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Topologische Datenanalyse
(2019)
Bei der Analyse von höherdimensionalen Daten kann deren Gestalt wichtige Informationen über den Datensatz liefern. Bei einer gegebenen Punktwolke, die aus einem unbekannten topologischen Raum ausgewählt wurde, versucht die Topologische Datenanalyse (TDA) den ursprünglichen Raum zu rekonstruieren. Dieser Beitrag soll eine Einführung in die Topologische Datenanalyse geben und konzentriert sich dabei auf zwei wichtige Aspekte: die Persistente Homologie und den Mapper. Dabei werden zuerst die notwendigen theoretischen Grundlagen vorgestellt und anschließend wird die Methodik bei der Visualisierung von Daten eingesetzt.
Die Persistente Homologie ist eines der Standardwerkzeuge in der TDA. Sie findet ihre Anwendung beispielsweise in den Bereichen Formerkennung und -beschreibung. Der Mapper als zweites wichtiges Konzept der TDA wandelt umfangreiche, höherdimensionale Datensätze in Simplizialkomplexe um und kann dadurch geometrische und topologische Eigenschaften der Daten bestimmen. Des Weiteren ist die Mapper-Methode ein brauchbares Werkzeug zur Visualisierungen von mehrdimensionalen Daten, woran statistische Verfahren scheitern.
Many European countries have experienced a significant increase of unemployment in recent years. This paper reviews several theoretical models that try to explain this phenomenon. Predominantly, these models claim a link between the poor performance of European labor markets and the high level of market regulation. Commonly referred to as the Eurosclerosis debate, prominent approaches consider insider-outsider relationships, search-models, and the influence of hiring and firing costs on equilibrium employment. The paper presents empirical evidence of each model and studies the relevance of the identified rigidities as a determinant of high unemployment in Europe. Furthermore, a case study analyzes the unemployment problem in Germany and critically discusses new reform efforts. In particular this section analyzes whether the recently enacted Hartz reforms can induce higher employment.
Urban pollution
(2022)
We use worldwide satellite data to analyse how population size and density affect urban pollution. We find that density significantly increases pollution exposure. Looking only at urban areas, we find that population size affects exposure more than density. Moreover, the effect is driven mostly by population commuting to core cities rather than the core city population itself. We analyse heterogeneity by geography and income levels. By and large, the influence of population on pollution is greatest in Asia and middle-income countries. A counterfactual simulation shows that PM2.5 exposure would fall by up to 36% and NO2 exposure up to 53% if within countries population size were equalized across all cities.
The COVID-19 pandemic created the largest experiment in working from home. We study how persistent telework may change energy and transport consumption and costs in Germany to assess the distributional and environmental implications when working from home will stick. Based on data from the German Microcensus and available classifications of working-from-home feasibility for different occupations, we calculate the change in energy consumption and travel to work when 15% of employees work full time from home. Our findings suggest that telework translates into an annual increase in heating energy expenditure of 110 euros per worker and a decrease in transport expenditure of 840 euros per worker. All income groups would gain from telework but high-income workers gain twice as much as low-income workers. The value of time saving is between 1.3 and 6 times greater than the savings from reduced travel costs and almost 9 times higher for high-income workers than low-income workers. The direct effects on CO₂ emissions due to reduced car commuting amount to 4.5 millions tons of CO₂, representing around 3 percent of carbon emissions in the transport sector.