1 NSIs and geostatistics Introduction to geo-statistical units ESTP course on Geographic Information Systems (GIS): Use of GIS for making statistics in

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NSIs and geostatistics

Introduction to geo-statistical units

ESTP course on Geographic Information Systems (GIS): Use of GIS for making statistics in a production environmentStatistics Norway, Oslo, 26th to 30th of March 2012

Mr Oliver HeidenMasterEurostat, European CommissionUnit E4, Regional Statistics and GI, Section of Regional and urban statisticsBECH A3/049, L-2920 [email protected]: +352 4301 31961Mob. +352 621 594545

Attribution (by) Licensees may copy, distribute, display and perform the work and make derivative works based on it only if they give the author or licensor the credits in the manner specified by these. http://creativecommons.org/licenses/by/3.0/

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Sense of geo-statistical units

geo statistical

unit

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• geo: Geospatial/Geography– Human geography (patterns, processes in human society)

Culture, e.g. Linguistic geography Development, e.g. Standard of living, Quality of life measurement Economics, e.g. Marketing geography and Transportation geography Health History, e.g. changes of places or regions changes through time Politics, e.g. interdependency: space ↔ politics, e.g. Electoral

geography, Geopolitics Population Settlement

– Physical geography (study of the solid earth, the atmosphere, hydrosphere, oceans and biosphere)

Geomorphology, Hydrology, Glaciology, Biogeography, Climatology, Meteorology, Coastal geography, Oceanography, Geomatics, Environmental geography

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• statistical: Statistics (science of data)– Data:

Quantitative or Qualitative Exhaustive or Sample Synchronic or Diachronic

– Methods: Observational or Experimental Descriptive or Inferential

– Numerous fields of application Actuarial science, Biostatistics, Business Analytics, Chemometrics,

Demography, Econometrics, Environmental statistics, Epidemiology, Geostatistics, Operations research, Population ecology, Quantitative psychology, Psychometrics, Quality control, Social statistics, Statistical finance, Statistical mechanics, Statistical physics, Statistical thermodynamics, etc.

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• unit:– Hierarchical component:

Entity regarded as an elementary structural or functional constituent of a whole.

– Comparability component: Determinate amount or quantity (as of length, time, heat, value) adopted

as a standard of measurement for other amounts or quantities of the same kind.

– In geo-statistical concepts refers mainly to area (2D) or points (0D, coordinates, addresses)

– Other units: persons

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Typology of geo-statistical units

• geo-statistical units:– Shape:

Random Standardised

– Origin: Natural Historical Administrative Analytical, functional

– Comparability Homogeneous Heterogeneous

– Coverage Comprehensive Selective

– Contiguity Single territory Divided territory

– Partition Exclusive Overlapping

– Stability Dynamic Semi-dynamic Stable

– Size: Physiographic regions,

Continents Supranational entities Countries Regions Municipalities, Communes Districts, Neighbourhoods

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Typology of geo-statistical unitsShape Origin Comparability Coverage Contiguit

yPartition Stability Size

Kommuner (NO)

Larger Urban Zones (Urban Audit, EU)

NUTS and Statistical Regions

Population Grid (1 km2)

Wine growing regions

COROP (NL)

TTWA (UK)

Postcode districts (EU)

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Typology of geo-statistical unitsShape Origin Comparability Coverage Contiguit

yPartition Stability Size

Kommuner (NO) Random

Historical Administrative

Heterogeneous Comprehen-sive

Single territory

Exclusive Dynamic 3

Larger Urban Zones (Urban Audit, EU)

Random

Administrative Analytical

Heterogeneous Selective Single territory

Exclusive Semi-dynamic

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NUTS and Statistical Regions

Random

Historical Administrative Analytical


Divided territory


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Population Grid (1 km2)

Standar-dised

Analytical Homogeneous Comprehen-sive

Single territory

Exclusive Stable 1

Wine growing regions

Random

Natural Historical

Heterogeneous Selective Divided territory


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COROP (NL) Random

Administrative Analytical


Single territory


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TTWA (UK) Random

Analytical Heterogeneous Comprehen-sive

Single territory


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Postcode districts (EU)

