This project evaluates the suitability of 3D interior space models acquired using Apple’s RoomPlan API for daylight simulations. The main contribution is a Python-based tool that converts RoomPlan output into HoneyBeeJSON by automatically reconstructing the ceiling and adding window frames, which are missing in RoomPlan’s output. Although RoomPlan is also able to capture furniture, these elements were not used in the geometric evaluation or in the daylight simulations. The resulting models can be directly used in Grasshopper for daylight simulations, reducing the modeling time required by practitioners. To assess the suitability of RoomPlan, three office interiors were scanned using a TLS and modeled both manually and with an iPhone 12 Pro.
The manual models were used as ground truth. For each room, a geometric evaluation and a daylight simulation evaluation were performed using three model versions: manual, RoomPlan with extruded window frames, and RoomPlan without extruded window frames. For both the geometrical and the daylight performance evaluation, it is apparent that the windows' frames extrusion is significant to achieve more accurate results. Geometric accuracy was evaluated using Chamfer and Hausdorff distances, showing good overall accuracy. However, errors were observed in wall heights when the ceiling was not clearly visible and in the separation of windows located close to each other. The models were used for point-in-time grid-based illuminance and view-based luminance simulations in Grasshopper using Honeybee.
For the illuminance simulations, the MAE is approximately 269 lux and the MAPE is 19.5%. For DGP, the MAPE is 7.6% for the RoomPlan models with extruded window frames, with only one misclassification of the DGP category. The results indicate that RoomPlan can be used for visual comfort studies but not for daylight availability studies. Despite these results, suggestions for further work are given, considering both the geometrical and the daylight simulation performance evaluation of the RoomPlan models. ... Read more

Redevelopment of inner-city areas is a complex process involving multiple stakeholders, extensive timelines, and a variety of governance structures and financial mechanisms. The public party is primarily responsible for the quality of public space in the Netherlands. However, the municipalities face increasing financial challenges while private developers gain more land and thus more benefits from a high quality public space. To create those high quality and attractive places, placemaking can be introduced in the area redevelopment. It can be temporary and strategically applied to increase the attractiveness of the area, which results in increased private real estate values while it is publicly funded or subsidized. If private developers engage in placemaking, municipal spending can be reduced and the expenses and benefits of placemaking can be more evenly distributed. Therefore, this research examined a type of financing method, called ‘land value capture’, that might be utilized to engage private developers in placemaking projects. The research question that is addressed is as follows: To what extent can land value capture be applied in governance of area redevelopment to involve private developers in placemaking projects in the Netherlands? ... Read more

The “affordability of housing” is generally defined as affordable housing for those with median household income (Eurostat, 2018). If not addressed effectively by policymakers, the unaffordable housing gap is expected to affect 1.6 billion people around the world by the year 2025 (McKinsey, 2014). The unaffordable housing in Amsterdam specifically harms the financial and social well-being of the residents of Amsterdam. Therefore, this research aims to grant a contribution to the field by using the Bayesian modelling methods on private rental market prices to reduce unaffordable housing issue. To investigate the issue, the researcher analyses the literature on urban economic models, the use of models in policy making and, collects data from the national database and online rental housing agencies. With the use of hierarchical Bayesian modelling and exploration tools, the relations between house features and local characteristics are explored and two price prediction models are built by using local house features such as size, bedroom number, distance to city centre and district category. The researcher finds several demand profiles and detected a strong negative correlation between rental prices and some industrial and business locations, which might provide an opportunity for city planners to combine the development of these locations with house supply injections to create more affordable housing. Moreover, from the two prediction models, the first model investigated the average district expensiveness better than conventional metrics by increasing its prediction accuracy and ability to quantify uncertainty. Furthermore, the second model categorized the Amsterdam districts according to the preference profiles obtained by model parameters to find most suitable districts for middle-income households. In both models, the location parameter is found to have the highest impact on rent prices. The research provides informative demand profile findings and a descriptive plan on housing supply injections which can be useful for policy makers. Moreover, the policymakers can benefit from the use of advanced model techniques to better assess the spatial housing market according to the city needs. Furthermore, the research evaluates the effect of local factors on rent prices in order to customize development plans to meet the citizen’s needs more robustly. Lastly, besides benefiting from policymaker’s improved actions, middle-income tenants themselves can also use the research findings to make more informed affordable housing decisions. ... Read more

Innovation districts are more and more seen as the answer of cities to the ever changing economy. They can as well be described as urban strategies for economic development and urban competitiveness. Around the world innovation districts pop up and seem to provide the perfect and required environment for an innovation ecosystem to which talent and businesses are attracted. Katz and Wagner define these districts as “geographic areas where leading-edge anchor institutions and companies cluster and connect with start- ups, business incubators and accelerators” (2014, p.1). In theory, the success of urban innovation districts relies on the balance of three types of assets: physical (buildings, parks, plazas), networking (events, workshops) and economical (start-ups, businesses, shops) assets (Katz and Wagner, 2014). Research shows that dense, inner-city locations combine a critical mass of human capital, vital physical conditions, the right amenities and different sorts of proximity for knowledge exchange that enable businesses to innovate and grow (Morrison, 2014).
It is in these districts that working, living and recreating fade off and that horizontal networking between a diversity of people is becoming increasingly important for innovation. Entrepreneurs and start-ups are considered economical assets in this respect and are crucial players in such districts as they tend to influence economic and job growth. Although they often lack the skills and experience, lack of finance, resources and means needed to do the job, they inhibit a great potential to drive and sustain innovation (Nguyen, 2016). To open up the benefits startups can provide, it is essential to understand how urban innovation districts can contribute to the development of startups. This research therefore focuses on the physical conditions innovation districts should provide and how these can facilitate and stimulate their development. This is investigated on in this research by a qualitative comparative case study within the planned Central Innovation District The Hague. ... Read more