It's about Time: Analytical Time Periodization

This paper presents a novel approach to the problem of time periodization, which involves dividing the time span of a complex dynamic phenomenon into periods that enclose different relatively stable states or development trends. The challenge lies in finding such a division of the time that takes into account diverse behaviours of multiple components of the phenomenon while being simple and easy to interpret. Despite the importance of this problem, it has not received sufficient attention in the fields of visual analytics and data science. We use a real-world example from aviation and an additional usage scenario on analysing mobility trends during the COVID-19 pandemic to develop and test an analytical workflow that combines computational and interactive visual techniques. We highlight the differences between the two cases and show how they affect the use of different techniques. Through our investigation of possible variations in the time periodization problem, we discuss the potential of our approach to be used in various applications. Our contributions include defining and investigating an earlier neglected problem type, developing a practical and reproducible approach to solving problems of this type, and uncovering potential for formalization and development of computational methods.

Identifying, exploring, and interpreting time series shapes in multivariate time intervals

We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes. A data structure representing a single episode is a multivariate time series. To analyse collections of episodes, we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes. Each episode is thus represented by a combination of patterns. Using this representation, we apply visual analytics techniques to fulfil a set of analysis tasks, such as investigation of the temporal distribution of the patterns, frequencies of transitions between the patterns in episode sequences, and co-occurrences of patterns of different variables within same episodes. We demonstrate our approach on two examples using real-world data, namely, dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover. © 2023 The Author(s)

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: It allows for detailed information gathering for infected people in a privacy-preserving fashion;and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. © 2020, University of Skovde. All rights reserved.