Exploring Trajectory Attributes in Brest Harbor

We describe a procedure for analyzing trajectory attributes that in-cludes spatial clustering of the trajectories and visual analysis of the distribution of attribute values in the temporal and spatial dimensions

Extracting user spatio-temporal profiles from location based social networks

Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including mobility profiles, points of interest or unusual events. This information is important for different domains like mobility route planning, touristic recommendation systems or city planning. Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their visits. The aim of this paper is to analyze how the spatio-temporal behavior of a large number of users in a well limited geographical area can be segmented in different profiles. These behavioral profiles are obtained by means of clustering algorithms that show the different behaviors that people have when living and visiting a city. The data analyzed was obtained from the public data feeds of Twitter and Instagram inside the area of the city of Barcelona for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or any general area) to discover useful profiles that can be described on terms of the city singular places and areas and their temporal relationships. These profiles can be used as a basis for making decisions in different application domains, specially those related with mobility inside and outside a city.Preprin

Analysing the spatial dimension of eye movement data using a visual analytic approach

Conventional analyses on eye movement data only take into account eye movement metrics, such as the number or the duration of fixations and length of the scanpaths, on which statistical analysis is performed for detecting significant differences. However, the spatial dimension in the eye movements is neglected, which is an essential element when investigating the design of maps. The study described in this paper uses a visual analytics software package, the Visual Analytics Toolkit, to analyse the eye movement data. Selection, simplification and aggregation functions are applied to filter out meaningful subsets of the data to be able to recognise structures in the movement data. Visualising and analysing these patterns provides essential insights in the user's search strategies while working on a (n interactive) map

Interactive spatiotemporal cluster analysis of vast challenge 2008 datasets

We describe a visual analytics method supporting the analysis of two different types of spatio-temporal data, point events and trajectories of moving agents. The method combines clustering with interactive visual displays, in particular, map and space-time cube. We demonstrate the use of the method by applying it to two datasets from the VAST Challenge 2008: evacuation traces (trajectories of people movement) and landings and interdictions of migrant boats (point events)