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 the user to estimate the overall number of aggregated points in a grid cell. A different visualization approach is proposed for more unique points, considered points of interest (POI), based on the selection method. The goal is to identify more locally relevant points from the data set, considered more important compared to other points in the neighborhood, which are then compared by numerical attribute. The method, derived from topographic isolation and called discrete isolation, is the distance from one point to the next with a higher attribute value. By using this measure, the most essential points can be easily selected by choosing a minimum distance and producing a homogenous spatial of the selected points within the chosen dataset. The two newly developed approaches are applied to multiscale mapping by constructing example workflows that produce multiscale maps. The publicly available multiscale mapping workflows OpenMapTiles and OpenStreetMap Carto, using OpenStreetMap data, are systematically explored and analyzed. The result is a general workflow for multiscale map production and a short overview of the toolchain software. In particular, the generalization approaches in the example projects are discussed and these are classified into cartographic theories on the basis of literature. The workflow is demonstrated by building a raster tile service for the micro diagrams and a vector tile service for the discrete isolation, able to be used with just a web browser. In conclusion, these new approaches for point data using VGI and LBSM allow better qualitative visualization of geodata. While analyzing vast global datasets is challenging, exploring and analyzing hidden data patterns is fruitful. Creating this degree of visualization and producing maps on multiple scales is a complicated task. The workflows and tools provided in this thesis will make map production on a worldwide scale easier.