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Finding patterns is a common act in human intellectual endeavours, and it is a complex challenge tackled by both humans and algorithms. For several decades, musical pattern discovery algorithms have been researched, and researchers have been comparing human-annotated patterns to algorithmic outputs, as well as algorithms to algorithms. However, traditional metrics have not fully captured the rich insights that these comparisons could offer. To contribute to the comparisons between musical pattern discovery mechanisms, this dissertation spans seven chapters. Chapter 1 provides the background of the dissertation, including an overview, research approaches, contexts, scope, thesis statement, and an enumeration of contributions. Chapter 2 delves into the concept of musical patterns and explores the diverse landscape of musical pattern discovery algorithms. Our exploration reveals the complexities surrounding the definition of patterns and the multifaceted nature of these algorithms. Chapter 3 is dedicated to the collection tools for human-annotated musical patterns and the analysis of factors that influence annotations. We observe that musical background impacts annotated patterns; tool interfaces and automatic matching affect the length and frequency of annotations. Chapter 4 introduces four methods tailored for comparing musical pattern discovery algorithms. These methods provide novel insights into the discrepancies between human-annotated patterns and their algorithmically extracted counterparts. These methods provide a more comprehensive approach to comparing algorithms, aiding in the interpretation and evaluation of algorithmic outputs. Chapter 5 implements Pattrans, a Domain-Specific Language (DSL) in the functional language Haskell for comparing musical pattern occurrences through musical transformations. We delve into its design for uncovering the relations between pattern occurrences in a modular way. Chapter 6 employs Pattrans to scrutinise transformations be

We verzamelen in dit digitale tijdperk steeds meer data. Ook over de ondergrond: in allerlei openbare bronnen vind je de meest uiteenlopende gegevens. Al die informatie kan helpen om ruimte, functies en processen onder het maaiveld inzichtelijk te maken. En die informatie kan op zijn beurt weer bijdragen aan bijvoorbeeld het leggen van de ruimtelijke puzzel, en invulling geven aan ‘water en bodem sturend’. Maar hoe ontsluit je die informatie zo goed mogelijk, zodat deze bruikbaar is voor de beoogde doeleinden? Een visualisatie kan daarbij een oplossing zijn!

The course is not a pure programming course, but has a rather strong focus on programming and scripting to solve geographical or spatial applied problems. During the course you will learn how to deal with both vector spatial data and raster data; such as satellite imagery used in remote sensing studies. The programming languages dealt with during the course are R and python. Professionals in private companies and research institutes are increasingly using scripting languages for applied spatial analysis. We notice that students still have a kind of 'scripting' barrier to overcome. Some students are afraid of scripting and others stick very closely to the examples given. We like to relax this attitude. From the perspective of an advanced course we like to change the cookbook approach in favor of a more explorative approach. Becoming more relaxed towards scripting and a more explorative approach means that some generic scripting principles must be known to create a 'core' script. On the other hand students have to learn which libraries are available for spatial data analysis and visualization and how these libraries can be used within the 'core' script.