In this thesis we present a computational framework, which allows simulating residential location choice. The specific application of the model focuses on representing populations under disaster risk. The aim of the tool is to enable public planning agencies to explore the synthesized households’ location choice under risk and different spatial, financial policy scenarios. The simulated urban system is represented as an agent-based model, where the households are the agents choosing between discrete options to relocate from one house to another. Their choice behavior is built upon a notion, that people make decisions based on regret. This is done with the help of Random Regret Minimization (RRM) model, allowing to capture varying levels of regret (profundity) and enabling incorporating multiple attributes of different dimensionality. Given the agent heterogeneity and the changing availability of the building stock, the choice sets are dynamic. Therefore, the traditional RRM approach would not return stable results: with every new option housing available it would have to be re-calibrated. As a solution, we propose re-interpreting the classical c{RRM} model by scaling the beta values by the choice set attribute variance. This allows to represent $eta$ as a unit-less preference weight, associated with a homogeneous population group. We apply the framework to two different scale case-studies in an earthquake-prone area in Groningen province, The Netherlands. The data used for simulation includes several public and private spatial datasets, as well as aggregate level statistical data to synthesize the properties of the households. As a showcase, we applied the simulation assuming homogeneous and equal preference weights for 7 optimization criteria. These criteria relate to static properties of the building stock and household-related dependencies to the network (job, school locations). To further exhibit model usability within public sector agencies, we also apply the model on several financial policy scenarios. The output of which is captured on both aggregate and semi-disaggregate levels, allowing for interactive exploration of the effects of the proposed scenarios. The model outcomes correspond to the expectations set prior to simulation. It showcases convergence, anticipated optimization behavior and spatial patterns, corresponding to the building stock properties of the region.

Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an extension on static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy. Sensors attached to it include a hygrometer, thermometer and microphone. The emphasis of the research was on localisation of the sensors, to put the gathered sensor data into geographical context. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. The GPS module was only used for composing the radiomap. When the radiomap is completed, the module can be switched off, only to be switched on for periodical updates of the radiomap. The quality of the radiomap methodology was evaluated by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The correctness can be improved by having a longer training period. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Vehicle requirements were specified as driving through the city centre and having a schedule and route producing as little noise, heat and air pollution as possible. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size. To decrypt the sent message and store it in a meaningful database, Node-RED was used. Despite visualisation of measurements showed promising results, there is margin for improvement as far as data capturing is concerned.

This paper introduces the Open Spatial Data Infrastructure (SDI) Assessment Framework as a new approach for assessing the openness of SDIs. Open SDIs are SDIs in which non-government actors such as businesses, citizens, researchers and non-profit organizations can contribute to the development and implementation of the SDI, use spatial data with as few restrictions as possible and benefit from using these geographic data. A pilot application of the new framework resulted in the Map of Open SDI in Europe, which aims to show the level of openness of national SDIs in Europe. The map could become a relevant and practical tool that shows the status of Open SDIs in Europe and supports decision makers and practitioners in making their own SDI more open.@en