This thesis is based on longitudinal data of the Swedish population provided byStatistics Sweden and is conducted on behalf of the Institute for Analytical Sociology.The focus is on investigating the effectiveness of a frequent sequence miningmethod called constrained Sequential PAttern Discovery using Equivalence classes(cSPADE). The method is applied to data on segregation within workplaces, specificallyreasons for Swedish employees moving to more segregated workplaces. Thethesis found that no unique pattern of age, gender, education, unemployment, income,workplace size or foreignness index explain why a Swedish employee movesto a more segregated workplace. Evaluating the algorithm, it was found that thenumber of observations need to be smaller or an alteration of the algorithm needsto be done to reduce the process time for this specific data set.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2007. / Härtill 5 uppsatser.

Energy use is a part of everyday life and the use of energy is a part of the global climate change. Policy makers urge individuals to change their daily behaviour in order to mitigate climate change and care for our common environment. The dissertation regards daily behaviour as activities performed by individuals. The theoretical base is the time-geographic approach wherein everyday life is regarded as a sequence of interlinked activities performed by indivisible individuals. The dissertation investigates individuals’ energy use as an outcome of the activities they perform in everyday life. The empirical base of the dissertation is time-diaries from the Swedish time use survey 2010/2011. The diary data is explored as sequences of daily activities by using sequence analysis and clustering. The results show that individuals’ energy use is closely interweaved with how they live their everyday lives in terms of activity sequences. The results imply that changing an activity affects both the intricate web of interaction in the household and the interdependence of activities in everyday life. Change does not only affect the singular activity that was the object for the change, but rather major parts of the sequence of activities. In order to address energy conservation in information campaigns considerations ought to be taken on how everyday life is shaped and formed by the individual, by negotiations between the individuals in households, and societal structures. Information can be targeted to groups of individuals  with similar activity sequences as they are revealed by cluster analysis. / Energianvändningen är en del av vardagen likaväl som användningen av energi är en del av den globala klimatförändringen. För att mildra effekterna på vår gemensamma miljö uppmanas människor av politiker och andra beslutsfattare att förändra sitt vardagsbeteende. I avhandlingen betraktas vardagsbeteendet som människors dagliga aktiviteter. Avhandlingens teoretiska grund är den tidsgeografiska ansatsen, där människors vardag betraktas som en sekvens av de aktiviteter som utförs av odelbara individer. Människors dagliga sekvens av aktiviteter undersöks för att ta reda på vilken energianvändning som genomförandet av aktiviteterna ger upphov till. Den empiriska grunden för avhandlingen är tidsdagboksdata från den svenska tidsanvändningsstudien från 2010/2011 och avhandlingen utforskar tidsdagböckerna som sekvenser av aktiviteter med hjälp av sekvens- och klusteranalys. Resultaten visar att individers energianvändning är nära sammanvävd med de aktivitetssekvenser som visar hur vardagslivet levs. Resultaten pekar vidare på att förändringar av enskilda aktiviteter också påverkar andra aktiviteter i det dagliga livet. Förändringar av en aktivitet påverkar således hela den dagliga sekvensen av aktiviteter. I utformningen av information som syftar till att minska hushållens energianvändning bör hänsyn tas till hur vardagslivets aktivitetssekvens formas av den enskilde i samspelet både med andra individer i hushållet och med samhällsstrukturerna. Målgruppsinriktad information kan utformas med utgångspunkt from människors likartade aktivitetsmönster så som de framgår genom klusteranalys.