Random

Functional Heterogeneous Comprehen-sive

Single territory

Over-lapping

Dynamic 2

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Ideal of geo-statistical units

Shape Origin Comparability Coverage Contiguity

Partition Stability Size

Standar-dised

Administrative Homogeneous Comprehen-sive

Single territory

Exclusive Stable Small

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Main concepts of geo-statistical units

• Administrative units:– Based on real-world phenomena– Commonly accepted, variety of implementation– Analysed area = policy target– Hierarchical structure– Subject to changes (external stimulus)

• Functional units:– Defined according to analytical requirements using

geographical criteria (e.g. altitude, type of soil etc.) socio-economic criteria (e.g. homogeneity, complementarity of regions)

– Not widespread, variety of methodologies – Analysed area ≠ policy target– Mainly one-level– Subject to changes (internal stimulus)

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• Grids:– Standardised breakdown (100m2, 1km2, 4km2)– Intuitive, standard entity– Analysed area ≠ policy target– Ad-hoc hierarchical structure– Stable

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Classifications

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Policy making

Statistics↔

ResearchBusiness

↔

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• Examples → Administrative units:– Municipalities

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• Examples → Administrative units:

– Municipalities Degree of

Urbanisation (DEGURBA)

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• Examples → Administrative units:– NUTS

3-level, full coverage, mixed: administrative/non-administrative Based on administrative breakdown in Member States, EFTA and

Candidate countries Managed by Eurostat Size (population) comparable across countries Stable for at least three years: NUTS2003, NUTS2006, NUTS2010

http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction

NUTS-1 NUTS-2 NUTS-3

MAX Population DEA 18 Mill. FR10 11 Mill.

ES300 6 Mill.

MIN Population FI2 27 000 FI20 27 000 ES703 10 000

MAX Area (km2) FI1 337 000 FI1D 227 000 SE332 106 000

MIN Area (km2) BE100 161 ES640 13 ES640 13

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• Examples → Administrative units:

– NUTS

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• Examples → Administrative units:– Urban-Rural Typology

Revision of a distorted method– Different size of LAU2s– Different size of NUTS3s

Population grid (1 km²): registered population when available, otherwise disaggregation grid (JRC)

Identify population living in urban areas:– Selection of grid cells with density > 300 inh./km²– Only groups of grid cells, representing a total population of > 5000

inhabitants– Contiguity is evaluated including diagonals

Principle: three categories of regions, based on the population share in rural raster cells

– Predominantly urban: population share of rural raster cells < 20%– Intermediate: population share of rural raster cells between 20% and 50%– Predominantly rural: population share of rural raster cells > 50%

Combining small NUTS3 regions

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• Examples → Administrative units:– Metropolitan Regions

Groupings of NUTS-3 Based on LUZ

http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/metropolitan_regions

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• Examples → Administrative units:– Urban Audit

Major revision– OLD:– 300 variables every 3 years, 38 variables annually, 369 core cities– Spatial definition without standards

– NEW:– 330 variables every 5 years, 40 variables annually– Common concept for definition of units– (Revision of LUZ based on commuting flows)

http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/city_urban

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• Examples → Administrative units:– Urban Audit

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• Examples → Functional units:– Labour Market Areas

one-level Defined by NSIs or Departments of Economy, Universities etc. Comparable within countries, dissimilar across countries Stable for at least three years: NUTS2003, NUTS2006, NUTS2010 EU classification: LAU (Local Administrative Units)

Eurostat project 2012:– Assessment of feasibility of EU-wide LMAs– Assessment of (potential) value-added of EU-wide LMAs– Proposal for best practices

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• Examples → Functional units:– Labour Market Areas

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• Examples → Functional units:

– Labour Market Areas

Key for color classification

A A & B overlap

B A & C overlap

C B & C overlap

D C & D overlap Other

Vacant areas.

Very few residents, not engaging in paid work. No commuters.

Areas under Turkish occupation.

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Policy making

Statistics↔

ResearchBusiness

↔

• Examples → Grids

Confidentiality

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Thanks!

Documents

1 NSIs and geostatistics Introduction to geo-statistical units ESTP course on Geographic Information Systems (GIS): Use of GIS for making statistics in