1. 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

2. 2 Introduction : which are these 4 countries ?

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

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

7. 7 The M4D answer (2011-2013) 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 M4D Core Indicators ESPON Area + Candidate Countries No missing values But..

8. 8 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 The M4D answer (2011-2013)

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

10. 10 How to get M4D time-series? 1.Open the Search Query page 2.Search by theme/policy/project/ keyword 3.Open the data filter 4.Click on time-series option

11. 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 estimationAfter estimation The M4D answer in 2014 11

12. 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. Next steps for the M4D time-series… smoothing discontinuities 12

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

14. 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.

15. 15 2. Urban data

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

17. 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 Several European urban databases

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

19. The small & medium sized cities: another major issue for European planning and urban policies Advantage of UMZ DB: small & medium cities EXIST 55 UMZ12 LUZ

20. 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 Two complementary urban databases

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 UMZ – LAU2 dictionary Elaborating the UMZ-LAU2 dictionary: a very complex task Available in the ESPON DB portal

23. ESPON URBAN OLAP Cube GEOSTAT Pop. Grid 2006 Area (1Km²) Measures LUZ FUA UMZ MUA Urban Atlas 10 m NUTSLAU 2 Urban Objects OLAP Database 100 x 100 m Grid End Users Urban OLAP Cube: a method to create grid indicator from administrative levels (NUTS2/3) 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 Data Source LAU 2 Urban Atlas 10 m

24. 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) Two different ways for attributing indicators into urban objects

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

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

27. 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 Results (SIRE DB  UMZ): Age structure

28. 28 3. Case Studies

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

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

31. 31 ESPON TPGs can deliver two types of datasets: Key indicator datasets Case Study 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 Introduction

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

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

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

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

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

37. 37 Click on flags Contextual information

38. 38 Data file and Geometry file Downloads

39. 39 Metadata page

40. 40 Dataset information Case Study metadata page

41. 41 Contacts Case Study metadata page

42. 42 Indicators Case Study metadata page

43. 43 Study Area Case Study metadata page

44. 44 Sources Case Study metadata page

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

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

47. 47 KIT benchmarks with extra ESPON study areas

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

49. 49 Support to Case Studies edition - TIGRIS

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

51. Conclusion : which are these 4 countries ? Lithuania Ukrainia Syria Russia Long term medium term short term

52. Thank you for your attention! 52

53. Annexe 1 How to deliver Case Study datasets? 53

54. Templates and examples available on the ESPON Database Portal in 4 clicks: Access the portal at http://db2.espon.euhttp://db2.espon.eu Click the Login Menu item and login Click the Upload Menu item Download templates and examples for Key Indicator Case Study Access to useful resource 54

55. Upload Of Case Study: “Data” file 55

56. Upload Of Case Study: “Geometry” file 56

57. Upload Of Case Study: “Confirm” step 57