Upgrade to Pro — share decks privately, control downloads, hide ads and more …

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

nickbearman
November 08, 2019
64

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

nickbearman

November 08, 2019
Tweet

More Decks by nickbearman

Transcript

  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: [email protected] ¦ Twitter: @nickbearmanuk

    View full-size slide

  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

    View full-size slide

  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

    View full-size slide

  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

    View full-size slide

  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
    http://www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/census/output-area--oas-/index.html

    View full-size slide

  6. 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).

    View full-size slide

  7. 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

    View full-size slide

  8. Overlay OAs with Landuse
    • Landuse, allocate population more accurately
    • Urban 90%, Water 0%, Woodland 0%, Rural 10%
    Walford and Haynes (2012)

    View full-size slide

  9. Areal Weighting
    • OA Pop = 100
    100

    View full-size slide

  10. Areal Weighting
    • OA pop = 100
    • Lake = 0%
    • Urban = 90%
    • Woodland = 0%
    • “Rural” = 10% 10
    0
    90

    View full-size slide

  11. Overlay with 1km grid, areal weighting
    • Areal overlay – 1km grid and 100m grid
    • Total population for each cell

    View full-size slide

  12. 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

    View full-size slide

  13. 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

    View full-size slide

  14. 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)

    View full-size slide

  15. 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

    View full-size slide

  16. 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)

    View full-size slide

  17. 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

    View full-size slide

  18. 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.

    View full-size slide

  19. GitHub
    • The code for this process is in R (mainly)
    • Available on GitHub:
    – https://github.com/nickbearman/popchange

    View full-size slide

  20. Total persons in 2011 Total persons 2011-1971

    View full-size slide

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

    View full-size slide

  22. Townsend score in 2011 Townsend score 2011-1971

    View full-size slide

  23. 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:

    View full-size slide

  24. https://popchange.liverpool.ac.uk

    View full-size slide

  25. How did we create the online
    interface?
    https://github.com/ClearMappingCo/popchange-web

    View full-size slide

  26. 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

    View full-size slide

  27. 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

    View full-size slide

  28. 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.

    View full-size slide

  29. Questions
    PopChange: Creating Gridded
    Population Surfaces for Great
    Britain to enable the Analysis
    of Small Area Change
    Nick Bearman
    Email:
    [email protected]
    Twitter: @nickbearmanuk

    View full-size slide