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

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

1 ESPON & evidence based policy making

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”

Aim: Specific Objectives 1 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

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

Applied research

Applied research activities 1 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

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

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.

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.

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

Policy briefs

Policy Briefs published 1 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

Planned activities and work plan for 2017

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

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

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

Wider Outreach and Uptake of Territorial Evidence 1 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?

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.

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

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. www.database.espon.eu

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.

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

R Programme for time-series estimations www.database.espon.eu Weighing by population

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 >10 000 inh Morphological urban areas (MUA) IGEAT Morphological (population density) 1988 >20 000 inh Functional urban areas (FUA) Functional (commuters) 1530 > 20 000 inh Functional urban areas EC-OECD 828 > 50 000 inh Metropolitan regions EC-JRC Functional 270 > 250 000 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.

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)

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

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

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

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

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

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

OLAP cubes www.database.espon.eu Weighing by population

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

Inspire policy making by territorial evidence Marjan van Herwijnen marjan.vanherwijnen@espon.eu Martin Gauk martin.gauk@espon.eu www.espon.eu Thank you for your attention Inspire policy making by territorial evidence

Integrating Proportional weighted calculation Maximum area