Abstract
The importance of the spatial component of data items has been long recognized and gave rise to a successful line of research and development in Geographic Information Systems (GIS). In many application domains it is also essential to deal with the evolution of data along time and to integrate spatial, temporal and other aspects of the information domain in an expressive and operationally effective manner. Until recently, temporal solutions provided by spatial database systems were semi-temporal approaches lacking full temporal support. As a consequence, most spatial database systems manage snapshots of the present state of facts without fully exploiting historical temporal aspects. This paper provides preliminary results on a spatiotemporal database implementation. The proposed system builds on existing database technologies, TimeDB and Oracle Spatial, for temporal and spatial support, respectively. The justification for the choice of these technologies is given, based on the state of the art in spatial and temporal database research. The integration of the spatial and temporal components is achieved with the extension of the TimeDB implementation layer. A set of goals has been established in order to cover both the integration of the spatial support and the enforcement of the temporal requirements in the extended system. Issues and solutions are presented and illustrative examples show the use of the implemented functionalities.

Abstract
Spatiotemporal databases provide effective means to represent, manage and query information evolving over time. However, the visualization of record sets that result from spatiotemporal queries through traditional visualization techniques can be of difficult interpretation or may lack the ability to meaningfully display several instants at the same time. We propose a Temporal Focus + Context visualization model to overcome issues from such techniques resorting to concepts from Information Visualization. In this model, Focus + Context is applied to time rather than, as more typically, to attributes or space, and allows large amounts of data from distinct periods of time and from several record sets to be compressed onto one. Underlying the proposed visualization technique is the calculation of a temporal degree of interest (TDOI) for each record driven by specific analysis, exploration or presentation goals and based on the record valid time attribute, as well as on user-defined temporal visualization requirements. In the mapping stage of the visualization pipeline, the TDOI for a record is used to control graphical properties, such as transparency and color. More complex rendering properties, such as sketch drawing edges or other non-photorealistic enhancement techniques, can also be used to convey the temporal aspects of data, replacing the original graphical features of the record data. By enhancing or dimming the representation of a data item, according to the corresponding degree of interest, it is possible to meaningfully compress information about distinct temporal states of data onto the same visualization display. The model has been applied to several test scenarios and proved appropriate and useful for a wide range of domains that require the display, exploration and analysis of spatial information discretely evolving over time. Information Visualization (2008) 7, 265-274. doi: 10.1057/palgrave.ivs.9500188

Abstract

Abstract
Point Set Registration (PSR) algorithms have very different underlying theoretical models to define a process that calculates the alignment solution between two point clouds. The selection of a particular PSR algorithm can be based on the efficiency (time to compute the alignment) and accuracy (a measure of error using the estimated alignment). In our specific context, previous work used a CPD algorithm to detect and quantify change in spatiotemporal datasets composed of moving and shape-changing objects represented by a sequence of time stamped 2D polygon boundaries. Though the results were promising, we question if the selection of a particular PSR algorithm influences the results of detection and quantification of change. In this work we review and compare several PSR algorithms, characterize test datasets and used metrics, and perform tests for the selected datasets. The results show pyCPD and cyCPD implementations of CPD to be good alternatives and that BCPD can have potential to be yet another alternative. The results also show that detection and quantification accuracy change for some of the tested PSR implementations.