Visual Analytics for Understanding Spatial Situations from Episodic Movement Data

Continuing advances in modern data acquisition techniques result in rapidly growing amounts of geo-referenced data about moving objects and in emergence of new data types. We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilizing Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data by means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) among the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors

Understanding movement data quality

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Understanding of data quality is essential for choosing suitable analysis methods and interpreting their results. Investigation of quality of movement data, due to their spatio-temporal nature, requires consideration from multiple perspectives at different scales. We review the key properties of movement data and, on their basis, create a typology of possible data quality problems and suggest approaches to identify these types of problems

Visual analytics methodology for eye movement studies

Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks

Geographical Counterpoint to Choreographic Information based on Approaches in GIScience and Visualization

This study provides geographical counterpoint to existing knowledge of a dance piece through approaches from GIScience and visualization by focusing on spatio-temporal movement of dancers in a large dataset of the dance. The goal of this study is to introduce a new application to bridging art and science in the domain of dance and geography disciplines. The study utilizes existing methodologies in GIScience, including exploratory spatial data analysis (ESDA), spatial analysis, Relative Motion (REMO) analysis, and Qualitative Trajectory Calculus (QTC) analysis for the reasoning of the dance data. The results of the study demonstrate the following. First, spatio-temporal information in the dance can be better understood by using approaches in geography, including ESDA, spatial analysis, REMO analysis, QTC analysis, and visualization. Second, the REMO analysis measured relative azimuth, speed, and δ-speed of the dancers per space and time and intuitively visualized their interactions. Third, the QTC analysis showed an example of measuring similarity and difference between repetitive movements of the dancers. The study exhibits how approaches of GIScience in geography could contribute to finding new knowledge of choreographic information that has been, in general, hard to recognize through other disciplines such as dance and statistics

Visual analytics of movement: An overview of methods, tools and procedures

Analysis of movement is currently a hot research topic in visual analytics. A wide variety of methods and tools for analysis of movement data has been developed in recent years. They allow analysts to look at the data from different perspectives and fulfil diverse analytical tasks. Visual displays and interactive techniques are often combined with computational processing, which, in particular, enables analysis of a larger number of data than would be possible with purely visual methods. Visual analytics leverages methods and tools developed in other areas related to data analytics, particularly statistics, machine learning and geographic information science. We present an illustrated structured survey of the state of the art in visual analytics concerning the analysis of movement data. Besides reviewing the existing works, we demonstrate, using examples, how different visual analytics techniques can support our understanding of various aspects of movement

Revealing Patterns and Trends of Mass Mobility through Spatial and Temporal Abstraction of Origin-Destination Movement Data

Origin-destination (OD) movement data describe moves or trips between spatial locations by specifying the origins, destinations, start, and end times, but not the routes travelled. For studying the spatio-temporal patterns and trends of mass mobility, individual OD moves of many people are aggregated into flows (collective moves) by time intervals. Time-variant flow data pose two difficult challenges for visualization and analysis. First, flows may connect arbitrary locations (not only neighbors), thus making a graph with numerous edge intersections, which is hard to visualize in a comprehensible way. Even a single spatial situation consisting of flows in one time step is hard to explore. The second challenge is the need to analyze long time series consisting of numerous spatial situations. We present an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps. The aggregated data are used for clustering of time intervals by similarity of the spatial situations. Temporal and spatial displays of the clustering results facilitate the discovery of periodic patterns and longer-term trends in the mass mobility behavior

Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces

Mobility diaries of a large number of people are needed for assessing transportation infrastructure and for spatial development planning. Acquisition of personal mobility diaries through population surveys is a costly and error-prone endeavour. We examine an alternative approach to obtaining similar information from episodic digital traces of people’s presence in various locations, which appear when people use their mobile devices for making phone calls, accessing the internet, or posting georeferenced contents (texts, photos, or videos) in social media. Having episodic traces of a person over a long time period, it is possible to detect significant (repeatedly visited) personal places and identify them as home, work, or place of social activities based on temporal patterns of a person’s presence in these places. Such analysis, however, can lead to compromising personal privacy. We have investigated the feasibility of deriving place meanings and reconstructing personal mobility diaries while preserving the privacy of individuals whose data are analysed. We have devised a visual analytics approach and a set of supporting tools making such privacy-preserving analysis possible. The approach was tested in two case studies with publicly available data: simulated tracks from the VAST Challenge 2014 and real traces built from georeferenced Twitter posts

Análisis de la movilidad espacial de la población asociada a huracanes a partir de la sombra digital geoespacial derivada de Twitter

Múltiples investigadores creen que el estudio del comportamiento y movilidad espacial de la población ha alcanzado un cuello de botella debido a la rigidez de los métodos tradicionales de investigación en el campo y a la dificultad de acceso a información relevante y de confianza. La sombra digital geoespacial es una de las oportunidades más prometedoras para poder desarrollar y probar nuevas hipótesis en el estudio del comportamiento espacial, pero la aplicación de estos nuevos métodos todavía no ha sido suficientemente explorada en el campo de los riesgos y desastres. Este artículo recoge los últimos avances en este ámbito centrándose en la capacidad de la sombra digital geoespacial de redes sociales (Twitter) como un método innovador para el estudio del comportamiento espacial humano durante emergencias. Esta investigación rastrea las localizaciones de usuarios de Twitter durante el periodo pre-desastre para producir estimaciones del número de evacuados, y en los meses posteriores al desastre para estimaciones de desplazados y del impacto del evento en el turismo