Toward a kinetic-based probabilistic time geography
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.
Keywords: kinetics; mobile objects; personal movement models; probability; time geography
Document Type: Research Article
Affiliations: 1: Spatial Pattern Analysis and Research Lab, Department of Geography, University of Victoria, Victoria, BC, Canada 2: Department of Mathematics & Statistics, University of Victoria, Victoria, BC, Canada
Publication date: 04 May 2014
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content