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The increasing complexity of urban planning projects today requires new approaches to better integrate stakeholders with different professional backgrounds throughout a city. Traditional tools used in urban planning are designed for experts and offer little opportunity for participation and collaborative design. This paper introduces the concept of geospatial tangible user interfaces (GTUI) and reports on the design and implementation as well as the usability of such a GTUI to support stakeholder participation in collaborative urban planning. The proposed system uses physical objects to interact with large digital maps and geospatial data projected onto a tabletop. It is implemented using a PostGIS database, a web map server providing OGC web services, the computer vision framework reacTIVision, a Java-based TUIO client, and GeoTools. We describe how a GTUI has be instantiated and evaluated within the scope of two case studies related to real world collaborative urban planning scenarios. Our results confirm the feasibility of our proposed GTUI solutions to (a) instantiate different urban planning scenarios, (b) support collaboration, and (c) ensure an acceptable usability. [ABSTRACT FROM AUTHOR]

Urbanization is leading us to a more chaotic state where healthy living becomes a prime concern. The high-rise buildings influence the urban setting with a high shadow rate on surroundings that can have no positive impact on the general neighborhood. Nevertheless, shadows are the main factor of defeatist virtual settings, they are expensive to render in real-time. This paper investigates how the amount of sunlight varies by season and how seasons can indicate the time of year to understand how shadows vary in length at different times of the day and how they change over the seasons. We propose a novel efficient (fast and scalable) algorithm to calculate a 2.5D cast-shadow map from a given LiDAR-derived Digital Surface Model (DSM). We present a proof-of-concept demonstration to examine the technical practicability of the introduced algorithm. Tensor-based techniques such as singular value decomposition, tensor unfolding are examined and deployed to represent the multidimensional data. The proposed method exploits horizon mapping ideas and extends the method to a modern graphics algorithm (Bresenham's line drawing algorithm) to account for the DSM's underlying surface geometry. A proof-of-concept is developed utilizing Python's TensorFlow library, exploring data flow graphs and the tensor data structure. The heavy computer graphics algorithm used in this paper is parallelized using PySpark. Results explicate significant enhancements in overall performance while preserving accuracy at negligible variations. [ABSTRACT FROM AUTHOR]

Buildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities in assessing the energy consumption of large building stocks and reduction potentials. However, current models rarely consider uncertainty associated to building usage and characteristics within the stock, resulting in potentially biased results. This study presents a generic model simplification approach using uncertainty propagation and stochastic sensitivity analysis to derive fast simplified (surrogate) models to estimate the current building stock energy use for improved urban planning. The methodology includes an engineering-based energy model as input to global sensitivity analysis (GSA) using the elementary effects (EE) screening and Sobol’ method for key parameters identification and regression analysis to derive simplified models for entire building stocks. The application to the housing stock of Esch-sur-Alzette (Luxembourg) showed that the parameters explaining most of the variability in final energy use for heating and domestic hot water are floor area, set-point temperature, external walls U-values, windows and heating system type. Results of the simplified models were validated against measured data and confirmed the validity of the approach for a simple yet robust assessment of the building stock energy use considering uncertainty and variability. [ABSTRACT FROM AUTHOR]

Due to the rapidly increasing percentage of the population living in urban centres, there is a need to focus on the energy demand of these cities and the use of renewable energies instead of fossil fuels. In this paper, we develop a spatial model to determine the potential per parcel for using shallow geothermal energy, for space heating and hot water. The method is based on the space heating and hot water energy demand of each building and the specific heat extraction potential of the subsurface per parcel. With this information, along with the available space per parcel for boreholes, the percentage of the energy demand that could be supplied by geothermal energy is calculated. The potential reduction in CO 2 emissions should all possible geothermal energy be utilised, is also calculated. The method is applied to Ludwigsburg, Germany. It was found that CO 2 emissions could potentially be reduced by 29.7% if all space heating and hot water requirements were provided by geothermal energy, which would contribute to the sustainability of a city. The method is simple in execution and could be applied to other cities as the data used should be readily available. Another advantage is the implementation into the web based Smart City Energy platform which allows interactive exploration of solutions across the city. [ABSTRACT FROM AUTHOR]