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Statistical research at the Joint Research Centre Andrea Saltelli, firstname.lastname@example.org NTTS Conference, Brussels, February 2009
ERAWATCH Unit at JRC-IPTS (Seville) Contact: Pietro Moncada Paterno’ Castello (JRC IPTS) email@example.com
The EU Industrial R&D Investment Scoreboard: analysis of 1000 EU and 1000 non-EU top investing companies in R&D The EU Survey of Business R&D Economic and policy analysis of corporate R&D. Industrial Research and Innovation at JRC - Seville http://iri.jrc.ec.europa.eu/
Industrial Research and Innovation Some Results (1): Nature of the R&D investment gap • EU's R&D intensity deficit is largely explained by the different industrial structure (sectoral composition effect).
R&D rises productivity much more in high-tech sectors than in low-tech ones Industrial Research and Innovation Some Results (2):Econometrics of R&D & firm productivity Source: European Commission, JRC –IPTS (2008) Analysis of 2007 EU Industrial R&D Investment Scoreboard
Agrilife Unit at JRC-IPTS (Seville) Contact: Marc Müller (JRC IPTS) firstname.lastname@example.org
Building an Agro-Economic Modelling Platform:Disaggregated Agricultural Social Accounting Matrices for EU27 (AgroSAM)
Minimisation of Cross Entropy Measure (CE), subject to accounting constraints: • AgroSAMs have to match EuroStat control totals • CE allows specifying confidence in data (higher confidence in cereal, oilseed, and dairy data, lower in fodder crops) • Contribution of EU27 AgroSAMs to GTAP database (2008) • Future Developments • Spatial coverage: Regional SAMs (NUTS2) • Annual coverage: Compilation of SAMs until • 2005 based on observations • 2010 based on projections CAPRI Data EuroStat Data Unbalanced AgroSAM Balancing procedure
Axel Tonini (JRC IPTS, Seville), Roel Jongeneel (LEI, The Hague),2008, Modelling dairy supply for Hungary and Poland by generalised maximum entropy using prior information,European Review of Agricultural Economics 35 (2) (2008) pp. 219-246.Müller, M. and I. Pérez Domínguez (2008): Compilation of Social Accounting Matrices with a Detailed Representation of the Agricultural Sector (AgroSAM). Presented at the 11th Annual Conference on Global Economic Analysis, Helsinki, Finland.Müller, M., I. Pérez Domínguez, and S.H. Gay (2009, forthcoming): Construction of Social Accounting Matrices for EU27 with a Disaggregated Agricultural Sector, IPTS Technical Documentation.
Spatial Data Infrastructures Unit of JRC-IES (Ispra) Contact: Jean Dusart email@example.com
The Spatial Data Infrastructures Unit was established in 2006 as the JRC's response to new policy priorities. One such new priority regards the creation of a European Spatial Data Infrastructure, with a particular focus on the development and implementation of distributed information systems for environmental monitoring through in-situ and Earth observation techniques according to the INSPIRE Directive adopted in February 2007 Our mission is to coordinate the scientific and technical development of the Infrastructure for Spatial Information in Europe (INSPIRE), support its implementation within the Commission and the Member States, evaluate its social and economic impacts, and lead the research effort to develop the next generation of spatial data infrastructures Spatial Data Infrastructures Unit (IES)
Context: One method of storing spatial information is by using geographical grids Equal area grid suitable for generalising data, statistical mapping, analytical work INSPIRE Directive complemented by Implementing Rules foresees the definition of a Pan-European Grid based on a commonly agreed reference system (ETRS89-LAEA) Grid defined as hierarchical (power of 10) with associated coding system JRC’s role as technical coordinator of INSPIRE : - identify user requirements and develop recommendations for grid specifications - testing use case implementations Harmonised multi-resolution geographical grid (IES) Applications: European Population Grid (JRC-EEA) Multi-scale Soil Information System (Soil Action) Eco-pedological Map for the Alpine Territory (Soil Action) SRTM DEM for Europe, Corine Land Cover, LUCAS, … Grid 100 km Grid 10 km 0,0
MARS Unit, JRC-IPSC (Ispra) Contact: Javier Gallego firstname.lastname@example.org
Role of JRC on LUCAS 2006: Optimising the sample Efficiency assessment Main task for 2009: Helping Eurostat to adapt 2006 sample to new priorities LUCAS (Land Use/Cover Area frame Statistical Survey) • 2001-2003 Relative efficiency • 2006
Fine resolution (1ha) Downloadable from EEA Population density grid of the EU Initial data: population per commune • Downscaling • LUCAS • Reference data
7th FWP Mars FOOD Mars STAT MARS Crop Yield Forecasting System • In Europe • 35 countries covered • 11 crops monitored • 33 years of meteo and agrometeo data (daily data from ~3000 stations) • 20 crop’s indicators are daily simulated by crop models • 21 years of low resolution satellite information
Rickety Numbers International rankings of higher education lack statistical robustness Michaela Saisana, Beatrice D'Hombres, Andrea Saltelli UNE ÉTUDE QUI MET EN CAUSE LE CLASSEMENT DE SHANGHAÏ See www.lemonde.fr Saturday 15 November 2008 http://www.lemonde.fr/archives/article/2008/11/14/vers-un-classement-europeen-des-universites_1118448_0.html
Global sensitivity analysis Courses every year Venice Brussels Ispra … 2000 2004 2008