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Today, “zoomable” maps are a state-of-the-art way to explore the world, available to anyone with Internet access. However, the process of creating this visualization has been rather loosely investigated and documented. Nevertheless, with an increasing amount of available data, interactive maps have become a more integral approach to visualizing and exploring big datasets and user-generated data. OpenStreetMap and online platforms such as Twitter and Flickr offer application programming interfaces (APIs) with geographic information. They are well-known examples of this visualization challenge and are often used as examples. In addition, an increasing number of public administrations collect open data and publish their data sets, which makes the task of visualization even more relevant. This dissertation deals with the visualization of user-generated geodata as a multiscale map. The basics of today’s multiscale maps—their history, technologies, and possibilities—are explored and abstracted. This work introduces two new multiscale-focused visualization approaches for point data from volunteered geographic information (VGI) and location-based social media (LBSM). One contribution of this effort is a visualization methodology for spatially referenced information in the form of point geometries, using nominally scaled data from social media such as Twitter or Flickr. Typical for this data is a high number of social media posts in different categories—a post on social media corresponds to a point in a specific category. Due to the sheer quantity and similar characteristics, the posts appear generic rather than unique. This type of dataset can be explored using the new method of micro diagrams to visualize the dataset on multiple scales and resolutions. The data is aggregated into small grid cells, and the numerical proportion is shown with small diagrams, which can visually merge into heterogenous areas through colors depicting a specific category. The diagram sizes allow, Heute sind zoombare Karten Alltag für jeden Internetznutzer. Die Erstellung interaktiv zoombarer Karten ist allerdings wenig erforscht, was einen deutlichen Gegensatz zu ihrer aktuellen Bedeutung und Nutzungshäufigkeit darstellt. Die Forschung in diesem Bereich ist also umso notwendiger. Steigende Datenmengen und größere Regionen, die von Karten abgedeckt werden sollen, unterstreichen den Forschungsbedarf umso mehr. Beispiele für stetig wachsende Datenmengen sind Geodatenquellen wie OpenStreetMap aber auch freie amtliche Geodatensätze (OpenData), aber auch die zunehmende Zahl georeferenzierter Inhalte auf Internetplatformen wie Twitter oder Flickr zu nennen. Das Thema dieser Arbeit ist die Visualisierung eben dieser nutzergenerierten Geodaten mittels zoombarer Karten. Dafür wird die Entwicklung der zugrundeliegenden Technologien über die letzten zwei Jahr-zehnte und die damit verbundene Möglichkeiten vorgestellt. Weitere Beiträge sind zwei neue Visualisierungsmethoden, die sich besonders für die Darstellung von Punktdaten aus raumbezogenen nutzergenerierten Daten und georeferenzierte Daten aus Sozialen Netzwerken eignen. Ein Beitrag dieser Arbeit ist eine neue Visualisierungsmethode für raumbezogene Informationen in Form von Punktgeometrien mit nominal skalierten Daten aus Sozialen Medien, wie beispielsweise Twitter oder Flickr. Typisch für diese Daten ist eine hohe Anzahl von Beiträgen mit unterschiedlichen Kategorien. Wobei die Beiträge, bedingt durch ihre schiere Menge und ähnlicher Ei-genschaften, eher generisch als einzigartig sind. Ein Beitrag in den So-zia len Medien entspricht dabei einem Punkt mit einer bestimmten Katego-rie. Ein solcher Datensatz kann mit der neuen Methode der „micro diagrams“ in verschiedenen Maßstäben und Auflösungen visualisiert und analysiert werden. Dazu werden die Daten in kleine Gitterzellen aggregiert. Die Menge und Verteilung der über die Kategorien aggregierten Punkte wird durch kleine Diagramme dargestellt, wobei die Farben die

