1. A new ESPON 2020 Programme - Filling in data gaps
Marjan van Herwijnen and Martin Gauk
ESPON EGTC
Working Group on Regional, Urban and Rural Development Statistics
Luxembourg, 18 October 2016

2. 1 2 Outline Data situation & estimating missing values
ESPON & evidence made policy-making
Applied research activities
Targeted analysis
Data situation & estimating missing values
Time series
Urban data 2

3. 1
ESPON & evidence based policy making

4. Mission
“ESPON 2020 shall continue the consolidation of a European Territorial Observatory Network and grow the provision and policy use of pan- European, comparable, systematic and reliable territorial evidence”

5. Aim: Specific Objectives1
Continued production of territorial evidence
Upgraded knowledge transfer and analytical user support
Improved territorial observation and tools for territorial analyses
Wider outreach and uptake of territorial evidence
Leaner, effective and efficient implementation provisions and proficient programme assistance 2 3 4 5

6. Target groups European policymakers (EP, EESC, EC, CoR)
National policymakers and practitioners
Authorities implementing EU funded programmes
Regional and local policymakers and practitioners involved in territorial development and cooperation in larger territories
Organisations at EU level with regional/urban interests
University academics (researchers and students)
The private sector and European citizens

7. Applied research

8. Applied research activities1
Geography of new employment dynamics in Europe
The World in Europe, global FDI flows towards Europe
Small and Medium-sized Enterprises in European Regions and Cities
Territories and low-carbon economy
Inner Peripheries: national territories facing challenges of access to basic services of general interest
Possible European Territorial Futures
Comparative Analysis of Territorial Governance and Spatial Planning Systems in Europe 2 3 4 5
Applied Research Projects
Under the Specific Objective 1 of the ESPON 2020 Cooperation Programme, “Enhanced European territorial evidence production through applied research and analyses”, Applied Research projects (AR) contribute to the European territorial and analytical evidence base.
ESPON AR respond to policy needs and focus on key priorities for territorial development, such as priority themes of the Europe 2020 Strategy, the Investment Plan for Europe, the Cohesion Policy and the Territorial Agenda 2020. 6 7
Final reports: December 2017

9. Targeted analysis
An open Invitation to Submit Your Proposals for Targeted Analyses

10. ESPON Targeted Analysis
How does it work?
Stakeholders develop and submit their project proposals
ESPON Targeted Analysis      
ESPON EGTC evaluates the proposals and selects a few
Stakeholders can apply the results in their policy development processes
ESPON EGTC develops the selected proposal into ToRs together with stakeholders
Academic experts carry out
the analytical work and
stay in close connection
with the stakeholders
Stakeholders propose practical ideas for relevant analysis by:
Describing their policy need concerning their own territories;
Explaining why they need a targeted analysis with a European perspective;
Outlining results they wish to achieve;
Explaining how they intend to make use of the results in practice.
Stakeholders proposals can be submitted continuously throughout the year.
ESPON EGTC evaluates twice per year all applications received by a certain deadline.
ESPON EGTC develops selected proposals into ToRs together with stakeholders.
Open EU-wide call for tenders to find academic experts that carry out analytical work.
Open call for tenders to find academic experts
Twice a year: next cut-off date for the submission of stakeholder proposals is on 13 January 2017.

11. Who can apply?
Stakeholders and practitioners from national, regional and local administrations of EU Member States and the four ESPON Partner States.
Authorities implementing EU funded programmes, e.g. Managing Authorities and programme secretariats.
Single or groupings of national.
Stakeholders can consider involving representatives of organisations from public and/or private sector.

