claude grasland on behalf of m4d

Claude GRASLANDon behalf of M4D

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Monitoring and benchmarking the European territory The M4D contribution: Time-series, Urban Data and Case Studies

4-5 December 2013

Vilnius, Lithuania

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Introduction : which are these 4 countries ?

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Introduction : which are these 4 countries ?

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Introduction : which are these 4 countries ?

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1. Search Interface, core data and time-series

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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

…

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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..

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• All Eurostat values have been kept, NUTS 2006 versionPotential 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 processSeveral months of work, errors may remain, difficult to update.

A first useful attemptA non-sustainable solution

The M4D answer (2011-2013)

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Total population 1990-2011 dataset

New censuses

heterogenous methods for gathering data

Need temporal smoothing?

The M4D answer (2011-2013)

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How to get M4D time-series?

1. Open the Search Query page

2. Search by theme/policy/project/keyword

3. Open the data filter4. Click on time-series

option

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

The M4D answer in 2014

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• 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

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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 website ESPON ET 2050 ESPON DEMIFER

• Need temporal smoothed input data to propose relevant forecasts.

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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.

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2. Urban data

Several European urban databases

Already integrated in the Espon DB

Waiting for the final version

• 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

Two complementary urban databases

provisional version (Dec. 2012)

Harmonized LUZ – 695 cities UMZ – 4304 cities

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

• 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

Two different ways for attributing indicators into urban objects

LAU2 (SIRE DB)

Grid data(GeoStat 2006 /

JRC / Corin Land Cover)

UrbanDatabase

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Problem of availability of time series

Few indicators at the moment

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

ESPON URBANOLAP Cube

GEOSTAT Pop. Grid 2006

Area (1Km²)

Measures

LUZ

FUA

UMZ

MUA

Urban Atlas 10 m

NUTSLAU 2

Urban ObjectsOLAP Database100 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

• 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

Enriching urban databases (SIRE DB UMZ)

Age structure – European level

Enriching urban databases (SIRE DB UMZ)

Age structure – Regional level

• 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

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3. Case Studies

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Introduction

ESPON TPGs can deliver two types of datasets:

Key indicator datasets Case Study datasets

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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

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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

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Introduction

Hence two Search user interfaces for:

Key indicator datasets

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Hence two Search user interfaces for:

Key indicator datasets Case Study datasets

Introduction

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Search – Case Study

Currently in test phase

Soon available

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Search – Case Study

By default: all Case Studies

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Search by project

Only the Case Studies of the selected project

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Click on flags

Contextual information

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Data file and Geometry file

Downloads

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Metadata page

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Dataset information

Case Study metadata page

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Contacts

Case Study metadata page

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Indicators

Case Study metadata page

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Study Area

Case Study metadata page

44Sources

Case Study metadata page

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Metroborderdepicts cross-border situations at local level (LAU2)

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EuroIslands highlights specific territories (NUTS 3 islands)

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KIT benchmarks with extra ESPON study areas

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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

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Support to Case Studies edition - TIGRIS

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• Continuous integration of Case Studies FUAs, European neighbourhood…

• Improvements regarding the user-friendliness of the Case Study search page

Future work

Conclusion : which are these 4 countries ?

Lithuania Ukrainia Syria Russia

Long term medium term short term

Thank you for your attention!

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Annexe 1How to deliver

Case Study datasets?

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Templates and examples available on the ESPON Database Portal in 4 clicks:

Access the portal at http://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

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Upload Of Case Study: “Data” file

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Upload Of Case Study: “Geometry” file

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Upload Of Case Study: “Confirm” step

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