Urban green space (UGS) has many environmental and social benefits. UGS provision and access are increasingly considered in urban policies and must rely on data and indicators that can capture variations in the distribution of UGS within cities. There is no consensus about how UGS, and their provision and access, must be defined from different land use data types. Here we identify four spatial dimensions of UGS and critically examine how different data sources affect these dimensions and our understanding of their variation within a city region (Brussels). We compare UGS indicators measured from an imagery source (NDVI from Landsat), an official cadastre-based map, and the voluntary geographical information provided by OpenStreetMap (OSM). We compare aggregate values of provision and access to UGS as well as their spatial distribution along a centrality gradient and at neighbourhood scale. We find that there are strong differences in the value of indicators when using the different datasets, especially due to their ability to capture private and public green space. However we find that the interpretation of intra-urban spatial variations is not affected by changes in data source. Centrality in particular is a strong determinant of the relative values of UGS availability, fragmentation and accessibility, irrespective of datasets.

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.

This paper presents a method for determining and mapping suitable locations for development using Multi Criteria Analysis and the Analytical Hierarchy Process and considering uncertainties in the process. The method is applied to the case study of Howth (Dublin), where development suitability is assessed against specific protection and conservation areas as well as ground water vulnerability. Uncertainty is incorporated using a Monte Carlo simulation into the Analytical Hierarchy Process calculations to determine criteria weightings. A map is derived, which includes, for all locations, both site suitability for development and the level of uncertainty attached to this suitability. The map combines a double categorization of suitability and uncertainty. The method allows for increased transparency in decision making regarding site suitability for development, as well as increased confidence in decision making to allow for reduced risk in terms of the potential impact of development.

Aim: Wild bees still face striking shortfalls in knowledge of biodiversity in key regions of the world. This includes Europe, where despite a long tradition of data gathering, the continental scale distribution patterns of wild bees have not been systematically analysed to date. This study aims to characterise large-scale biodiversity patterns to: (i) understand spatial-temporal heterogeneity in large-scale databases, (ii) locate genuine diversity hotspots and their relationship with biogeographical patterns or habitats of interests and (iii) identify understudied species and areas to further design conservation actions for most at risk species in key regions. Location: Europe.Taxon: Bees. Methods:We present a continental and standardised study of bee taxonomic and phylogenetic diversity patterns in Europe, using a large compilation of occurrence records of nearly three million validated occurrence records for 1515 wild bee species.Results: Southern and eastern Europe suffer from the largest gaps in data availability while northern and western regions benefit from better historical coverage. Our models show that higher wild bee diversity in Europe is hosted in xeric, warm areas, as highlighted by a clear latitudinal gradient. However, phylogenetic diversity is predicted to be more homogenous across Europe than taxonomic diversity, suggesting that policies and strategies targeted to protect species richness may differ from those targeting greater phylogenetic diversity. B I OS K E TCHNicolas Leclercq is interested in the patterns of biodiversity at large scales. His work often focuses on wild bee diversity. He and the other authors collaborate on questions of wild bee diversity and their conservation in their own geographical region.