Time-specific population grids using new forms of data – Prof. David Martin

Professor David Martin
(University of Southampton, Geography and Environmental Science)

Time-specific population grids using new forms of data

(Vortrag Englisch, Diskussion D/E) beim AGEO-Forum 2019 – Anmeldung

Abstract: Many population mapping applications are currently hampered by the very specific temporal relevance of available data, which is rarely acknowledged by users.  Conventional census and population register datasets typically represent the ‘night-time’ population distribution, with employment statistics and other indicators sometimes being used to estimate ‘day-time’ or ‘ambient’ population distributions.  In reality, applications such as emergency planning and risk assessment require a far broader range of temporal scenarios, under which specific population distributions can be estimated for different times of day, days of week and weeks of the year to reflect major population redistribution cycles driven by factors such as work, education and leisure.  This presentation describes the “Population 24/7” research agenda, in which a variety of administrative and sensed data sources including diary data, traffic flow data and pedestrian footfall counts are being integrated with more conventional sources within a coherent framework to develop time-specific high small area population estimates.

Short biography: David Martin is Professor of Geography at the University of Southampton, UK.  He is a Director of the UK Data Service and National Centre for Research Methods and from 2010-16 served as a member of the Economic and Social Research Council.  Working with the Office for National Statistics, he developed the automated zone design methods currently used to produce the geographical reference frame for census population and workplace statistics in England and Wales.  David has been active in the development of gridded population models since his 1989 PhD research and has most recently focused on harnessing new and emerging forms of data to estimate time-specific population grids for a range of organizations concerned with public health and emergency planning.

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