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The development of a European Spatial Data Infrastructure (SDI) officially started with the entry into force of the INSPIRE Directive in 2007. INSPIRE’s implementation phase should be completed by the European Union (EU) and its member states at the end of 2021: a pivotal point to evaluate INSPIRE’s current governance and explore future scenarios. First, INSPIRE’s governing system is evaluated through an online survey by its involved stakeholders. Second, these results are applied in an agent-based model to explore potential governance scenarios and strategies. The results show that strong aspects of INSPIRE’s governing system are the supported vision and its formal structures, such as standards, technology and roles and responsibilities. Weak aspects are the access to resources, especially budget and time resources, and data use. The agent-based simulations show that INSPIRE is probably more constrained by its budget resources than its current dominant hierarchical interaction mix, although a combination of adaptive governance and continuous budget proved the most sustainable governance scenario.

In order to facilitate and coordinate spatial data sharing and exchange, many organisations have developed spatial data infrastructures (SDIs). SDI governance plays a pivotal role in the development and evolution of an SDI, but as SDIs are complex adaptive systems, governing is a challenge. This research therefore proposes a complexity perspective to SDI governance by exploring the use of agent-based modelling to simulate and examine SDI governance interactions. In this agent-based simulation, we examine interactions between SDI stakeholders, data availability and the effects of different governance styles (hierarchical, network and laissez-faire governance) and budget policies. The simulation shows that it is possible to mimic SDI governance dynamics through agent-based modelling. By running different scenarios, it appears that a network approach is more successful compared to a hierarchical or laissez-faire approach. Expert validation shows that overall the results of the simulation are credible and insightful, although improvements can be made to make the model more realistic. With agent-based modelling, SDI governance becomes more tangible and visible, which facilitates discussion and understanding. Agent-based modelling therefore appears to be a helpful new approach in a better understanding of the complexities and dynamics of SDI governance

This contribution tests the added value of including landslide path dependency in statistically based landslide susceptibility modelling. A conventional pixel-based landslide susceptibility model was compared with a model that includes landslide path dependency and with a purely path-dependent landslide susceptibility model. To quantify path dependency among landslides, we used a space–time clustering (STC) measure derived from Ripley's space–time K function implemented on a point-based multi-temporal landslide inventory from the Collazzone study area in central Italy. We found that the values of STC obey an exponential-decay curve with a characteristic timescale of 17 years and characteristic spatial scale of 60 m. This exponential space–time decay of the effect of a previous landslide on landslide susceptibility was used as the landslide path-dependency component of susceptibility models. We found that the performance of the conventional landslide susceptibility model improved considerably when adding the effect of landslide path dependency. In fact, even the purely path-dependent landslide susceptibility model turned out to perform better than the conventional landslide susceptibility model. The conventional plus path-dependent and path-dependent landslide susceptibility model and their resulting maps are dynamic and change over time, unlike conventional landslide susceptibility maps.

In this PhD thesis, I characterized and quantified the spatiotemporal effects of path dependency among landslides in Collazzone study area, in Umbria region, central Italy, and that means landslides themselves increase the susceptibility for future landslides. Such effects was strong in about a decade with an exponential decay response over time. In this aspect, a modified time-variant landslide susceptibility model was proposed reflecting the spatiotemporal effects of landslide path dependency. The effect of landslide path dependency was better captured in pixel-based landslide susceptibility models, and improved the performance and accuracy of these models substantially.