In the field of Geographical Information Science (GIS), geodata transformation and analysis tools often take the shape of software libraries written in system level programming languages.
However, the societal impact of these tools is often limited, as most end users only access these libraries via indirect means: Through bindings in other languages, through plugin in applications, or both.
Additionally, the tools end users end up with are often not composable, and may contain other hurdles like installation or configuration.

The goal of this study is to make core GIS libraries more directly available and composable to end users.
This study presents and prototypes a novel method, centered around a visual programming language to host the functionalities of GIS libraries from within an application, and in a composable manner.
Additionally, the visual language is used to connect these libraries to a user-definable Graphical User Interface (GUI).
This prototype is implemented as a static web application, so that these libraries are directly accessible to end users without installation.
GIS libraries are compiled to WebAssembly, making the library usable in any language, including
this web based visual language by using a ’no boilerplate’ plugin system.
Finally, both scalability to handle sizable datasets, and a rich GUI (3D viewers, file inputs, sliders), are primary design considerations and assessment criteria.

The results show that this specific web based VPL appears to be a feasible method for providing direct access to some native GIS libraries, and does offer a unique set of features not found in comparable visual languages.
The significance of this method, compared to other web based geometry VPLs, lies in the fact that it offers a lenient plugin system, in combination with a range of different GUI nodes, certain "dataflow VPL" properties, and a proposed zero cost abstraction runtime.
All of these features combined lead to a VPL which is able to directly connect GUI components with native GIS libraries, all while remaining scalable in principle.

On a practical level, more work remains to proof this feasibility.
The methodology developed by this study is only theoretically accessible and composable, based on achieved features.
User testing is required to confirm if this method indeed improves workflows, and actually saves time and energy of developers and end users.
Moreover, the prototypical software implementation used is limited and not production ready. Both the fact that the ’no boilerplate’ plugin system cannot be used with C / C++ GIS libraries, and that backend execution is not possible yet, must be improved upon in future work.
Despite this, visual programing, distribution using WebAssembly, and Rust based geocomputation, all proved to be valuable directions of future GIS research.

Solar energy is an important renewable energy source that is already generated by millions of solar panels attached to building roofs throughout The Netherlands. Whether a roof is suitablefor solar panels relies on its type, orientation and whether it is put in shadow by a neighbouring object. This means, the higher the solar potential of a roof, the better.

A solar radiation model is used to determine the solar potential of a roof. Well-known Geographical Information Systems, such as ArcGIS and GRASS GIS provide solar radiation models for raster data. However, raster data does not model the 3D urban environment accurate enough. Vector data is better capable of representing 3D buildings models, but solar radiation models for vector data are not widespread available and are computationally inefficient, because of the shadow casting step.

This research aims at providing an efficient solar radiation model for processing large-scale 3D city models. The 3D BAG data set, containing all the buildings in The Netherlands as vector data, is taken as use case. Their building models are stored in CityJSON format, subdivided into smaller tiles based on the spatial extent. The implemented model in this research, called SolarBAG, takes one or multiple tiles as input, processes the building geometries to compute the solar potential, and outputs an enriched CityJSON file where each building roof consists of yearly solar potential values. Within the process, the building geometries are stored in an Rtree to allow fast retrieval when filtering neighbouring buildings that potentially cast a shadow over another building. The roofs of buildings are sampled into a grid of points to account for the variable solar radiation values on a roof surface caused by shadows of neighbouring buildings. A ray-box intersection method is used to find the neighbouring buildings casting a shadow on another building.

To assess the quality and scalability of the implemented solar radiation method, experiments are conducted. For the quality assessment, the solarpy module used to compute the beam solar radiation, is compared to ground truth values, and the solar radiation model is compared to the solar radiation tool in ArcGIS. For the scalability assessment, the solar radiation model is tested for an increasing number of tiles. Based on the assessments, it can be concluded that the implemented solar radiation model can successfully enrich building roofs with solar potential values for one or multiple CityJSON files. However, there are still some bugs and inconsistencies present in the solar radiation model, and performance gains could still be achieved by neighbour filtering improvements.