American Association of Geographers 2018

This is the presentation I gave at the AAG 2018 in New Orleans. It discusses ongoing work on high resolution demographic projections.

Transcript

1. Nik Lomax Andrew P Smith AAG New Orleans Innovations in Urban Analytics, 12 April 2018 High resolution demographic projections for infrastructure planning
2. • The UK Infrastructure Transitions Research Consortium (ITRC) is a collaboration of seven universities and over 50 partners from infrastructure policy and practice. • ITRC is helping governments, utility providers, designers, investors and insurers by developing new ways to evaluate the performance and impact of long-term plans and policy for infrastructure service provision in an uncertain future. • Supported by an EPSRC Programme Grant.
3. Demographics for demand models Demographic projections Demand modelling
4. Essential for planning the delivery of services and the allocation of resources to sub-national areas 2014 sub-national population projections by ONS projections of diversity to 2061 from NewEthpop Or the assessment of population change for measuring demographic diversity and social equality Demographic projections
5. Demographic projections Projections are needed at high resolution for the MISTRAL project Crown copyright © 2017 Ordinance Survey 2014 sub-national population projections by ONS But Spatial resolution is usually limited to larger administrative areas
6. Two projection approaches • Static sequential microsimulation constrained to official projections • Dynamic microsimulation which borrows strength from survey and census data as well as supply data from new build and housing stock information • The static model is delivering results, the dynamic model is work in progress
7. Data inputs • 2011 Census • Standard tables – near complete enumeration • Microdata – detailed attribute cross-tabulations • Survey data to add attributes/ calculate transition rates • British Household Panel Survey • Understanding Society • Ordinance Survey Mastermap & Address Point data • ONS population projection constraints • DCLG (1) household projections and (2) housing additions
8. Modelling approach
9. Population at MSOA scale Classified residential buildings Residential household size and type Demand models Model component integration Households at OA scale
10. Static sequential microsimulation Source: Lomax, N. and Smith, A P. (2017) Microsimulation for demography. Australian Population Studies, 1(1): 73-85
11. Dynamic microsimulation
12. Household Change Exeter Newcastle
13. Exeter Newcastle Population Change
14. Source: Pregnolato et al. (2018) A building stock and household composition model for the UK. GISRUK, Leicester, 17-20 April. Allocation to buildings • MasterMap and AddressPoint data are the input for a spatial allocation algorithm that classify the building stock in terms of residential type. • Spatial assignment by fitting buildings to households on the basis of the correlation of household size and number of rooms with building area footprint. • This allows all buildings footprints to be assigned a plausible set of household characteristics.
15. Input to demand models Source: Results from MISTRAL energy team, Oxford
16. Conclusion (1 of 2) • High resolution demographic projections are needed for effective modelling of demand across a range of sectors • Most projection models operate at aggregate scales • Microsimulation offers a useful tool to undertake modelling at fine spatial scale • And the potential to incorporate a range of variables
17. Conclusion (2 of 2) • Of the two microsimulation approaches: • Static sequential is easier to implement but misses much of the dynamic processes which go on as population changes • The fully dynamic model is potentially the best performing but very hard to implement. This is currently work in progress.
18. Nik Lomax Andrew P Smith AAG New Orleans Innovations in Urban Analytics, 12 April 2018 High resolution demographic projections for infrastructure planning