic-based time geography, to develop a more realistic set of movement constraints that consider movement probabilities related to object kinetics. Using random-walk theory, the existing probabilistic time geography model characterizes movement probabilities for the space–time cone using a normal distribution. The normal distribution has a symmetric probability density function and is an appropriate model in the absence of skewness – which we relate to an object’s initial velocity. Moving away from a symmetric distribution for movement probabilities, we propose the use of the skew-normal distribution to model kinetic-based movement probabilities, where the degree and direction of skewness is related to movement direction and speed. Following a description of our model, we use a set of case-studies to demonstrate the skew-normal model: a random walk, a correlated random walk, wildlife data, cyclist data, and athlete movement data. Our results show that for objects characterized by random movement behavior, the existing model performs well, but for object movement with kinetic properties (e.g., athletes), the proposed model provides a substantial improvement. Future work will look to extend the proposed probabilistic framework to the space–time prism.
Citation
Long , J A , Nelson , T A & Nathoo , F S 2014 , ' Toward a kinetic-based probabilistic time geography ' , International Journal of Geographical Information Science , vol. 28 , no. 5 , pp. 855-874 . https://doi.org/10.1080/13658816.2013.818151
Publication
International Journal of Geographical Information Science
Status
Peer reviewed
ISSN
1365-8816Type
Journal article
Rights
© 2013 Taylor & Francis. This is an Accepted Manuscript of an article published in the International Journal of Geographical Information Science on 2 September 2013, available online: http://www.tandfonline.com/10.1080/13658816.2013.818151
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