EU age- and gender- stratified population data

From Opasnet
Revision as of 19:02, 25 September 2014 by Pauli (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search


The text on this page is taken from an equivalent page of the IEHIAS-project.


This population data set was prepared for the INTARESE and HEIMTSA projects. It includes population data on the emep 50 km x 50 km grid for the years 2000, 2010, 2020, 2030 and 2050. The data is stratified by 5 year age bands and by gender.

Data were derived by making use of different data sources: UN data, CIESIN / SEDAC Gridded World Population and national census data.

A) Census data are available on LAU level 2 for the year 2001. They are stratified by gender and age and used as basis data set for 2000/2001. They give spatial information as well as information on age groups and gender. However, they do not give information about the development in the future. Only data for 23 countries are available. BG, CY, LV, RO, CH, NO and IS are missing.

B) UN data are available by country for the years 1950 to 2050 stratified by gender and 5-year age groups. They were used for filling of information gaps on country totals and gender and age stratification for those countries for which no LAU census data was available. Furthermore, they are used for deriving growth rates of population subgroups for future years. If for some reason not gridded data are needed but country totals, UN data can be taken. They give information on a country level. No further spatial information is available.

C) GWP (Gridded World Population) data are available from CIESIN/SEDAC. They provide gridded data on several resolutions for several regions. Interesting for the INTARESE/HEITMSA study were the data for 2000 and 2010 for a resolution of ½°. GWP data are used for filling of spatial information gaps for those countries for which no LAU census data is available. They furthermore give some feeling for spatial shift of population from 2000 to 2010. No information for the years 2020, 2030 and 2050 is available. No stratification regarding gender or age groups is available.

D) EUROSTAT data and projections are available for all required years. EUROSTAT data, including projections to the future, are used as one basic assumption for the energy modelling, which in turn is an important basis for emission scenario modelling. No stratification regarding gender or age groups is available for future years. Comparisons indicate that EUROSTAT data, including projections, does not differ much from UN data, including projections. Thus, consistency is preserved.

Steps to generate the data sets

Step 1a: Processing LAU census data to fit it to the Emep grid cell

  • Filling gaps in the available data sets (e.g. for some countries for some LAU regions only the total number of persons was available, not split by age and gender)
  • Filling missing age groups (e.g. for some countries no 5-year age bands were given but e.g. 15-year bands: they were further split up using age group fractions derived from the UN data)
  • Intersection with Emep 50 km x 50 km grid
  • Summing up per grid cell, age and gender

Step 1b: Filling gaps: Filling data for those countries for which no LAU census data was available

  • Using UN data for country totals
  • Splitting into subgroups on a country level using UN data (subgroup fractions)
  • Area-weigh total population using GWP data (using percentages of grid cells compared to the total GWP population)

UN data are used for country totals as country totals for all sources are relatively small, so there is no reason against using them. Furthermore, UN data country totals and growth rates are used for projections to the future (see step 2). Thus, consistency is preserved.

Step 1c: Summing up data from both sources

  • Summing up values for each grid cell from both sources

Step 2: Projections to the future

  • Growth rates from UN data (for each subgroup separately) are taken to project the basic data set to the future.
  • Result: Data set including for each grid cell the number of persons of each subgroup in the years 2000, 2010, 2020, 2030 and 2050. For 2020, 2030 and 2050, medium, high and low estimates are available.

Growth rates from UN data are taken because i) UN data are taken whenever possible for consistency reasons, ii) UN data have several growth rates (middle, high, low) which gives some kind of uncertainty bounds, and iii) EUROSTAT growth rates fit quite well with the UN data growth rates so there is no inconsistency here.

See also

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages
Toolkit
Data

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty
Appraisal