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Estimating Hampshire's Population at Output Area Level

This research project aims to estimate the private residential household population of Hampshire by age, gender, and census output area, as well as the number of students and armed forces personnel in private households. The data is sourced from the 2001 census and will be used to forecast population changes and provide population estimates by year of age, gender, and output area.

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Estimating Hampshire's Population at Output Area Level

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  1. Estimating Hampshire’s Population at Output Area level Simon BrownSenior Research OfficerResearch and Communications

  2. Objectives • Estimate the private residential household population of Hampshire by single year of age, gender and census output area (OA) • Estimate the number of students and armed forces (and their dependants) in private households • Residents in communal establishments to be handled separately

  3. Source data • All data sourced from the 2001 census • Data available at OA level by gender and year of age up to 24, then by 5 year age groups up to age 90 • Disclosure control means values under 4 have been replaced with a 0 or 3

  4. The 0-24 year old population • Desirable to re-introduce values of 1 and 2 to obtain a more realistic population distribution • Half of the 3s were replaced with 2s, then a proportion of 0s were changed to 1s so that the sum of the OAs matched the ward totals • The specific 0s and 3s to be changed were randomly selected

  5. The 25-74 year old population • Only 5 year age-bands available for OAs, but individual year of age available for lower super output areas (LSOAs) • LSOAs typically contain between 4 and 6 OAs, and have a population of around 1,500 • OA age-band totals split out into individual years of age using the age structure from the relevant LSOA • Estimates then scaled to ward totals by year of age and gender

  6. The 75 and over population • Only ward level data available for single year of age and gender • OA level data is for 5 year age-groups from 75-89, then for 90 and over (by gender) • Age-group totals adjusted to introduce values of ‘1’ and ‘2’ • Split into individual year of age using ward age structure

  7. Rounding estimates to whole numbers • More intuitive to have a base-population made up of whole numbers • Estimates for 25-99 year old population generally not whole numbers due to scaling • Decimals rounded up or down by comparison with a rounded number so that low decimals, such as ‘0.1’, would occasionally be rounded up

  8. Students & Military • Need to separately identify these groups in population forecasting model as their migration propensities are very different to other residents • Net effect is that the size and age of these populations tends to be roughly constant

  9. Students • Ward level data available on students living in private households and not with their parents (Theme Table 2) • Students assumed to be aged between 18-24 • Commissioned a table showing OA totals for students in households and not with parents • Ward population distributed to OAs • Estimates rounded

  10. Members of the Armed Forces (AF) and dependants • Census data only available at district level due to disclosure control • Table AF1 contains number of AF members in private households • Table AF2 contains number of persons in households with an AF representative • Both tables used to estimate total AF members and dependants by age and gender

  11. Members of the Armed Forces (AF) and dependants 2 • Commissioned a table based on UV81 showing OA totals for AF members in households • OA totals used to distribute district level estimates for AF members and their dependants

  12. Running the population forecasting model • Produces population estimates by year of age, gender and output area • Starts from 2001 base population and currently runs up to 2012 • Covers population change resulting from: • Dwellings gains and losses • Natural change of population • Other in and out migration

  13. Modelling in Excel • Model was initially built in Excel with the aim of transparency • 4 large files for each district per year • Total size of model around 12GB (3DVDs) • Slow to run, even with a macro • Easy for mistakes to be made in formulae • Any changes to model would be cumbersome

  14. Moving to model into Visual Basic • Visual Basic (VB) comes with Excel and is used to write or record macros • Initially we used VB to open and close the Excel files in order and insert correction factors • Realised that quite simple code, handling arrays of data, could be used to run the whole model

  15. Improvements with VB • One piece of code used to produce forecasts for all districts for all years • Less chance of manual error and much quicker to make changes • Model reduced to less than 1mb in size (about 0.1% of Excel model size) • Produces population forecasts for a district by OA, age and gender for 12 years in around 30 seconds (approx. 1 million values)

  16. Current status of model • Base population and necessary factors stored in Excel files • VB code picks up this data, performs the calculations and outputs back into Excel • Model produces a summary showing annual births, deaths, migration etc. by ward • Results for wards, parishes and urban areas are up on our website

  17. Viewing our forecasts • Our website address: http://www3.hants.gov.uk/environment-statistics/population.htm • Thanks for listening. Any questions?

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