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

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

Utforskning av sekvenser i händelsebaserade data

Event-based data are encountered daily in many disciplines and are used for various purposes. They are collections of ordered sequences of events where each event has a start time and a duration. Examples of such data include medical records, internet surfing records, transaction records, industrial process or system control records, and activity diary data. This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal trends, relationships and similarities across the data, allow for comparisons to be made within and between the records, and can also help predict forthcoming events.The presented work has researched methods for identifying and exploring such event-sequences which are based on modern visualization, interaction and data mining techniques. An interactive visualization environment that facilitates analysis and exploration of event-based data has been designed and developed, which permits a user to freely explore different aspects of this data and visually identify interesting features and trends. Visual data mining methods have been developed within this environment, that facilitate the automatic identification and exploration of interesting sequences as patterns. The first method makes use of a sequence mining algorithm that identifies sequences of events as patterns, in an iterative fashion, according to certain user-defined constraints. The resulting patterns can then be displayed and interactively explored by the user.The second method has been inspired by web-mining algorithms and the use of graph similarity. A tree-inspired visual exploration environment has been developed that allows a user to systematically and interactively explore interesting event-sequences.Having identified interesting sequences as patterns it becomes interesting to further explore how these are incorporated across the data and classify the records based on the similarities in the way these sequences are manifested within them. In the final method developed in this work, a set of similarity metrics has been identified for characterizing event-sequences, which are then used within a clustering algorithm in order to find similarly behavinggroups. The resulting clusters, as well as attributes of the clusteringparameters and data records, are displayed in a set of linked views allowing the user to interactively explore relationships within these. The research has been focused on the exploration of activity diary data for the study of individuals' time-use and has resulted in a powerful research tool facilitating understanding and thorough analysis of the complexity of everyday life

Exploring sequences in event-based data

This thesis is available online through Linköping University Electronic Press: www.ep.liu.se Event-based data are encountered daily in many disciplines and are used for various purposes. They are collections of ordered sequences of events where each event has a start time and a duration. Examples of such data include medical records, internet surfing records, transaction records, industrial process or system control records, and activity diary data. This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal trends, relationships and similarities across the data, allow for comparisons to be made within and between the records, and can also help predict forthcoming events. The presented work has researched methods for identifying and exploring such event-sequences which are based on modern visualization, interaction and data mining techniques

Visualizing thermal comfort in residential passive house designs

Energy use for thermal comfort in housing accounts for a large share of total energy use in many countries. The housing sector has the potential to lower the demand for heating and cooling through better building designs, such as the passive house concept. This concept integrates building technologies, energy systems and activities performed by end-users in their everyday lives to minimise the use of external energy sources for thermal comfort. In this study we propose a methodology based on the combination of personal diary data and their in-context interactive visualization as a promising approach to study and better understand thermal comfort in private spheres. We collected activity diaries from residents in a new display city district called Vallastaden in Linköping, Sweden. Passive house designs were promoted in Vallastaden and around 10 percent of the new housing was built with this concept. Activity and thermal comfort data from the diaries were digitalised and coded. Following this the data were represented and visually explored using an adapted, for the purpose, version of a visual analysis tool.Vallastaden energidesig

En tidsgeografisk studie av strukturen i lärares vardag

Rapporten En tidsgeografisk studie av lärares vardag bygger på det dagboksmaterial som Skolverket samlade in under våren 2012 från drygt 3 600 lärare. Den besvarar frågor som: När under dagen utförs arbetsrelaterade aktiviteter och var utförs de? Har aktiviteterna lång eller kort varaktighet? Vilka skillnader och likheter finns mellan lärare i olika årskurser? V ilka arbetsrelaterade aktiviteter utförs efter lektionstid och var genomförs de? Finns det sekvenser av aktiviteter som återkommer hos många lärare i yrkesvardagen? I rapporten visualiseras grundskollärares tidsanvändning så att deras aktiviteter under dygnet beskrivs i den tidsordning som de genomförs. Resultatet visar att lärares arbetsvecka i mångt och mycket ser likartad ut. Det tar sig uttryck i att lärare arbetar ungefär lika långa dagar, vistas ungefär lika lång tid i skolan, genomför till viss del samma aktivitetssekvenser. De flesta arbetsrelaterade aktiviteterna genomförs i skolan, men många lärare utför också arbetsrelaterade aktiviteter på kvällstid i hemmet. Påtagliga skillnader framgår mellan veckans fyra första dagar och fredagar och mellan lärare i olika årskurser i flera olika avseenden. Lärares yrkesvardag analyseras med hjälp av de tidsgeografiska begreppen projekt och restriktioner. Lärares förvärvsarbete är ett individprojekt och detta ska passa in både i skolans organisationsprojekt (med målet att skapa förutsättningar för elevernas lärande) och i det egna hushållets organisationsprojekt (med målet att skapa ett bra liv för hushållsmedlemmarna). Skolans organisationsprojekt innebär auktoritetsrestriktioner för lärare genom att de måste följa avtal, arbeta enligt bestämda undervisningsscheman och delta i föreskrivna möten. För lärarnas del innebär bland annat schemaläggningen av undervisningen att kopplingsrestriktioner uppstår. Lärare som vill eller behöver arbeta tillsammans måste ha tidsmässiga möjligheter och någon ledig lokal för att genomföra sina gemensamma aktiviteter och då kan inte någon av dem vara schemalagd för lektioner. Kopplingsrestriktioner uppträder t ex när tätt packade scheman och högt tryck på skolans lokaler och försvårar för samarbete mellan lärare. När arbetsrelaterade aktiviteter utförs hemma på kvällstid vävs skolans organisationsprojekt in i lärarhushållets privata projekt. Det innebär att lärares individprojekt förvärvsarbete blir en kopplingsrestriktion som hindrar henne/honom från att samtidigt delta i hushållets privata projekt. Kanske skulle t ex utökad tillgång till arbetsutrymmen i skolan där aktiviteter kan utföras ostört och koncentrerat, möjliggöra för lärare att genomföra mer av sina arbetsrelaterade aktiviteter på dagtid och i skolan? Visualiseringen av den kontextuella tidsanvändningen och analysen av de kopplingsrestriktioner som uppstår i lärarnas yrkesvardag indikerar att andra former av organisering av en del arbetsrelaterade aktiviteterna skulle kunna frigöra tid för lärarnas kärnaktiviteter