12. What are general requirements for stakeholder proposals?
Presence of European perspective/dimension in the targeted analysis
Degree of added value and transferability
Realism in analytical terms
Use of the analytical results in policy making
Relevance for place-based development strategies
Competent involvement of stakeholders in the analytical process and in steering of the activity
Relevance and complementarity to existing ESPON territorial evidence

13. Policy briefs

14. Policy Briefs published1
Territorial Scenarios for Europe 2050
Territorial and urban aspects of migration and refugee inflow
Territorial Implication of Better Regulation for Europe towards 2050
Urban Partnership Themes in a Wider Territorial Context
Second Tier Cities Matter
Polycentric Territorial Structures and Territorial Cooperation
Pathways to a circular economy in cities and regions 2 3 4 5 6 7

15. Planned activities and work plan
for 2017

16. Applied research activities (7 out of the following 15 themes)
Functional Urban and Metropolitan Regions
Financial Instruments and Territorial Cohesion
Green Infrastructures and ecosystem services
Blue Growth in Europe
European Territorial Reference Framework towards 2050
Transport Systems in a Polycentric Europe
Youth Unemployment: Territorial trends and regional resilience 2 3 4 5 6 7

17. Applied research activities (7 out of the following 15 themes)8
Vision building in cross-border regions
Economic impacts of natural hazards
Territorial consequences of limiting land take
Circular Economy and Territorial Consequences
Flows of Migrants and Refugees
Growth beyond GDP
Macro-regional links and interdependencies
Territories with geographical specificities 9 10 11 12 13 14 15

18. Targeted Analysis 1
Invite stakeholder proposals and launch eight targeted analysis projects Provide support to EU funded programmes Continue to produce policy briefs/working papers towards policy processes, including for key policy priorities of the upcoming EU presidencies (Malta and Estonia) 2 3

19. Wider Outreach and Uptake of Territorial Evidence1
Launch of a new revamped website Organise two seminars in cooperation with the EU Presidencies (Malta and Estonia) High-level conference on the European Territorial Review and cohesion policy 2 3
7-8 December 2016 ESPON Seminar in Bratislave
Where are European cities heading?

20. 2 Data issues ESPON database filling in data gaps & generating
fit-for-purpose
datasets
As you saw from the presentation of Marjan, ESPON engages in a variety of research activities which in turn translates into a a huge data need from global to local and beyond.

21. Data demand and availability
Need for data at the beginning of ESPON activities.
Need for the most recent data.
Need for variety of themes.
Need for good coverage (EU+EFTA & often beyond).
Need the data at smaller territorial units (NUTS3, LAU2, grid, urban).
Need for measuring dynamics (time-series, managing NUTS change).
The need is for datasets with complete spatio-temporal coverage for various territorial units (especially smaller). And this is needed to be obtained in a timely manner to be able to conclude swift policy messages.
Understandably, this is a very tall order.
Data that meets these requirements is often not available for various reasons: 1) administrative changes
2) the data is not collected at levels we always would like

22. A response from ESPON Database Portal (core database strategy)
Focus on the storage of count variables
Store formulas for indicators derived from count variables
Enlarge time series by estimation of missing values
Develop procedure of exchange of indicators between geometries of various types
Propose innovative procedures of multi-level analysis of indicators for territorial monitoring
ESPON database project proposed a variety of solutions in order to overcome the aforementioned data issues.
It was a project that went further than simply collecting data and making it available.
It also looked analytically into the many data related challenges our researchers faced and proposed and developed a number of methodological solutions and tools to overcome these challenges.
Different data estimation models were compiled and tools developed and compiled under an ESTI framework.

23. Estimating missing values: ESTI framework
(E) space dimension
(S) source dimension
Level n-1
Level n
Level n+1 S1 S2
(T) time dimension
(I) Thematic dimension
T-n
T-1 T
T+1
T+n I
I+1
The Spatial dimension (E): Estimate the missing values thanks to values known at an upper, lower or at the same hierarchical level.
The Source dimension (S): Alternative sources of information can be used when the main source does not provide the targeted information.
The Time dimension (T) uses information provided in time before or after the value to estimate.
The Thematic dimension (I): The idea is to replace the missing value by a known value coming from an alternative indicator, controlled by a correlation factor.
Lets have a closer look at few of them.

