Claude GRASLAND on behalf of M4D

1. Monitoring and benchmarking the European territory The M4D contribution: Time-series, Urban Data and Case Studies 4-5 December 2013 Vilnius, Lithuania Claude GRASLAND on behalf of M4D 1

2. Introduction : which are these 4 countries ? 2

3. Introduction : which are these 4 countries ? 3

4. Introduction : which are these 4 countries ? 4

5. 1. Search Interface, core data and time-series 5

6. Data, data, data… ESPON Seminar in Vilnius, Dec. 2013 ESPON Seminar in Lillehammer, Dec. 2003 … • Need for data at the beginning of TPGs projects. • Need for the most recent data. • Need for measuring dynamics (managing NUTS change) 6

7. The M4D answer (2011-2013) M4D Core Indicators • Total population: 1990 – 2011, NUTS 0-1-2-3 • Age structure (5 years): 2000 – 2009, NUTS 0-1-2 • Births / deaths: 2000-2010, NUTS 0-1-2-3 • GDP (euros/pps): 1999-2008, NUTS 0-1-2-3 • Active population: 1999-2008, NUTS 0-1-2 • Unemployed/employed population: 1999-2008, NUTS 0-1-2 • ESPON Area + Candidate Countries • No missing values But.. 7

8. The M4D answer (2011-2013) • All Eurostat values have been kept, NUTS 2006 version • Potential problems of statistical discontinuities. • Missing values have been estimated within the ESTI framework (time, space, thematic, source dimensions) • Short time-series to statistically ensure the quality of the estimation, no margin of error. • A manual process • Several months of work, errors may remain, difficult to update. A first useful attempt A non-sustainable solution 8

9. The M4D answer (2011-2013) Total population 1990-2011 dataset Need temporal smoothing? New censuses heterogenous methods for gathering data 9

10. How to get M4D time-series? Open the Search Query page Search by theme/policy/project/keyword Open the data filter Click on time-series option 10

11. The M4D answer in 2014 • We can estimate missing values in the official series data to create the best official time series Green cells have complete official data; red cells require estimation Before estimation After estimation 11

12. Next steps for the M4D time-series… smoothing discontinuities • The official series are not always smooth – here the year-on-year growth rates reveal unexpectedly rapid changes between 2002/3 and 2003/4 in some of the series. • If there is no apparent reason for these changes we will locally smooth the outliers to give the best homogenised series. 12

13. What could be strategic for time-series creation? • Official data and smoothed data Need for official data Need for smoothed data European Commission website ESPON ET 2050 ESPON DEMIFER • Benchmark with policy objectives. • One-shot results (situation in …?) • Need temporal smoothed input data to propose relevant forecasts. 13

14. What could be strategic for time-series creation? M4D Draft Final Report (June 2014) • Feedback on 7 years of database project. • Recommandations for 2014-2020. 14

15. 2. Urban data 15

16. Several European urban databases Already integrated in the Espon DB Waiting for the final version

17. Several European urban databases • 4 different urban DB have been expertized by ESPON M4D • 2 morphological delineations (continuous built-up areas) • 2 functional urban areas • Among them, 3 have already been integrated into the ESPON DB portal • The last one, the Harmonized LUZ (Urban Audit 2012) should be uploaded when available

18. Two complementary urban databases UMZ – 4304 cities Harmonized LUZ – 695 cities provisional version (Dec. 2012)

19. The small & medium sized cities: another major issue for European planning and urban policies 12 LUZ 55 UMZ Advantage of UMZ DB: small & medium cities EXIST

20. Two complementary urban databases • Importance of harmonized LUZ • For the first time, an official harmonized DB • Integrate large perimeters that functionally depend on core cities • Should be related to various socio/economic/demographic indicators (Urban Audit) • Importance of UMZ • Small&medium city sized cities are captured • Major policy stakes • Future urban objectives in structural funds • Allow a better knowledge of territorial dynamics

21. Two different ways for attributing indicators into urban objects LAU2 (SIRE DB) Grid data (GeoStat 2006 / JRC / Corin Land Cover) Urban Databases Problem of availability of time series Few indicators at the moment

