An evaluation of space time cube representation of spatiotemporal patterns.

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

The Trade-offs with Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Fast and correct analysis of such information is important in for instance geospatial and social visualization applications. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a dataset to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap we report on a between-subjects experiment comparing novice users error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the dataset, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users when analyzing complex spatiotemporal patterns.

The influence of stacks on flow patterns and stratification associated with natural ventilation

We investigate the steady state natural ventilation of an enclosed space in which vent A, located at height hA above the floor, is connected to a vertical stack with a termination at height H, while the second vent, B, at height hB above the floor, connects directly to the exterior. We first examine the flow regimes which develop with a distributed source of heating at the base of the space. If hBhB>hA, then two different flow regimes may develop. Either (i) there is inflow through vent B and outflow through vent A, or (ii) the flow reverses, with inflow down the stack into vent A and outflow through vent B. With inflow through vent A, the internal temperature and ventilation rate depend on the relative height of the two vents, A and B, while with inflow through vent B, they depend on the height of vent B relative to the height of the termination of the stack H. With a point source of heating, a similar transition occurs, with a unique flow regime when vent B is lower than vent A, and two possible regimes with vent B higher than vent A. In general, with a point source of buoyancy, each steady state is characterised by a two-layer density stratification. Depending on the relative heights of the two vents, in the case of outflow through vent A connected to the stack, the interface between these layers may lie above, at the same level as or below vent A, leading to discharge of either pure upper layer, a mixture of upper and lower layer, or pure lower layer fluid. In the case of inflow through vent A connected to the stack, the interface always lies below the outflow vent B. Also, in this case, if the inflow vent A lies above the interface, then the lower layer becomes of intermediate density between the upper layer and the external fluid, whereas if the interface lies above the inflow vent A, then the lower layer is composed purely of external fluid. We develop expressions to predict the transitions between these flow regimes, in terms of the heights and areas of the two vents and the stack, and we successfully test these with new laboratory experiments. We conclude with a discussion of the implications of our results for real buildings.

A Bayesian approach to tracking in single molecule fluorescence microscopy

Using fluorescence microscopy with single molecule sensitivity it is now possible to follow the movement of individual fluorophore tagged molecules such as proteins and lipids in the cell membrane with nanometer precision. These experiments are important as they allow many key biological processes on the cell membrane and in the cell, such as transcription, translation and DNA replication, to be studied at new levels of detail. Computerized microscopes generate sequences of images (in the order of tens to hundreds) of the molecules diffusing and one of the challenges is to track these molecules to obtain reliable statistics such as speed distributions, diffusion patterns, intracellular positioning, etc. The data set is challenging because the molecules are tagged with a single or small number of fluorophores, which makes it difficult to distinguish them from the background, the fluorophore bleaches irreversibly over time, the number of tagged molecules are unknown and there is occasional loss of signal from the tagged molecules. All these factors make accurate tracking over long trajectories difficult. Also the experiments are technically difficulty to conduct and thus there is a pressing need to develop better algorithms to extract the maximum information from the data. For this purpose we propose a Bayesian approach and apply our technique to synthetic and a real experimental data set.