The kinds of physical spaces present in the real world are becoming ever more complex, and the locations defining the boundaries between these spaces are often arbitrary. Distinguishing between which spaces count as `outdoors,' and which count as `indoors,' becomes more difficult when `semi-outdoor' and `semi-indoor' spaces are considered. Integrating these different spaces within geovisualisations is difficult because data on the spaces are often collected and stored separately.
Many existing navigational applications avoid the explicit differentiation between different types of spaces, or choose to only visualise one type of space.
Additionally, these applications rarely identify which areas are visible to users at their present positions, and which areas are occluded.
This thesis explores the potential of utilising point clouds directly in geovisualisations to communicate information about the types of spaces surrounding a hypothetical user in a real-world environment.

Raw point cloud data is collected on three different transitional spaces, all of which contain an outdoor element. These point clouds are classified into four different `space-types' (outdoor, indoor, semi-indoor, and semi-outdoor), and visibility analysis is performed on them directly. The resulting information on space-type and visibility is combined within multiple different data visualisations, the concepts of which have been designed using a list of requirements based on existing literature.
The visualisations show that there is potential for direct use of point clouds in communicating information about spaces to a user, and that discerning between visible and occluded spaces, has potential value to a user orienting themselves within their environment with aid of a geovisualisation.

As a method that can accurately represent 3D spatial information, point cloud visualisation for indoor environments is still a relatively unexplored field of research. Our client for this project, the Dutch National Police, requested a variety of potential solutions for visualising (unfamiliar) indoor environments that can be viewed by both external command centres, and internal operations units. Currently, unknown interior layouts (or layouts that are different in practise to what is stated on paper) can have serious, sometimes even life-threatening, consequences in time-sensitive situations. This project uses a game engine to directly visualise point cloud data input of indoor environments. The primary aim is to find ways of clearly communicating a point cloud of an environment to a layman viewer through intuitive visualisations, to aid decision-making in high-stress moments. The final product is a variety of visualisation concepts, hosted within a game engine in order to allow users to navigate throughout (part of) a building, and customise certain interaction features. To aid the layman viewer, various interpretation methods (e.g. cartography) are considered. The Unreal Engine 4 (UE4) project was designed and developed based on the requirements given by Dutch Police, and consisted of 4 modules: data preprocessing, render style, functional module, and User Interface (UI). An indoor point cloud dataset is used for the implementation, while corresponding mesh and voxel models are also respectively generated and evaluated as reference objects. The implemented software product is evaluated based on a Structured Expert Evaluation Method and finally our project result demonstrates that point cloud has unique advantages for visualisation of indoor environments especially in pre-processing efficiency, detail level, and volume perception.