24. Example 1: time dimension
We can estimate missing values in the official series data to create the estimated time series (scripts ready in R)
Green cells have complete official data; red cells require estimation
Each series can be modelled with either a linear, quadratic or exponential trends, with an autocorrelated error term:
autoregressive model, moving average model or combination of the both or Bayesian inference (probability).
Before estimation
After estimation 24

25. R Programme for time-series estimations
Weighing by population

26. Example 2: space & source dimensions Populating urban databases
Name
Producer
Criteria
No. and min city size
Urban Morphological zones (UMZ)
EEA
Morphological (urban tissue and function)
4300
> inh
Morphological urban areas (MUA)
IGEAT
Morphological (population density)
1988
> inh
Functional urban areas (FUA)
Functional (commuters)
1530
> inh
Functional urban areas
EC-OECD
828
> inh
Metropolitan regions
EC-JRC
Functional
270
> inh
What is urban?
5 different urban DB have been expertized by ESPON database
2 morphological delineations (continuous built-up areas)
3 functional urban areas
Choosing an appropriate delineation depends on:
a) our research questions;
b) data availability.

27. Complementary urban databases
Harmonized FUAs – 828 cities
UMZ – 4304 cities
Importance of harmonized FUA
For the first time, an official harmonized DB
Integrate large perimeters that functionally depend on core cities
Related to various socio/economic/demographic indicators (Urban Audit)

28. Small & medium sized towns: another major issue for European planning and urban policies
12 FUA
55 UMZ
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
Advantage of UMZ DB: small & medium cities EXIST

29. Different ways for populating urban databases: LAU & NUTS
Grid data
LAU2, NUTS3
Problem of availability of time series
Few indicators at the moment
Urban
Databases
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/NUTS3 level OR using grids
LAU2/NUTS3 information
A fundamental pre-requisite: creating links between urban objects and local units (dictionary)
A major issue: robustness and completeness of LAU2 DB
Integrating heterogeneous data sources in a single database

30. Populating urban objects with LAU2/NUTS3 data needs a dictionary
Available for LAU2 – FUA, NUTS3 – Metropolitan regions
Elaborating the UMZ-LAU2 dictionary: a very complex task
UMZ – LAU2 dictionary
Creating an UMZ LAU2 dictionary can be a very complex task.
Available in the ESPON DB portal

31. Different ways for populating urban databases: grid
Grid data
LAU2, NUTS3
Problem of availability of time series
Few indicators at the moment
Urban
Databases
Easy to apply
Few indicators
Problems with time series
Integrating heterogeneous data sources in a single database

32. Integrating heterogeneous data sources in a single database
Different ways for populating urban databases: integrating different data sources
Grid data
LAU2, NUTS3
Problem of availability of time series
Few indicators at the moment
Urban
Databases
Easy to populate urban database by OLAP cube
Data is integrated through reference grid.
But risk of statistical illusion (e.g. GDP Nuts 3 -> GRID - >FUA)
Integrating heterogeneous data sources in a single database

33. Urban OLAP Cube: a method/tool to integrate geographic, thematic and socioeconomic data
Urban Objects
GEOSTAT Pop. Grid
Area (1Km²)
Measures
OLAP Database
1 km2 100 x 100 m grid
UMZ
LUZ
MUA
FUA
ESPON URBAN
OLAP Cube !
Data Source
Online analytical processing
LAU2/NUTS3 data is integrated using a reference grid
Weigthed by population
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
End Users
LAU 2
Urban Atlas 10 m
Urban Atlas 10 m

34. OLAP cubes
Weighing by population

35. Estimated vs official data
Estimated data
European Commission website
ESPON ET 2050
ESPON DEMIFER
Benchmark with policy objectives.
One-shot results (situation in …?)
- data availability issues
Analysing trends (same methodology, harmonised, comparable datasets
Proposing relevant forecasts.
- not so well automated
- risks for statistical illusions
- OLAP not tested for policy making purposes 35 35

36. Inspire policy making by territorial evidence
Marjan van Herwijnen
Martin Gauk
Thank you for
your attention
Inspire policy making by territorial evidence

37. Integrating
Proportional weighted calculation
Maximum area