Spatial data infrastructures (SDIs) are becoming more mature worldwide. However, despite this growing maturity, longitudinal research on the governance of SDIs is rare. The current research examines the governance history of two SDIs in The Netherlands and Flanders (Belgium). Both represent decades-long undertakings to create a large-scale base map. During these processes, SDI governance changed, often quite radically. We analyse written accounts from geo-information industry magazines to determine if the SDI governance of these two base maps can be considered adaptive. We conclude that SDI governance was adaptive, as it changed considerably during the evolution of the two SDIs. However, we also find that most governance models did not hold up very long, as they were either not meeting their goals, were not satisfying all stakeholders or were not in alignment with new visions and ideas. In recent years, the policy instruments governing these base maps became increasingly diverse. In particular, more hierarchical instruments were introduced. Indeed, governance scholars increasingly agree that governance can better respond to changes when a broader mix of policy instruments is applied. Alas, this does not make SDI governance any less complex.

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.

2.1. IntroductIon 2.2. MethodS, data and caSeS 2.3. reSultS 2.4. dIScuSSIon 2.5. concluSIonS CHAPTER 3. UNDERSTANDING GOVERNANCE DYNAMICS: THE GOVERNING SYSTEM OF SPATIAL DATA INFRASTRUCTURES 47 abStract 3.1. IntroductIon 3.2.Governance and the GovernInG SySteM 3.3. reSearch Method 3.4. reSultS 3.5. dIScuSSIon and concluSIon CHAPTER 4. GOVERNANCE INTERACTIONS OF SPATIAL DATA INFRASTRUCTURES: AN AGENT-BASED MODELLING APPROACH abStract 4.1. IntroductIon 4.2. SdI Governance conceptS 4.3. Method 4.4. reSultS 4.5. dIScuSSIon 4.6. concluSIon CHAPTER 5. THE FUTURE OF INSPIRE: EXPLORING GOVERNANCE SCENARIOS FOR THE EUROPEAN SPATIAL DATA INFRASTRUCTURE 105 abStract 5.1. IntroductIon 5.2. Method 5.3. reSultS 5.4. dIScuSSIon 5.5. concluSIon CHAPTER 6. SYNTHESIS 129 6.1. MaIn fIndInGS 6.2. overall reflectIon 6.3. IMplIcatIonS for practIce 6.4. reSearch challenGeS for the future REFERENCES 144 APPENDICES 155 appendIx a. Geo-InforMatIon MaGazIne artIcleS referred to appendIx b. codInG reSultS per caSe and round appendIx c. reSultS and queStIonS expert valIdatIon appendIx d. overvIew, deSIGn conceptS, detaIlS (odd) -SdI Governance Model appendIx e. blank GovernInG SySteM fraMework for applIcatIon In practIce SUMMARY 180 SAMENVATTING 183 ACKNOWLEDGEMENTS 186 ABOUT THE AUTHOR 188 PE&RC TRAINING AND EDUCATION STATEMENT 190is that it has a spatial reference to a location (GSDI 2004). As 'everything happens somewhere', this information is critical in many processes such as disaster management, environmental protection and urban planning (UN-GGIM 2018). In many cases, spatial data is difficult to share, as depending on its scale and level of detail, the datasets can be large and spatial data has its own technicalities (GSDI 2004; van den Brink et al. 2019;Lee and Kang 2015). To make spatial data sharing easier, the spatial data infrastructure (SDI) concept emerged from the 1990s (Masser 1999). Spatial data infrastructuresA spatial data infrastructure (SDI) facilitates and coordinates spatial data sharing and exchange (Crompvoets et al. 2004). The term 'spatial data infrastructure' is interchangeable with the term 'geo-information infrastructure', although the former has become the most popular, especially in scientific literature (Groot 1997). Traditionally, SDIs are broken down in five core components: people, data, standards, access network and policy (see Figure 1.2). As people want to share or access spatial data, they have to go through the three other components: standards are necessary for interoperable data and web services, access network (or technology) is necessary for access to the location where the data is physically stored in order to use it and policy is needed to understand what you are allowed to do with the data (e.g. licensing) at what costs, but also to stimulate or even require data sharing and re-use (Rajabifard and Williamson 2001;Rajabifard, Feeney, and Williamson 2002).