22. Population urban objects to LAU2 data need a dictionary Elaborating the UMZ-LAU2 dictionary: a very complex task UMZ – LAU2 dictionary Available in the ESPON DB portal

23. Urban OLAP Cube: a method to create grid indicator from administrative levels (NUTS2/3) GEOSTAT Pop. Grid 2006 Urban Objects OLAP Database Measures 100 x 100 m Grid UMZ LUZ ESPON URBAN OLAP Cube MUA FUA ! Area (1Km²) Data Source • The data source used to populate the urban objects depends on their definitions: • Morphological objects can be populated by Local or grid data • Functional objects can be populated by these one and NUTS data disaggregated LAU 2 NUTS LAU 2 End Users Urban Atlas 10 m Urban Atlas 10 m

24. Two different ways for attributing indicators into urban objects • Urban objects are defined by geometric attributes (delineations) and thematic attributes • It is essential to populate urban DB with indicators (social, economic, demographic, environmental…) • Two different ways: using indicators available at LAU2 level OR using grids • LAU2 information • A fundamental pre-requisite: creating links between urban objects and local units (dictionary) • A major issue: robustness and completeness of SIRE DB • Grids information • Easy to populate urban database by OLAP cube • But risk of statistical illusion (e.g. GDP Nuts 3 -> GRID - >LUZ)

25. Enriching urban databases (SIRE DB  UMZ) Age structure – European level

26. Enriching urban databases (SIRE DB  UMZ) Age structure – Regional level

27. Results (SIRE DB  UMZ): Age structure • An example of thematic valorisation of harmonized urban DB: a typology of age structure by city • At the European scale, three main types of regions • Ageing ones (Germany, Austria, northern Italy & Spain) • Intermediate (UK, France, Belgium, Netherlands, northern Europe) • Young ones (Central & Eastern Europe, southern Italy & Spain, Greece, Ireland) • When typologing at regional scale (central Europe), city size effects appear along side regional differenciations (West-East) • Large cities oldest • Small&medium youngest

28. 3. Case Studies 28

29. Introduction • ESPON TPGs can deliver two types of datasets: • Case Study datasets • Key indicator datasets 29

30. Introduction • ESPON TPGs can deliver two types of datasets: • Key indicator datasets • Case Study datasets • Cover the entire ESPON Space (EU28+4+CC) • Respect the ESPON metadata and data template (INSPIRE) • Rely on NUTS or Urban nomenclatures 30

31. Introduction • ESPON TPGs can deliver two types of datasets: • Case Study datasets • Key indicator datasets • Does not necessary cover the entire ESPON Space • May be data at local scale • May be data to compare different regions in the world (Barcelona vs Mexico) • Cover the entire ESPON Space (EU28+4+CC) • Respect the ESPON metadata and data template (INSPIRE) • Rely on NUTS or Urban nomenclatures 31

32. Introduction • Hence two Search user interfaces for: • Key indicator datasets 32

33. Introduction • Hence two Search user interfaces for: • Key indicator datasets • Case Study datasets 33

34. Search – Case Study Currently in test phase Soon available 34

35. Search – Case Study By default: all Case Studies 35

36. Search by project Only the Case Studies of the selected project 36

37. Click on flags Contextual information 37

38. Downloads Data file and Geometry file 38

39. Metadata page 39

40. Case Study metadata page Dataset information 40

41. Case Study metadata page Contacts 41

42. Case Study metadata page Indicators 42

43. Case Study metadata page Study Area 43

44. Case Study metadata page Sources 44

45. Metroborder depicts cross-border situations at local level (LAU2) 45

46. EuroIslands highlights specific territories (NUTS 3 islands) 46

47. KIT benchmarks with extra ESPON study areas 47

48. Overview of ESPON Case Studies up to December 2013 These maps do not necessarily reflect the real coverage of ESPON Case Studies • 10 ESPON Projects • 11 Case Studies • 67 Points in the ESPON Area • 18 points out of the ESPON Area KEY FIGURES 48

49. Support to Case Studies edition - TIGRIS 49

50. Future work • Continuous integration of Case Studies • FUAs, European neighbourhood… • Improvements regarding the user-friendliness of the Case Study search page 50