Sjukfrånvaro innebär en kostnad för både arbetsgivare och arbetstagare. För en anonym fullgrossist är detta ett problem på en av deras lagerlokaler, där sjukfrånvaron är hög. Uppsatsen syftar till att identifiera intressanta mönster över tid som leder till sjukfrånvaro genom att analysera data från företagets lönesystem och tidssystem. Datamaterialet är longitudinellt och för att upptäcka mönster som leder till sjukfrånvaro används sekvensanalys. För att generera de sekventiella mönstren används algoritmen cSPADE då den möjliggör att tidsbegränsningar kan anges för sekvenserna. Relevansen hos de genererade sekvenserna utvärderas med tre intressemått: support, konfidens och lift. Tre separata analyser genomförs där olika antal variabler används, beroende på om de förändras över tid eller har ett konstant värde, och för dessa analyser aggregeras data veckovis. De vanligaste händelserna som leder till sjukfrånvaro hos expeditörer är olika anställningstider, kön och födelseår. Några dagars sjukfrånvaro under en vecka, det vill säga mellan 8 och 40 timmar, är mer förekommande bland expeditörerna jämfört med kortare respektive längre sjukfrånvaro. Det går att konstatera att mönster med tidigare sjukfrånvaro ofta leder till fortsatt sjukfrånvaro. Uppsatsen belyser även de problem som uppstår inom sekvensanalys, till exempel att konstanta variabler överskuggar de icke-konstanta variablerna i de genererade sekvenserna. Detta händer när variabler som förändras över tiden används i kombination med variabler som har konstanta värden, något som kan förekomma i longitudinella datamaterial. / Absence due to sickness results in a cost to both employers and employees. For an unnamed wholesaler this is a problem at one of their warehouses, where the rate of sick leave is high. The aim of this thesis is to identify interesting patterns over time that lead to sick leave by analyzing data from the company's payroll system and their attendance system. The data is longitudinal and to detect the patterns that lead to sick leave, sequence analysis is used. To generate the sequential patterns the algorithm cSPADE is used since it allows time constraints to be specified for the sequences. The relevance of the generated sequences is evaluated with three interest measures: support, confidence and lift. Three separate analyses are performed where different variables are used, depending on whether they change over time or have a constant value, and for these analyses the data is aggregated weekly. The most common events that lead to sick leave for the employees are different duration of employment, gender and birth year. A few days sick leave during a week, namely between 8 and 40 hours, is more common among the employees compared to shorter and longer sick leave. It can be noted that the pattern of previous sick leave usually leads to continued sick leave. The thesis also highlights the problems that arise in sequence analysis, for example that the constant variables overshadow the non-constant variables in the resulting sequences. This happens when variables that change over time are used in combination with variables that have a constant value, which may occur in longitudinal data.

Two years after the first recorded outbreak of Covid-19 its long-term effects are still not completely understood. An unknown proportion of all covid patients go on to develop post-acute covid syndrome and suffer long-term symptoms and health effects long after the initial infection subsides. Project Crowdsourcing Långtidscovid-Sverige sent out in summer of 2021 an open online survey and gathered respondents through crowdsourcing to gather info about people in Sweden with prolonged health effects lasting at least three months after confirmed or suspected Covid-19 infection. In this thesis an explorative analysis of the aforementioned survey is conducted with its initial focus placed onthe progression of symptoms. Descriptive statistics are provided for the survey sample; hierarchical clusteringon principal components is performed; and association rule mining as well as sequence rule mining is used toextract frequently co-occurring symptoms. Women stand for 85.2% of all respondents, possibly indicating a skewed gender distribution in the sample. The average age of a respondent is 50 years old, but ranges between 18 and 80 years of age. The number of reported symptoms tend to diminish over time and symptoms within the 'air passages' category diminish on average quicker than other categories. Hierarchical clustering with Ward’s criterion revealed 4 clusters with an average silhouette coefficient of 0.246. The resulting clusters are not well-separated from each other and have some overlap in their bordering regions, and should therefore be interpreted with caution. Broadly speaking, individuals from cluster 1, 3 and 4 are distinguished primarily by their total number of symptoms reported, meanwhile cluster 2 is characterized by individuals that experience many symptoms early on and fewer symptoms later on. The most prevalent symptom over the entire period is fatigue (90.2%), closely followed by worsening symptomsafter physical activity (87.1%), problems with concentration (82.3%), headaches (79.5%), and brain fog (77.9%). There are several strong associations between various symptoms, especially for symptoms within the same category. Most symptoms have a sequential correlation with themselves and have an increased tendency to occur several times.