GIScience 2012, Columbus, Ohio, 18-21 September 2012
M.W. Horner and J. A. Downs
“Mobile object analysis continues to be well-studied in GIScience (Hornsby and Egenhofer 2002; Laube et al. 2005; Neutens et al. 2011). Time geography remains the key theoretical framework for understanding mobile objects’ movement possibilities (Miller 2005). Within time geography, recent efforts have sought to enhance its ‘probabilistic’ potential through exploring questions of data uncertainty, spatial representation, and limitations of classical approaches (Kuijpers et al. 2010; Neutens et al. 2011; Winter and Lin 2011). Along these lines, Downs (2010) fused time geography and kernel density estimation in developing timegeographic density estimation (TGDE), which may be used to estimate mobile objects’ probable locations in continuous space, given a time budget between control points (Downs 2010). Downs and Horner (2012) extend TGDE to discrete network space, demonstrating its application with GPS-based vehicle tracking data (Downs and Horner 2012) and using it in searches for travellers’ destinations missing in travel surveys (Horner et al. 2012).
Intensity Values for Traveller 1 (eq. 1).
“The present paper explores a new direction for TGDE, namely the creation of a densitybased accessibility measure for mobile objects. Related to time geography, accessibility measures have also garnered widespread attention in the literature (Kwan 1998; Miller 1999; O’Sullivan et al. 2000; Yu and Shaw 2008; Delafontaine et al. 2012). Our new metrics gauge how accessible a moving object is to particular opportunities of interest, given the constraints inherent to its movement plan. Thus, we are able not only visualize where the object most likely could have been (Downs and Horner 2012), but we also capture the configuration and magnitude of activities relative to its travel path from both a visual and analytic perspective.”
Journal of Hydrometeorology, Volume 13 Issue 4, August 2012
Rebekka Erdin, Christoph Frei, and Hans R. Künsch
“Geostatistics provides a popular framework for deriving high-resolution quantitative precipitation estimates (QPE) by combining radar and rain gauge data. However, the skewed and heteroscedastic nature of precipitation is in contradiction to assumptions of classical geostatistics. This study examines the potential of trans-Gaussian kriging to overcome this contradiction. Combination experiments are undertaken with kriging with external drift (KED) using several settings of the Box–Cox transformation. Hourly precipitation data in Switzerland for the year 2008 serve as test bed to compare KED with and without transformation. The impact of transformation is examined with regard to compliance with model assumptions, accuracy of the point estimate, and reliability of the probabilistic estimate. Data transformation improves the compliance with model assumptions, but some level of contradiction remains in situations with many dry gauges. Very similar point estimates are found for KED with untransformed and appropriately transformed data. However, care is needed to avoid excessive transformation (close to log) because this can introduce a positive bias. Strong benefits from transformation are found for the probabilistic estimate, which is rendered positively skewed, sensitive to precipitation amount, and quantitatively more reliable. Without transformation, 44% of all precipitation observations larger than 5 mm h−1 are considered as extremely unlikely by the probabilistic estimate in the test application. Transformation reduces this rate to 4%. Although transformation cannot fully remedy the complications for geostatistics in radar–gauge combination, it seems a useful procedure if realistic and reliable estimation uncertainties are desired, such as for the stochastic simulation of QPE ensembles.”