Social media data is used for analytics, e.g., in science, authorities or the industry. Privacy is often considered a secondary problem. However, protecting the privacy of social media users is demanded by laws and ethics. In order to prevent subsequent abuse, theft or public exposure of collected datasets, privacy-aware data processing is crucial. This dissertation presents a concept to process social media data with social media user’s privacy in mind. It features a data storage concept based on the cardinality estimator HyperLogLog to store social media data, so that it is not possible to extract individual items from it, but only to estimate the cardinality of items within a certain set, plus running set operations over multiple sets to extend analytical ranges. Applying this method requires to define the scope of the result before even gathering the data. This prevents the data from being misused for other purposes at a later point in time and thus follows the privacy by design principles. This work further shows methods to increase privacy through the implementation of abstraction layers. An included case study demonstrates the presented methods to be suitable for application in the field.:1 Introduction 1.1 Problem 1.2 Research objectives 1.3 Document structure 2 Related work 2.1 The notion of privacy 2.2 Privacy by design 2.3 Differential privacy 2.4 Geoprivacy 2.5 Probabilistic Data Structures 3 Concept and methods 3.1 Collateral data 3.2 Disposable data 3.3 Cardinality estimation 3.4 Data precision 3.5 Extendability 3.6 Abstraction 3.7 Time consideration 4 Summary of publications 4.1 HyperLogLog Introduction 4.2 VOST Case Study 4.3 Real-time Streaming 4.4 Abstraction Layers 4.5 VGIscience Book Chapter 4.6 Supplementary Software Materials 5 Discussion 5.1 Prevent accidental data disclosure 5.2 Feasibility in the field 5.3 Adjustability for different use cases 5.4 Limitations of HLL 5.5 Security 5.6 Outlook and further research 6 Conclusion Appendix Referenc

Events profoundly influence human-environment interactions. Through repetition, some events manifest and amplify collective behavioral traits, which significantly affects landscapes and their use, meaning, and value. However, the majority of research on reaction to events focuses on case studies, based on spatial subsets of data. This makes it difficult to put observations into context and to isolate sources of noise or bias found in data. As a result, inclusion of perceived aesthetic values, for example, in cultural ecosystem services, as a means to protect and develop landscapes, remains problematic. In this work, we focus on human behavior worldwide by exploring global reactions to sunset and sunrise using two datasets collected from Instagram and Flickr. By focusing on the consistency and reproducibility of results across these datasets, our goal is to contribute to the development of more robust methods for identifying landscape preference using geo-social media data, while also exploring motivations for photographing these particular events. Based on a four facet context model, reactions to sunset and sunrise are explored for Where, Who, What, and When. We further compare reactions across different groups, with the aim of quantifying differences in behavior and information spread. Our results suggest that a balanced assessment of landscape preference across different regions and datasets is possible, which strengthens representativity and exploring the How and Why in particular event contexts. The process of analysis is fully documented, allowing transparent replication and adoption to other events or datasets.

Die Region Mestia liegt abgeschieden im Norden Georgiens im Großen Kaukasus. Markant für die Region Mestia ist der Doppelgipfel der Ushba. Dieser zieht Touristen an, welche Wandertouren und Erkundungen durchführen möchten. Um diese sicher gewährleisten zu können, ist eine Alpenvereinskarte dringend erforderlich. In dieser Arbeit wird eine Generalisierung des Gebäudedatenbestand der Region Mestia mit Hilfe der Geoinformationssoftware ArcGIS Pro für eine ebensolche Alpenvereinskarte erstellt. Als Ausgangsdaten werden OpenStreetMap-Datensätze verwendet. Es werden die für den Zweck dieser Karte relevanten Daten herausgefiltert und alle unwichtigen Elemente entfernt. Zudem werden alle Stützpunkte der Gebäude gebildet und deren Abstand zueinander mittels eines dichte basierten Clustering bestimmt. Im Anschluss werden die Stützpunkte den Gebäude-Polygonen zugewiesen. Anhand der Abstandsbestimmung kann im Folgenden eine Unterscheidung der Gebäude in zwei Kategorien vorgenommen werden. Einzelnstehende Gebäude bzw. Gebäude mit einer sehr geringen Anzahl an Gebäuden in der unmittelbaren Nachbarschaft werden der Kategorie „Einzelgebäude“ zugeordnet. Alle anderen Gebäude werden in die Kategorie „Siedlung“ eingegliedert, da diese Gebäude sehr zahlreich und dicht beieinanderstehen. Die beiden Kategorien werden unterschiedlich generalisiert. Die Gebäude, die der Kategorie „Einzelgebäude“ zugeordnet sind, werden vereinfacht und