Indoor localisation methods are an essential part for the management of COVID-19 restrictions, social distancing, and the flow of people in the indoor environment. Moving towards an open work space in this scenario requires effective real-time localisation services and tools, along with a comprehensive understanding of the 3D indoor space. This project’s main objective is to analyse how ArcGIS Indoors can be used with location awareness methods to elaborate and develop space management tools for COVID­-19 restrictions in order to reopen the workspace for TU Delft Campus. This was accomplished by using six Arduino micro controllers, which were programmed in C++ to scan all available Wi-­Fi fingerprints in the east wing of the Faculty of Architecture and the Built Environment of TU Delft and send over the data to an ArcGIS Indoor Information Model (AIIM). The data stored on the AIIM is then accessed using the app on the user’s Android device using REST Application Programming Interface (API) where a kNN based matching algorithm then identifies the location of the user. The results show that the localisation is not consistent for rooms that are directly above each other or share common access points. However, when functioning to locate different tables inside a room, the system proved to uniquely distinguish between the specific tables. As a result, we can conclude that based on the size of the rooms, more Arduino devices should be installed to achieve an ideal accuracy. Finally, recommendations are made for the continuation of this research.

As we advance technologically, spatial data gains importance with its ability to be used in various fields. Traditionally, most spatial data are collected by government organisations. To facilitate sharing and reuse of spatial data, Spatial Data Infrastructures (SDIs) have been developed in the last decades. More recently with the growing maturity of open data, the notion emerged that not only government organisations should be involved in SDIs but also companies and citizens should be included to upload and share their data in an Open SDI. One of the Open SDI challenges is how to engage and continuously motivate non-governmental parties to participate as an open data community. This research used OpenStreetMap (OSM) as a case study to explore the factors that foster user participation in an Open SDI community, as we consider OSM to be a non-governmental Open SDI ecosystem. Using a qualitative and a quantitative approach, we found that participants that were mostly heavy contributors are highly motivated to participate mostly because they believe in the main goal of OSM, they value the community and the development of the OSM platform and trust the system and its users. Moreover, they also participate for fun, instrumentality, altruism, meeting own needs, and for learning. Although OSM participants perceive OSM as an Open Data Ecosystem, there are dividing opinions of the perception of Open Spatial Data Infrastructures. The participants’ concerns and recommendations for future development of OSM can be used by government organisations in the development of Open SDIs.@en

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.