Search results for "spatio-temporal data"
showing 6 items of 6 documents
Statistical inference for eye movement sequences using spatial and spatio-temporal point processes
2017
Eye tracking is a widely used method for recording eye movements, which are important indicators of ongoing cognitive processes during the viewing of a target stimulus. Despite the variety of applications, the analyses of eye movement data have been lacking of methods that could take both the spatial and temporal information into account. So far, most of the analyses are based on strongly aggregated measures, because eye movement data are considered to be complex due to their richness and large variation between and within the individuals. Therefore, the eye movement methodology needs new statistical tools in order to take full advantage of the data. This dissertation is among the first stud…
Local characteristics of functional marked point processes with applications to seismic data
2022
We present a family of local inhomogeneous mark-weighted summary statistics for general marked point processes. These capture various types of local dependence structures depending on the specified involved weight function. We use them to propose a local random labeling test. This procedure enables us to identify points and thus regions where the random labeling assumption does not hold, for example, when the (functional) marks are spatially dependent. We further present an application to a seismic point pattern with functional marks provided by seismic waveforms. Indeed, despite the relatively long history of point process theory, few approaches to analyzing spatial point patterns where th…
Functional principal component analysis for multivariate multidimensional environmental data
2015
Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in modelling these data has been generated, but the complexity of spatio-temporal models, together with the size of the dataset, results in a challenging task. The modelization is even more complex in presence of multivariate data. Since some modelling problems are more natural to think through in functional terms, even if only a finite number of observations is available, treating the data as functional can be useful (Berrendero et al. in Comput Stat Data Anal 55:2619–2634, 2011). Although in Ramsay and Silverman (Functional data analysis, 2nd edn. Springer, New York, 2005) the case of multiva…
Representing and Reasoning for Spatiotemporal Ontology Integration
2004
International audience; The World-Wide Web hosts many autonomous and heterogeneous information sources. In the near future each source may be described by its own ontology. The distributed nature of ontology development will lead to a large number of local ontologies covering overlapping domains. Ontology integration will then become an essential capability for effective interoperability and information sharing. Integration is known to be a hard problem, whose complexity increases particularly in the presence of spatiotemporal information. Space and time entail additional problems such as the heterogeneity of granularity used in representing spatial and temporal features. Spatio-temporal ob…
Sensor Fusion Combining 3-D and 2-D for Depth Data Enhancement
2012
Time-of-Flight (ToF) cameras are known to be cost-efficient 3-D sensing systems capable of providing full scene depth information at a high frame rate. Among many other advantages, ToF cameras are able to provide distance information regardless of the illumination conditions and with no texture dependency, which makes them very suitable for computer vision and robotic applications where reliable distance measurements are required. However, the resolution of the given depth maps is far below the resolution given by standard 2-D video cameras which, indeed, restricts the use of ToF cameras in real applications such as those for safety and surveillance. In this thesis, we therefore investigate…
A pre-processing and network analysis of GPS tracking data
2020
Global Positioning System (GPS) devices afford the opportunity to collect accurate data on unit movements from temporal and spatial perspectives. With a special focus on GPS technology in travel surveys, this paper proposes: (1) two algorithms for the pre-processing of GPS data in order to deal with outlier identification and missing data imputation; (2) a clustering approach to recover the main points of interest from GPS trajectories; and (3) a weighted-directed network, which incorporates the most relevant characteristics of the GPS trajectories at an aggregate level. A simulation study shows the goodness-of-fit of the imputation data algorithm and the robustness of the clustering algori…