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.

We are now gradually entering the era of big data - maybe a bit too much of a buzzword, but it is not lied. Technology is evolving fast, enabling faster and more efficient data acquisition, storage, retrieval and processing. Point cloud datasets are such a type which relies on large files and lots of processing power. The rather fast evolutions in technology enable the shared idea between Delft University of Technology and Fugro of an ‘Open Point Cloud Map’. This Open Point Cloud Map aims at making point cloud datasets easily available to the public, even letting them performsimple analysis. Both Fugro and TU Delft want to take lead in development of such an environment; three student teams from TU Delft thus form a partnership with Fugro to kick-off three in-depth researches which would result in one step closer to the vision of OPCM. The ChronoCity team will focus on the time-component (Figure 1.1) in the acquainted point clouds, while the other two teams focus on location-dependency and different scales and granularities of the datasets. The Chronocity-team strives to create an online interactive tool which gives the user the ability to view, explore and analyze massive point cloud datasets on-the-fly. Since the limited timespan in which the project should take place this would not yield a fully optimized application, but at least the general principles are defined and evaluated on for a more defined future in the development of the OPCM. A large portion of the efforts will go into making the data and analyses available to the public - in an interactive and user-friendly way; because without this availability, the underlying principles are not brought to the public also. Regarding these underlying principles the most important one is change detection. During the project a suitable algorithm is designed and evaluated for detecting new, removed and changed geometric points.

This synthesis project is focused on implementing an Internet of Things (IoT) network to measure environmental data in the city of Delft. This network consists of sensor platforms that are placed in the urban environment. Each sensor platform is mounted on fixed locations and it is not moved during the measurement time. The aim is to raise community’s environmental awareness to improve the quality of the environment. Recent developments in technology made it possible to fabricate small, efficient, and reliable sensors boards which are the base of these sensors platforms and making them efficient and reliable. Sensor boards like Arduino, Raspberry Pi, and LoPy are some examples of these small sensor boards. In this project, the LoPy is used which is a sensor board that is equipped with Bluetooth Low Energy, Wifi and a LoRa radio. This last one is a communication technology that makes longer communication distances possible. The sensor network measures four different environmental indicators that will be distributed to the public: temperature, humidity, noise and air quality. The network then communicates via LoRa this data to one centralized server where the data is stored, processed and sent back to the citizens. This data is made publicly accessible to academia, citizens and the stakeholders alike. The network is also made interactive, people who pass by can interact with the sensors and request specific environmental data in real time. The sensor network has been build and deployed in the city. During the uptime of the network it succeeded to provide the data to the citizens via the feedback mechanisms: a website with a dashboard and an automated twitter account. Local differences have been measured with temperature and humidity sensors. With regard to the noise sensor and air quality sensors no definitive conclusions could be drawn.

