European Forum for Geography and Statistics
Vienna Conference
Vienna, 10 – 12 November 2015
Name (s) of author(s)
David Martin, Samantha Cockings and Alan Smith
Organization
Geography and Environment, University of Southampton, Southampton, SO17 1BJ, UK
Title of Presentation, Paper or Poster
Exploiting census workplace data to build a daytime grid map of England and Wales
Abstract
(300 – 500 words)
The 2011 census of England and Wales was enumerated on a place of residence basis but included a
question on place of work for those in employment, permitting the allocation of each employee to both
residential and workplace locations. Innovations in census outputs included creation of an entirely new,
additional small area geography, known as workplace zones (mean workplace population count 493) to
reflect the spatial distribution of employees at their places of work. This new national coverage of 53,578
small areas has been created using an automated zoning procedure and used for publication of workplacebased statistics, providing a detailed geographical subdivision of commercial and business districts which
may have small residential populations but very large daytime populations. Workplace-based population
statistics have been published for these zones in addition to conventional census data products based on
residential populations published for census output areas (181,408 areas with mean residential population
count 309).
Grid-based models using residential 2011 census data have been constructed on the Geostat grid from
2011 residential population data, but this presentation will explore the creation of experimental population
grids for both daytime and night-time, initially on an Ordnance Survey Great Britain 200m grid, using a
combination of output area and workplace zone datasets with additional non-census government data on
road traffic estimates, places of education and health care. Employment accounts for the single largest
daily spatial redistribution of population and there are many potential applications for daytime statistics.
There is already research evidence for the utility of such time-specific population models in applications
such as service delivery and emergency planning, although the challenges of time-specific model
validation and integration with new big data sources are considerable.
The Population24/7 approach used for this spatial reallocation of population is based on the allocation of
daily time profiles to different population activities, such as different sectors of employment. The entire
population is reallocated to those locations at which they are believed to be present at a specified target
time. The approach may be readily extended to incorporate additional non-census data sources or to
estimate population distributions for specific target times and dates, taking account of major temporal
cycles such as school and university term dates, seasonal change and day of week. The ability to model
these cycles opens up potential to produce data products such as an ambient population grid and to
identify those areas where the greatest temporal fluxes in population are likely to occur.