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Keywords: GIS Urban planning Energy demand Geothermal energy iGuess Smart city energy a b s t r a c t 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.

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Abstract. Many different algorithms can be used to optimize spatial network designs. For spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria. The objective of this work is to compare the performance of different optimization algorithms for both criteria. Both adding to and deleting measurements from an existing network are considered. We applied four algorithms to three datasets with known variogram models, in all cases taking the mean universal kriging variance (MUKV) as the interpolation accuracy measure. Preliminary results show that greedy algorithms that minimize the entropy perform best, both in computing time and MUKV.