This research focuses on the analysis of Floating Car Data (FCD) data to understand sustainable transportation behavior in the area of Amsterdam. Using data collected by mobile devices, trips are analyzed by their distance and transportation method. Sustainability is a relevant factor to the social and scientific community. With increasing population and growing cities, the impact of travel on the environment is also increasing. New policies are required to stimulate a more sustainable approach to transportation. Research on sustainable travel behavior provides input for the policy makers. The main research question is defined by what extent FCD can be used to provide insights in the sustainable mobility behavior in Amsterdam. In the existing literature, there are four objectives for sustainable mobility: hazards reduction, travel reduction, modal shift and accessibility. The modal shift (e.g. replacing care usage by public transport) is one of the key drivers for this research. To quantify the behavior, different sustainable mobility indicators are identified to determine the sustainable direction related to the mode of transportation. In the literature there are many methods to quantify sustainable transportation behavior ranging from the traditional methods like counting and surveys to modern approaches including smartphone and sensor data. The methodology used starts with the problem statement and literature review. The data sets are selected and analyzed, providing the results and conclusions. A decision tree is used for categorizing different trips people make, where a difference between short and longer distance trips is made. This research makes use of a wide range of tools ranging from PostgreSQL databases to advanced features of ESRI to visualize data. Five different available data sets are analyzed for their suitability for this research. Based on several requirements like availability, having Origin and Destination (OD) information, usability and documentation, the data sets are assessed and two data sets (Google OD and LMS) are chosen to be analyzed in more detail. The data is filtered and cleaned to make sure it fits the scope (Amsterdam) and the two sets are compared to each other. The trips are split into short and long distance trips, where for both a detailed analysis is performed on the trips which can be easily replaced by more sustainable trips. Regarding the short distance trips, analysis shows that walking and biking are the most common option in busy areas as the city center and the business district. The less sustainable car trips for short distances show several patterns in the city. Using interactive maps, these patterns are identified and both data sets are compared. For longer distance car trips, train transportation is the more sustainable option. There is however a tradeoff between the most sustainable and least time-consuming transportation option. This makes that train trips are not always the most logical or even sustainable choice for transportation. A method is developed and applied to the data set to test if a train trip is a realistic alternative. The results show for both data sets that there is a significant amount of car trips which could easily be replaced by the train. During the analysis of the data for long distances, a problem has been found with the Google data set. It seems that this data set is showing most of the time the same noise. This phenomenon was found by comparing the different modes of transportation available in the Google data set. The error is not in all the data, but it seems to have an impact on the data set. Conclusion is that the data provides many insights on travel behavior and different available data sets can be linked together to provide deeper insights. The Google data set shows interesting results for shorter distances but gives less reliable results for longer distances. The LMS data set is used to compare the results for both short and long distances. The use of FCD data to study and stimulate sustainable mobility behavior seems very promising, but the quality of the available data sets has a large influence on the usability.

Precise and comprehensive knowledge about 3D urban space, critical infrastructures, and below ground features is required for simulation and analysis in the fields of urban and environmental planning, city administration and disaster management. In order to facilitate these applications, geo-information about functional, semantic, and topographic aspects of urban features, their mutual dependencies and relations are needed. Substantial work has been done in the modeling and representation of above ground features in the context of 3D city modelling by means of CityGML. However, the below ground part of the real world, of which utility networks form a big part, is often neglected in 3D city models. At the same time, several existing utility network data models exist. These are, however, commonly tailored to a specific domain and not suitable for the integrated modeling and representation of utility networks and city objects in 3D urban space This research proposes a 3D data modeling approach for integrated management of below ground utility networks features (viz. electricity and sewer) and related above ground city objects (viz. streetlights and manhole covers). The data modeling approach is successfully examined by implementing relationships between 1) the below ground electricity network and above ground streetlights and 2) between the sewer network and the above ground manhole covers. Having existing utility network data and city objects as input, a file in CityGML Utility Network ADE format is created. The manipulation of the data structure and content, according the proposed data modeling approach, is completed in FME. The output CityGML dataset allows interoperability but may become very large and objects may be arbitrarily nested leading to complex data structures. Therefore carefully optimized database schemas are required that enables efficient storage, management and data access of the CityGML data. The object-oriented CityGML data model, including the Utility Network ADE, is mapped to a relational database by means of the 3DCityDB. Subsequently, the CityGML data is inserted into the derived relational database. Several relevant (network) analyses are performed by querying the designed relational database. It shows the possibility to simulate what network features are affected by e.g. a utility strike by means of pgRouting and visualization in a GIS. This research made one of the first attempts to thoroughly model existing utility network data and city objects according the CityGML Utility Network ADE. Following are further research that could optimize the proposed data modeling approach for better decision making in the field of asset management: - Modeling multiple different utility networks and city objects - Modeling in a higher LoD - Detailing the CityGML Utility Network ADE classes and use - Better investigating on more types of analyses - Implementing larger datasets - Implementing datasets with a different accuracy - Exporting a CityGML file from the relational database - Better investigating on visualization of the data - Investigating on how to model different types of relationships

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.

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.