als Punktsignaturen dargestellt. Alle anderen Gebäude werden aggregiert und Polygone mit einem geringen Flächeninhalt eliminiert. Zudem werden die Außenkanten der Polygone geglättet, damit ein vereinfachter Grundriss der Gebäude vorliegt. In einer Evaluierung wird die erstellte Generalisierung des Gebäudebestandes mit einer weiteren Gebäudegeneralisierung, welche mit dem Programm QGIS erstellt ist, gegenübergestellt. Den Expert*innen werden zudem Bilder von den Gebäuden der Region und zwei Beispielkarten, auf denen die generalisierten Gebäude dargestellt sind, The Mestia region is in the north of Georgia in the Great Caucasus. A distinctive feature of the Mestia region is the double peak of the Ushba. It attracts tourists who want to go hiking and exploring. To be able to guarantee this safely, an alpine association map is urgently needed. In this work, a generalisation of the building data stock of the Mestia region is created with the help of the geoinformation software ArcGIS Pro for a similar alpine association map. OpenStreetMap datasets are used as source data. The data relevant for the purpose of this map are filtered out and all unimportant elements are removed. In addition, all support points of the buildings are formed and their distance to each other is determined using density based clustering. Subsequently, the support points are assigned to the building polygons. Based on the distance determination, the buildings can be divided into two categories. Single buildings or buildings with a very small number of buildings in the immediate vicinity are assigned to the category 'single building'. All other buildings are placed in the category 'Settlement', as these buildings are very numerous and close together. The two categories are generalised differently. The buildings assigned to the category 'single building' are simplified and represented as point signatures. All other buildings are aggregated and polygons with a small area are eliminated. In addition, the outer edges of the polygons are smoothed so that a simplified ground plan of the buildings is available. In an evaluation, the created generalisation of the building stock is compared with another building generalisation created with the QGIS programme. The experts are also shown pictures of the buildings in the region and two sample maps showing the generalised buildings. It must be decided which map depicts the buildings on the picture. In this paper, the different generalisation of individual buildings and settlements is considered. The differences betwee

As the global population continues to grow, urbanization is one of the most significant anthropogenic processes linked to ecological change. But even in countries where the overall population is stagnating, migratory movements toward urban centres will continue to place pressure on the finite resource of land. Therefore, it is particularly important to determine and describe the development of settlement areas as precisely as possible in order to inform spatial planning decisions. For this reason, this dissertation presents vector-based methods to analyse, quantify and evaluate small-scale changes in settlement area. In this work, which constitutes a cumulative dissertation, novel methods are described that can be used to determine not only areal change in settlement and traffic areas (SuV), but also the type of building change and urban densification. This is of particular interest for the spatial planning of expanding metropolitan areas, where the question arises: Where, how and to which extent can built-up areas be further densified in order to reduce the consumption of land for new settlement areas? The methods presented here can facilitate spatially detailed analyses and already form the basis for a nationwide monitoring of settlement and open space development. This work shows how geometric deviations and changes in the underlying data model can be taken into account when determining SuV growth from data of the Authoritative Topographic-Cartographic Information System (ATKIS). In this context, positional inaccuracies of linearly and arealy modelled geometries are each treated in a special way so that minor positional offsets no longer affect the SuV increase. In addition, changes in the data model are accommodated by disregarding specific object reallocations when determining the SuV increase. To test these methods, the SuV increase was determined and analysed for Germany using national ATKIS data sets that feature geometric positional inaccuracies and data mo