With the proliferation of the IoT infrastructure, the trajectory data is dynamically emerging. This data originates from a variety of moving objects, containing big volumes of multi-dimensional information such as space, time, semantics etc. The underlying information can be potentially applied to create added value through scientific research, decision-making, emergency management etc.

However, due to the special properties of the trajectory data, namely high frequency, cardinality, dimensionality, heterogeneity etc., traditional data management systems face difficulties in handling such data. Even though some distributed solutions or big data solutions exist in other fields, they are not designed considering the modelling, accessing, distributing and querying characteristics of this special spatio-temporal data.

Given the spatial data management problems, a clustering/indexing solution for high dimensional Point Cloud by Space-filling curve considering the heterogeneous data spatial distribution has been developed, advocated and validated by a series of research finished at the GDMC, TU Delft.

However, it is uncertain whether the framework can be extended to other types of space-related phenomena. Furthermore, whether distributed database techniques can be utilized remains to be explored and what adjustments should be made is still unclear. To some extent, this thesis is an expanded study based on the Point Cloud research mentioned above.

To address these data management challenges, this thesis focuses on trajectory data modelling and compression, indexing and clustering, partitioning and distributing. Also, the querying strategies are studied. More specifically, the three main results of this thesis are:

Model the trajectory as the sequence split by semantic attributes and a spatio-temporal cube. This modelling takes proximity (locality) preservation and trajectory preservation into account at the same time, resulting in a balanced level of flexibility and aggregation, mitigating the storage burden by row-wise compression. For different subdivision resolutions (depth of Octree for space partitioning), the compression ratio can be up to 10.

Access the trajectory data by Space-filling Curve. The Space-filling Curve indexing method maps the 3D (high dimensional) indices to 1D (low dimensional) indices, overcoming the contradiction between high dimensionality and high cardinality. Adaptive Octree is used to mitigate the heterogeneity of the trajectory data. Based on the experiment results, the optimal tree depth is 4 or 5. The query optimization (specifically, the range merging technique) is also preliminarily explored.

Distribute the data on the distributed machines. This distributed deployment results in higher (nearly linearly in the experiment) scalability (horizontal expansion of disk, memory and CPU resources) and speed-up. The Space-filling Curve distributing strategy results in better load-balancing. However, due to the lack of flexibility of the distributed database platform used (specifically, Greenplum), the localization of data and computation (such as local aggregation) is limited.

As the way we interact with maps keeps changing, so do the maps change alongside. And it can be easily pointed out how these changes come alongside a large number of advantages for the average map user, such as quick access to data or the ability to view more or less of the Earth's surface with just a mouse scroll, as well as for specialists such as cartographers or spatial data analysts, as it is now easier then ever to manipulate complex data. That being said, the challenges have also shifted, from the expertise of the map maker to the software solutions which now do all the work.

One of the many challenges imposed by the aforementioned is represented by the way the map generalization process is achieved. This graduation project serves as a continuation to the countless amount of research which has already been performed in this field, with a focus on the niche world on Vario-Scale Maps, and in particular how borders are handled in this generalization process.

There is already a large number of different solutions available, some of them being considered as standard and used by some of the biggest players in the world of geo-information. However, it seems that no single one solution is a `silver bullet`, as they all have their advantages and disadvantages, as well as cases where one generalization workflow is clearly more suited then others.

Considering the actual status quo of the industry, this thesis will take a look at some of these already available solutions on the market, both individually as well as together, and will try to answer the following research question: \emph{To what extent can multiple line-generalization algorithms be (simultaneously) introduced in the Vario-Scale structure such that they preserve the topology and enable an optimal line density (while trying to preserve the characteristics of the initial shape as well).}

To reach an answer, it is necessary to start first from the lowest level, with understanding how line generalization function in different situations, then slowly building up the structure by introducing these new concepts in the broader workflow, to see what impact it has on it as a whole.

Geographic Information (GI) has proven its value with its ability to be used in various fields, which left numerous questions about how this data should be stored, used, and managed. To facilitate the sharing and reuse of spatial data, Spatial Data Infrastructures (SDIs) emerged in the last decades and with the increase in Open Data, the belief emerged that not only the public sector should be involved in SDIs, but also companies and citizens should be included as part of the Open SDI. To evaluate its value and the success of open GI in relation to the participation of users and communities in open SDI is still a challenge. The possibility to improve user participation in open SDI, such as OpenStreetMap, needs to be assessed. This research used preliminary web-based surveys and semi-structured interviews as per Schmidt [2004] with users of the OpenStreetMap, as well as qualitative analyses and combined quantitative approaches, to explore the factors that foster user participation in the community of an open SDI, using a methodology that adapted motivational factors from Budhathoki [2010]. The selection of fundamental intrinsic and extrinsic motivational factors were used as indicators in user participation in OpenStreetMap, for both the survey and the semi structured interviews. Surveys showed that participants in this research were mostly heavy contributors, that primarily who are extrinsically motivated to participate mostly because they believe in the main goal of OSM, they value the community and the development of the platform, and trust the system and its users. Moreover, the users have intrinsic motivations to also participate for fun, instrumentality, altruism, to meet their own needs, alignment with the unique ethos and for learning. One of the conclusions that was drawn from this research is that OpenStreetMap is perceived as an Open Data Ecosystem among its users, yet some diving opinions to the perception about Open Spatial Data Infrastructures. Future development points for OSM are the need for stricter standards and safeguards against hostile commercial or external takeovers if OpenStreetMap wants to effectively engage both the public and private sector. Applying a stricter standard for the metadata of datasets that are used by governmental and external parties should guarantee
further re-use and provide sustainability for OSM. This includes an increase in their outreach through better marketing for the relevant potential users and provide further support systems for a new users. Furthermore, the expansion on different spatial data is also desirable, as well creating linked or integrated data with external open data providers. Current government or private SDIs that are in the process of transitioning into user-centric or open SDIs can apply these factors from this research into their own decision making processes if increasing user participation is their end goal.

The promotion of the data re-usability was the main motivation behind the conception of linked data. Spatial data can also be expressed as linked data. This thesis explores different approaches to utilize spatial data in linked data format, in order to determine if the technologies have matured enough to assist solving spatial problems. More precisely, it attempts to select a position to build a new shopping center in Delft, Netherlands by using relational data and linked data. Five different Linked-data implementations are identified and examined; two of them, a Geographic SPARQL Protocol and Resource Description Framework (RDF) Query Language (a recursive acronym for GeoSPARQL) (GeoSPARQL) endpoint and an API, are offered from Publieke Dienstverlening Op de Kaart (PDOK). The other three approaches are designed using Oracle Spatial and Graph. From the latter three, one approach utilizes linked data stored in a local database while the rest utilize relational data mapped into linked-data. All five approaches are assessed for GeoSPARQL compliance and are compared to a QGIS solution. From the aforementioned approaches, only the two mapping ones have achieved the desired solution. However, none of them fully complies with GeoSPARQL specification hence, further research is necessary.