Exploring time diaries using semi-automated activity pattern extraction

Identifying patterns of activities in time diaries in order to understand the variety of daily life in terms of combinations of activities performed by individuals in different groups is of interest in time use research. So far, activity patterns have mostly been identified by visually inspecting representations of activity data or by using sequence comparison methods, such as sequence alignment, in order to cluster similar data and then extract representative patterns from these clusters. Both these methods are sensitive to data size, pure visual methods become too cluttered and sequence comparison methods become too time consuming. Furthermore, the patterns identified by both methods represent mostly general trends of activity in a population, while detail and unexpected features hidden in the data are often never revealed. We have implemented an algorithm that searches the time diaries and automatically extracts all activity patterns meeting user-defined criteria of what constitutes a valid pattern of interest for the user’s research question. Amongst the many criteria which can be applied are a time window containing the pattern, minimum and maximum occurrences of the pattern, and number of people that perform it. The extracted activity patterns can then be interactively filtered, visualized and analyzed to reveal interesting insights. Exploration of the results of each pattern search may result in new hypotheses which can be subsequently explored by altering the search criteria. To demonstrate the value of the presented approach we consider and discuss sequential activity patterns at a population level, from a single day perspective.Time-geography, diaries, everyday life, activity patterns, visualization, data mining, sequential pattern mining

Escalas de calidad de vida relacionada con la salud y funcionalidad en pacientes con patologías de hombro: revisión sistemática, evaluación estandarizada y validación de un cuestionario

189 p.El hombro, es una de las articulaciones más flexibles del ser humano, hecho que lo hace susceptible a varias lesiones. En el ámbito de la salud, los datos relacionados con el estado de una persona se pueden obtener mediante observación clínica, por cuestionarios auto-cumplimentados de Calidad de Vida Relacionada con la Salud (CVRS), o por escalas que combinan ambos tipos de datos. Estos instrumentos tienen que contar con ciertas propiedades psicométricas, como la fiabilidad, la validez y la sensibilidad al cambio, entre otras. Se estudiaron las propiedades de 11 escalas de CVRS, que se usan en la patología de hombro, y una de ellas se validó al castellano. Además, se revisaron y evaluaron las propiedades psicométricas de la escala funcional Constant-Murley score (CMS), en diferentes grupos de patología de hombro. El CMS combina datos auto-cumplimentados con datos del especialista, y se considera por muchos como regla de oro en la evaluación de la patología de hombro. Se realizaron búsquedas sistemáticas y la evaluación se realizó por pares de revisores, con la herramienta Evaluating the Measurement of Patient Reported Outcomes (EMPRO). Entre las escalas de CVRS evaluadas, la American Shoulder and Elbow Surgeons form (ASES-p), compuesta por 11 ítems fue la finalmente seleccionada. Nuestros resultados indican que ASES-p tiene validez convergente, sensibilidad al cambio y es reproducible. Los modelos factoriales y Rasch, apoyaron su uni-dimensionalidad. El ítem del deporte y la distribución de la puntuación en dos sub-escalas son aspectos que requieren de más estudio. En cuanto a la escala CMS, la evidencia disponible sugiere que es sensible al cambio. Se puede usar para la evaluación de sujetos con patología sub-acromial, pero los datos no apoyan su uso en fracturas, inestabilidad, artritis y hombro congelado. El CMS no se puede percibir como regla de oro en la evaluación de hombro

Escalas de calidad de vida relacionada con la salud y funcionalidad en pacientes con patologías de hombro: revisión sistemática, evaluación estandarizada y validación de un cuestionario

189 p.El hombro, es una de las articulaciones más flexibles del ser humano, hecho que lo hace susceptible a varias lesiones. En el ámbito de la salud, los datos relacionados con el estado de una persona se pueden obtener mediante observación clínica, por cuestionarios auto-cumplimentados de Calidad de Vida Relacionada con la Salud (CVRS), o por escalas que combinan ambos tipos de datos. Estos instrumentos tienen que contar con ciertas propiedades psicométricas, como la fiabilidad, la validez y la sensibilidad al cambio, entre otras. Se estudiaron las propiedades de 11 escalas de CVRS, que se usan en la patología de hombro, y una de ellas se validó al castellano. Además, se revisaron y evaluaron las propiedades psicométricas de la escala funcional Constant-Murley score (CMS), en diferentes grupos de patología de hombro. El CMS combina datos auto-cumplimentados con datos del especialista, y se considera por muchos como regla de oro en la evaluación de la patología de hombro. Se realizaron búsquedas sistemáticas y la evaluación se realizó por pares de revisores, con la herramienta Evaluating the Measurement of Patient Reported Outcomes (EMPRO). Entre las escalas de CVRS evaluadas, la American Shoulder and Elbow Surgeons form (ASES-p), compuesta por 11 ítems fue la finalmente seleccionada. Nuestros resultados indican que ASES-p tiene validez convergente, sensibilidad al cambio y es reproducible. Los modelos factoriales y Rasch, apoyaron su uni-dimensionalidad. El ítem del deporte y la distribución de la puntuación en dos sub-escalas son aspectos que requieren de más estudio. En cuanto a la escala CMS, la evidencia disponible sugiere que es sensible al cambio. Se puede usar para la evaluación de sujetos con patología sub-acromial, pero los datos no apoyan su uso en fracturas, inestabilidad, artritis y hombro congelado. El CMS no se puede percibir como regla de oro en la evaluación de hombro

SimpliFly: A Methodology for Simplification and Thematic Enhancement of Trajectories.

