Toward a kinetic-based probabilistic time geography

References

• Arellano-Valle, R.B. and Azzalini, A., 2008. The centred parametrization for the multivariate skew-normal distribution. Journal of Multivariate Analysis, 99, 1362–1382.
   Web of Science ®Google Scholar
• Azzalini, A., 1985. A class of distributions which includes the normal ones. Scandinavian Journal of Statistics, 12 (2), 171–178.
   Web of Science ®Google Scholar
• Azzalini, A. and Dalla Valle, A., 1996. The multivariate skew-normal distribution. Biometrika, 83 (4), 715–726.
   Web of Science ®Google Scholar
• Benhamou, S., 2011. Dynamic approach to space and habitat use based on biased random bridges. PloS One, 6 (1), e14592.
   PubMed Web of Science ®Google Scholar
• Börger, L., Dalziel, B.D., and Fryxell, J.M., 2008. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecology Letters, 11 (6), 637–650.
   PubMed Web of Science ®Google Scholar
• Cagnacci, F., et al., 2010. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 365 (1550), 2157–2162.
   PubMed Web of Science ®Google Scholar
• Codling, E.A., Plank, M.J., and Benhamou, S., 2008. Random walk models in biology. Journal of the Royal Society Interface, 5 (25), 813–834.
   PubMed Web of Science ®Google Scholar
• Delafontaine, M., Neutens, T., and Van de Weghe, N., 2011. Modelling potential movement in constrained travel environments using rough space – time prisms. International Journal of Geographical Information Science, 25 (9), 1389–1411.
   Web of Science ®Google Scholar
• Dettki, H., Ericsson, G., and Edenius, L., 2004. Real-time moose tracking: an internet based mapping application using GPS/GSM-collars in Sweden. Alces, 40, 13–21.
   Google Scholar
• Douglas, D.H., 1994. Least-cost path in GIS using an accumulated cost surface and slopelines. Cartographica, 31 (3), 37–51.
   Google Scholar
• Downs, J.A., 2010. Time-geographic density estimation for moving point objects. LNCS, 6292, 16–26.
   Google Scholar
• Downs, J.A. and Horner, M.W., 2012. Probabilistic potential path trees for visualizing and analyzing vehicle tracking data. Journal of Transport Geography, 23, 72–80.
   Web of Science ®Google Scholar
• Gonzalez, M.C., Hidalgo, C.A., and Barabási, A.-L., 2008. Understanding individual human mobility patterns. Nature, 453, 779–782.
   PubMed Web of Science ®Google Scholar
• Gupta, R.C. and Gupta, R.D., 2004. Generalized skew normal model. Test, 13 (2), 501–524.
   Web of Science ®Google Scholar
• Hägerstrand, T., 1970. What about people in regional science? Papers in Regional Science, 24 (1), 6–21.
   Google Scholar
• Horne, J.S., et al., 2007. Analyzing animal movements using Brownian bridges. Ecology, 88 (9), 2354–2363.
   PubMed Web of Science ®Google Scholar
• Kalbfleisch, J.G., 1985. Probability and statistical inference. New York, NY: Springer.
   Google Scholar
• Kareiva, P.M. and Shigesada, N., 1983. Analyzing insect movement as a correlated random walk. Oecologia, 56 (2–3), 234–238.
   Web of Science ®Google Scholar
• Knighton, R. and Claramunt, C., 2001. An aeronautical temporal GIS for post-flight assessment of navigation performance. Transactions in GIS, 5 (1), 53–66.
   Google Scholar
• Krumm, J., Letchner, J., and Horvitz, E., 2007. Map matching with travel time constraints. In: SAE world congress. April 16–19, Detroit MI. SAE International, 11.
   Google Scholar
• Kuijpers, B., Miller, H.J., and Othman, W., 2011. Kinetic space-time prisms. In: 19th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems. New York, NY: Association of Computing Machinery, 162–170.
   Google Scholar
• Kwan, M., 1998. Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework. Geographical Analysis, 30 (3), 191–216.
   Web of Science ®Google Scholar
• Kwan, M., 2004. GIS methods in time-geographic research: geocomputation and geovisualization of human activity patterns. Geografiska Annaler. Series B, Human Geography, 86 (4), 267–280.
   Google Scholar
• Lenntorp, B., 1999. Time-geography – at the end of its beginning. GeoJournal, 48 (3), 155–158.
   Google Scholar
• Liu, J., et al., 2009. Automatic player detection, labeling and tracking in broadcast soccer video. Pattern Recognition Letters, 30 (2), 103–113.
   Web of Science ®Google Scholar
• Long, J.A. and Nelson, T.A., 2012. Time geography and wildlife home range delineation. The Journal of Wildlife Management, 76 (2), 407–413.
   Web of Science ®Google Scholar
• Long, J.A. and Nelson, T.A., 2013. Measuring dynamic interaction in movement data. Transactions in GIS, 17 (1), 62–77.
   Google Scholar
• Lu, W.-L., Okuma, K., and Little, J.J., 2009. Tracking and recognizing actions of multiple hockey players using the boosted particle filter. Image and Vision Computing, 27 (1–2), 189–205.
   Web of Science ®Google Scholar
• Lu, W.-L., et al., 2011. Identifying players in broadcast sports videos using conditional random fields. In: IEEE conference on computer vision, CVPR’ 11, Providence, RI, 8p.
   Google Scholar
• Miller, H.J., 2003. What about people in geographic information science? Computers, Environment and Urban Systems, 27 (5), 447–453.
   Google Scholar
• Miller, H.J., 2005. A measurement theory for time geography. Geographical Analysis, 37 (1), 17–45.
   Web of Science ®Google Scholar
• Miller, H.J. and Bridwell, S.A., 2009. A field-based theory for time geography. Annals of the Association of American Geographers, 99 (1), 49–75.
   Web of Science ®Google Scholar
• Miller, H.J. and Wu, Y.-H., 2000. GIS software for measuring space-time accessibility in transportation planning and analysis. GeoInformatica, 4 (2), 141–159.
   Google Scholar
• Nams, V.O., 2005. Using animal movement paths to measure response to spatial scale. Oecologia, 143 (2), 179–188.
   PubMed Web of Science ®Google Scholar
• Othmer, H.G., Dunbar, S.R., and Alt, W., 1988. Models of dispersal in biological systems. Journal of Mathematical Biology, 26, 263–298.
   PubMed Web of Science ®Google Scholar
• Pearson, K., 1905. The problem of the random walk. Nature, 72, 294.
   Google Scholar
• Prager, S.D. and Yu, B., 2005. Contextualized probability for approximation of spatiotemporal data distributions. In: Internaional conference on Cybernetics and Information Technologies, Systems and Applications (ISAS CITSA). Orlando, FL, 318–322.
   Google Scholar
• Raper, J., et al., 2007. A critical evaluation of location based services and their potential. Journal of Location Based Services, 1 (1), 5–45.
   Google Scholar
• R Development Core Team, 2012. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
   Google Scholar
• Skellam, J.G., 1951. Random dispersal in theoretical populations. Biometrika, 38 (1–2), 196–218.
   PubMed Web of Science ®Google Scholar
• Turchin, P., 1998. Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sunderland, MA: Sinauer Associates.
   Google Scholar
• Urbano, F., et al., 2010. Wildlife tracking data management: a new vision. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 365 (1550), 2177–2185.
   PubMed Web of Science ®Google Scholar
• Winter, S., 2009. Towards a probabilistic time geography. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in Geographic Information Systems – GIS  ’09. New York, NY: ACM Press, 528–531.
   Google Scholar
• Winter, S. and Yin, Z.-C., 2010. Directed movements in probabilistic time geography. International Journal of Geographical Information Science, 24 (9), 1349–1365.
   Web of Science ®Google Scholar
• Winter, S. and Yin, Z.-C., 2011. The elements of probabilistic time geography. GeoInformatica, 15 (3), 417–434.
   Web of Science ®Google Scholar
• Yu, B. and Kim, S.H., 2006. Interpolating and using most likely trajectories in moving-objects databases. In: S. Bressan, J. Kung, and R. Wagner, eds. DEXA 2006, LNCS 4080. Berlin Heidelberg: Springer-Verlag, 718–727.
   Google Scholar