BiodiverCities: A roadmap to enhance the biodiversity and green infrastructure of European cities by 2030

BiodiverCities is a European Parliament Pilot Project, developed with the aim of enhancing the use of Urban Green Infrastructure (UGI) to enhance the condition of urban ecosystems, providing benefits for people and nature. In this report, an evaluation around the most appropriate reporting unit for an urban ecosystems assessment is carried out, comparing Functional Urban Areas (FUA) and Local Administrative Units (LAU). Furthermore, UGI are assessed from a multi-scale perspective. The status and scenarios of UGI in European urbanised areas is first analysed measuring the urban green areas and the tree canopy cover. Secondly, the contribution of UGI to the overall European Green Infrastructure (EU-GI) is quantified, evaluating the respective role of FUA and LAU. Finally, the effect of urban characteristics on biotic homogenization is analysed exploring how urbanised areas impact on avian population and communities in French cities. The results of this study will inform the development of a roadmap for greening cities in Europe in the 2020-2030 decade

EU-wide methodology to map and assess ecosystem condition

The EU Biodiversity Strategy for 2030 calls for developing an EU-wide methodology to map, assess and achieve good condition of ecosystems, so they can deliver benefits to society through the provision of ecosystem services. The EU-wide methodology presented in this report addresses this methodological gap. The EU-wide methodology has adopted the System of Environmental Economic Accounting - Ecosystem Accounting (SEEA EA) as reference framework. The SEEA EA is an integrated framework for organizing biophysical information about ecosystems, adopted as a global statistical standard by the United Nations. The SEEA EA is also the reference framework under the proposal for the amendment of Regulation (EU) No 691/2011 on European environmental economic accounts. Building on previous work done within the MAES initiative, the EU-wide methodology presents useful insights to operationalise the SEEA EA at EU level by integrating different EU data streams in a consistent way with this global statistical standard to consistently map and assess ecosystem condition in the EU across all ecosystem types. The adoption of the SEEA EA framework offers the flexibility to integrate different data flows, leveraging the use of available EU data, such as data reported by MS under EU legislation and EU geospatial data. The EU-wide methodology. The implementation of the EU-wide methodology, making use of available data, will provide the scientific knowledge base to support a range of policies and legal instruments

NRand-K: Minimizing the impact of location obfuscation in spatial analysis

Location privacy, or geoprivacy, is critical to secure users’ privacy in context‐aware applications. Location‐based services pose privacy risks for users, due to the inferences that could be made about them from their location information and the potential misuse of this data by service providers or third‐party companies. A common solution is to apply masking or location obfuscation, which degrades location information quality while keeping a geographic coordinate structure. However, there is a trade‐off between privacy, quality of service, and quality of information, the last one being a valuable asset for companies. NRand is a location privacy mechanism with obfuscation capabilities and resistance against filtering attacks. In order to minimize the impact on location information quality, NRand‐K has been introduced. This algorithm is designed for use when releasing location information to third parties or as open data with privacy concerns. To assess the impact of location obfuscation on exploratory spatial data analysis (ESDA), a comparison is performed between obfuscated data with NRand, NRand‐K, and unaltered data. For the experiments, geolocated tweets collected during the Central Italy 2016 earthquake are used. Results show that NRand‐K reduces the impact on ESDA, where inferences were similar to those obtained with the unaltered data