Movement data sets collected using today's advanced tracking devices consist of complex trajectories in terms of length, shape, and number of recorded positions. Multiple additional attributes characterizing the movement and its environment are often also included making the level of complexity even higher. Simplification of trajectories can improve the visibility of relevant information by reducing less relevant details while maintaining important movement patterns. We propose a systematic stepwise methodology for simplifying and thematically enhancing trajectories in order to support their visual analysis. The methodology is applied iteratively and is composed of: (a) a simplification step applied to reduce the morphological complexity of the trajectories, (b) a thematic enhancement step which aims at accentuating patterns of movement, and (c) the representation and interactive exploration of the results in order to make interpretations of the findings and further refinement to the simplification and enhancement process. We illustrate our methodology through an analysis example of two different types of tracks, aircraft and pedestrian movement

Розробка модуля отримання демографічних та клінічних даних про пацієнта для експертної системи оцінювання ризику серцево – судинних захворювань у хворих на артеріальну гіпертензію

Signaling data from the cellular networks can provide a means of analyzing the efficiency of a deployed transportation system and assisting in the formulation of transport models to predict its future use. An approach based on this type of data can be especially appealing for transportation systems that need massive expansions, since it has the added benefit that no specialized equipment or installations are required, hence it can be very cost efficient. Within this context in this paper we describe how such obtained data can be processed and used in order to act as enablers for traditional transportation analysis models. We outline a layered, modular architectural framework that encompasses the entire process and present results from initial analysis of mobile phone call data in the context of mobility, transport and transport infrastructure. We finally introduce the Mobility Analytics Platform, developed by Ericsson Research, tailored for mobility analysis, and discuss techniques for analyzing transport supply and demand, and give indication on how cell phone use data can be used directly to analyze the status and use of the current transport infrastructure

Car dependent practices: findings from a sequence pattern mining study of UK time use data

This paper identifies three main understandings of the notion of 'car dependence' in transport research: a micro-social understanding (dependence as an attribute of individuals), a macro approach (attribute of societies or local areas as whole), and a meso-level understanding, where it refers to trips – or rather to the activities that people travel to undertake. While the first two approaches have been dominant, this paper further develops the third, addressing questions as to whether and why certain activities are inherently more difficult to switch away from the car. At the theoretical level, it builds on theories of social practice to put forward the notion of ‘car dependent practices’. At the empirical level, it demonstrates that the application of sequence pattern mining techniques to time use data allows the identification of car and mobility intensive activities, arguably representing the trace of car dependent practices. Overall, the findings of this mining exercise suggest that the emphasis of existing literature on escorting children, shopping and carrying heavy goods as car dependent trip purposes is not misplaced. Our analysis adds to this knowledge by contextualising the information by providing detailed quantitative analysis of a larger, richer set of activities hitherto overlooked in transport policy. The article concludes by illustrating the policy implications of the approach adopted and the findings generated, discussing possible strategies to steer practices in a more sustainable direction by creating material alternatives to the 'cargo function' of car travel

Evaluation of shoulder-specific patient-reported outcome measures: A systematic and standardized comparison of available evidence

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Background: The aim of this study was to perform a standardized and systematic evaluation of the available evidence on multi-item shoulder-specific patient-reported outcome measures that are applicable to a wide spectrum of disorders. Materials and methods: A systematic review was conducted in PubMed to identify articles with information regarding the development process, metric properties, and administration issues of shoulder-specific patient-reported outcome measures. Two experts independently reviewed all the articles identified for one instrument and applied the EMPRO (Evaluating Measures of Patient Reported Outcomes) tool, which was designed to assess the quality of attributes in a standardized way. An overall EMPRO score and 6 attribute-specific scores were calculated (range, 0-100) to describe the quality of instrument performance. Results: We identified 11 instruments and 112 articles (2-30 articles per instrument). The American Shoulder and Elbow Surgeons (ASES) shoulder assessment, Simple Shoulder Test (SST), and Oxford Shoulder Score (OSS) were the best rated, with overall scores of 77.4 points, 72.6 points, and 69.7 points, respectively. They have been shown to be valid, reliable, and responsive, with a low administration burden. Acceptable results were also found for the Flexilevel Scale of Shoulder Function, Shoulder Pain and Disability Index, and Dutch Shoulder Disability Questionnaire, but some of their attributes need further evaluation. Conclusions: Current evidence supports the use of the ASES, SST, or OSS. We recommend the SST for longitudinal studies or clinical trials, the Dutch Shoulder Disability Questionnaire for clinical practice to minimize administration burden, and the ASES or OSS to discriminate among patients' or groups' evaluations at one point of time. © 2014 Journal of Shoulder and Elbow Surgery Board of Trustees.Department of HealthGovernment of the Basque Country, Spai

A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model-building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal-oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed