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PopChange: Creating Gridded Population Surfaces for Great Britain to enable the Analysis of Small Area Change

Ac36cbdeb128eb88c6bce0ddff38a030?s=47 nickbearman
November 08, 2019

PopChange: Creating Gridded Population Surfaces for Great Britain to enable the Analysis of Small Area Change

An overview of the PopChange dataset and tool for WorldPop / Flowminder staff at their offices in Southamtpon



November 08, 2019


  1. PopChange: Creating Gridded Population Surfaces for Great Britain to enable

    the Analysis of Small Area Change Nick Bearman Project team: Chris Lloyd*, Gemma Catney* and Paul Williamson, University of Liverpool, UK *Now Queens University, Belfast Email: ¦ Twitter: @nickbearmanuk
  2. PopChange: An old project, but interesting methods • Chris Lloyd

    started 2014-2015 • I worked on this 2015-2016 • Lots of potential to be developed • But…. I moved / Chris Lloyd moved….. • Now working: • 2d/wk at CDRC, UCL Geography • 3d/wk Geospatial Training Solutions, Training & Consultancy
  3. PopChange project outline • Identification of comparable variables from the

    UK Censuses of 1971, 1981, 1991, 2001 and 2011 • Creation of population surfaces for Britain for all comparable variables (1km cells nationally; in Northern Ireland grid square counts for 1971-2011 are already available) • Provision of population surfaces, code in R programming language to manipulate data and an online atlas of population change
  4. Overview UK Census Data, comparing small area changes over time

    Three main problems: 1. Size and shape of the zones used 2. Questions asked 3. Output variables
  5. Exploring Change over Small Areas • Output Areas change over

    time • Splits • Merges • E.g. 2001 vs 2011 • 2.6% change* (4,561 OAs) • Black: OA 2001 • Red: OA 2011
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  8. Exploring Change over Small Areas Solutions to moving between different

    zone sets: (i) converting counts from irregular zones to a surface (ii) transferring counts from one set of zones to another using areal interpolation (iii) transferring counts from one set of zones to another on a best-fit basis (Martin et al., 2002). This research focuses on a combination of (i) and (ii).
  9. The Process • Identify comparable variables • Overlay OAs (EDs)

    with landuse data • Use areal overlay & weighting to estimate population • Overlay 1km grid (100m urban) • Use areal overlay & weighting with grid & OA • Smooth grid cells
  10. Overlay OAs with Landuse • Landuse, allocate population more accurately

    • Urban 90%, Water 0%, Woodland 0%, Rural 10% Walford and Haynes (2012)
  11. Areal Weighting • OA Pop = 100 100

  12. Areal Weighting • OA pop = 100 • Lake =

    0% • Urban = 90% • Woodland = 0% • “Rural” = 10% 10 0 90
  13. Overlay with 1km grid, areal weighting • Areal overlay –

    1km grid and 100m grid • Total population for each cell
  14. Why Gridded data? • Benefits are that all units are

    of the same size and shape and this makes it easier to assess scale effects without the need to account for zones whose size and shape differs • With grid cells, there are holes where there are no people; this is conceptually more sensible than zones (e.g., output areas or wards) which cover the land area completely
  15. Postcode Units • Limited archival data for Land Cover •

    Postcodes went back to ~1980 • Use Postcode Unit (points) to create a surface • Allocate population based on this surface
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  17. Using Postcode Density 1. Generate a postcode intensity grid using

    kernel estimation – allocate to 1km cells 2. Overlay 1 with source zones (e.g., Output Areas (OAs) giving OAG) 3. Compute populations (OAG_Estimate) for each OAG zone with: – WtArea = Wt x OAG_Area; (Wt is from Postcodes) – WtAreaSum = WtArea summed by OA; – OAG_Estimate = WtArea / WtAreaSum x OAPop 4. Aggregate OAG_Estimate values by grid cell Population is then allocated to 1km grids, based on postcode densities (i.e. more postcodes -> more people)
  18. Cell Smoothing • Identify cells that are not split by

    OA boundaries • By definition, they will be equal • But this creates step changes in population which we know do not exist
  19. Cell Smoothing • Therefore we smooth the values using a

    3x3 win • But - this will change the total population value • So we then rescale the values to match* * pycnophylactic (mass preserving)
  20. Cell Smoothing • Combine the rescaled and original values •

    And then smooth the whole grid iteratively until RMS error < 0.001 between smoothed grid and previously smoothed grid • Data will be smoothed different number of times
  21. Cell Smoothing The number (n) of smoothing iterations for each

    count until the RMS difference decreased to less than 0.001. 2001 2011 Counts n iterations n iterations White 16 11 Non-White 3 3 LLTI 10 7 No LLTI 15 10 The figures accord with expectation in that a larger number of iterations is required to reach convergence for ‘smoother’ counts than is the case for less-smooth counts. The categories White and No LLTI each include the large majority of people and are relatively spatial homogenous compared the categories Non-White and LLTI. Therefore, more smoothing is likely to be optimal in the former cases than in the latter. Also, the results suggest that ‘White’ and ‘No LLTI’ were, on average, smoother in 2001 than they were in 2011.
  22. GitHub • The code for this process is in R

    (mainly) • Available on GitHub: –
  23. Total persons in 2011 Total persons 2011-1971

  24. Unemployed persons (%) in 2011 Unemployed persons 2011-1971

  25. Townsend score in 2011 Townsend score 2011-1971

  26. Did it work? • Generate 1km grids from Small Area

    (SA) data using postcode centroids to determine variations in population density within SAs. • Use NI Census Grid Square resource (available since 1971) to assess accuracy of estimates for grid cells. • NI total population: 1,810,863 • Small Areas:
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  36. How did we create the online interface?

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  38. Updates • Website moving from Liverpool to QUB • Including

    more non-Census (e.g. admin data) • Papers being finalised on changes in: – (1) deprivation, (2) country of birth and ethnicity, and (3) self-reported health (Emily Dearden's PhD) • Briefing on housing spaces (overcrowding) • Outputs in LSOA/DZ, more relevant to policy. • Onoing work in SA, spatial inequalities, grids
  39. Summary • Use of gridded outputs to overcome issues of

    changing boundaries for small areas over time • New method of population allocation using Postcode Unit density • Web based interface for wider public participation • Code for both on GitHub
  40. Acknowledgements Support from the ESRC is acknowledged gratefully (Grant Ref

    No ES/L014769/1). Team members also include Gemma Catney, Alex Singleton and Paul Williamson. The Office for National Statistics are thanked for provision of the data. Office for National Statistics, 2011 Census: Digitised Boundary Data (England and Wales) [computer file]. ESRC/JISC Census Programme, Census Geography Data Unit (UKBORDERS), EDINA (University of Edinburgh)/Census Dissemination Unit. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland.
  41. Questions PopChange: Creating Gridded Population Surfaces for Great Britain to

    enable the Analysis of Small Area Change Nick Bearman Email: Twitter: @nickbearmanuk