Carlson M. Büth

Information theory, i.e. the mathematical analysis of information and of its processing, has become a tenet of modern science; yet, its use in real-world studies is usually hindered by its computational complexity, the lack of coherent software frameworks, and, as a consequence, low reproducibility. We here introduce infomeasure, an open-source Python package designed to provide robust tools for calculating a wide variety of information-theoretic measures, including entropies, mutual information, transfer entropy and divergences. It is designed for both discrete and continuous variables; implements state-of-the-art estimation techniques; and allows the calculation of local measure values, \p\-values and \t\-scores. By unifying these approaches under one consistent framework, infomeasure aims to mitigate common pitfalls, ensure reproducibility, and simplify the practical implementation of information-theoretic analyses. In this contribution, we explore the motivation and features of infomeasure; its validation, using known analytical solutions; and exemplify its utility in a case study involving the analysis of human brain time series.

This thesis investigates the impact of the spatial order of cities on the performance of presented, data-driven partitioning approaches. The study addresses two research questions: 1. How does the travel time change if all neighborhoods were Low Traffic Neighborhoods (LTNs)? 2. What LTN configuration can we suggest for different types of cities? We present a framework to analyze the impact of LTNs on travel time that utilizes Open Street Map (OSM) street data, and GHSL population data to calculate network measures, such as directness, global efficiency, average circuity, street orientation order. Central components of this work are the LTN generation, evaluation, and visualization. The evaluation of 100 global cities and 80 cities in Germany reveals that both a residential-based approach and a betweenness-based approach yield positive results, with minimal travel time increases. This research contributes to the understanding of the impact of LTNs on travel time and provides a framework for the simplified generation and evaluation of LTNs.

To mitigate the risk of injuries, many countries recommend bicycle helmets. The current paper seeks to examine the effectiveness of bicycle helmets by performing a systematic review focusing on meta-analyses. First, the current paper explores the findings of studies that employ meta-analyses using bicycle crash data. Second, the results are discussed considering the findings from research analyzing bicycle helmet effectiveness in a laboratory using simulation, and then are complemented with key methodological papers that address cycling and the overall factors contributing to the injury severity. The examined literature confirms that wearing a helmet while cycling is beneficial, regardless of age, crash severity, or crash type. The relative benefit is found to be higher in high-risk situations and when cycling on shared roads and particularly preventing severe head injuries. The results from the studies performed in laboratories also suggest that the shape and size of the head itself play a role in the protective effects of helmets. However, concerns regarding the equitability of the test conditions were found as all reviewed studies used a fifty-percentile male head and body forms. Lastly, the paper discusses the literature findings in a broader societal context.