Our work has produced a dashboard that identifies geographical areas which might experience increased isolation and exclusion as we leave the Covid-19 pandemic and lockdowns.
Although much work has already been completed which identifies individuals most at risk from health impacts of the Covid-19 pandemic, there is considerable uncertainty regarding which societal impacts will persist as the UK leaves Covid-19 lockdowns. This project was undertaken with the aim of advancing the understanding of the social and spatial impacts of emergence from lockdown, particularly understanding how previously implemented restrictions will have impacted individuals and households. Using SPENSER, a synthetic population, we have identified individuals and households at risk from five Covid-19 restrictions: shielding, school closures, limited household interaction, furlough and limited to local area, along with households at risk from unique combinations of these five scenarios. This has been translated onto a dashboard which displays additive counts of household level impacts at the Middle Layer Super Output Area (MSOA) level.
Data and methods
We applied five Covid-19 restrictions (that cover a breadth of socio-economic impacts) to individuals and households across Yorkshire and the Humber. Our population came from SPENSER, a synthetic micro-population, along with additional characteristics obtained from supplementary datasets. The criteria for an individual or household to be impacted by each restriction were influenced by external statistics and are as follows:
Shielding: a randomly extracted 4.83% of the population who had been classified as in poor health, based on answering that their day-to-day activities were limited a lot due to a long-term health problem or disability in the 2011 census. The ailing population is representative of MSOA level trends and split into four age categories (0-15, 16-49, 50-64 and 65 and over).
School closure: households with at least one child aged 13 or under. This age was chosen as it is the age cut off for forming a Covid-19 ‘childcare bubble’.
Limited household interaction: all single person households as determined by a household size of one (a pre-existing characteristic in the SPENSER data).
Furlough: the proportion of individuals working in 1. Accommodation and food service activities, 2. Arts, entertainment and recreation; and other service activities and, 3. Wholesale and retail trade; repair of motor vehicles and motorcycles industries, were identified at the MSOA level from 2011 census data and replicated proportionally in our SPENSER population. The average percentage of furloughed employees were then identified. These were 61.3%, 67% and 13.8% respectively.
Limited to local area: all households who live in an MSOA where there is no accessible green space within 1km. These data were from CDRC’s Access to Healthy Assets and Hazards dataset.
Once all the restrictions had been applied to the households, each household was assigned to a scenario which represented a unique combination of all of the five restrictions. There were 32 scenarios in total. This enabled additive counts of impacts on households to be calculated. These final outputs are displayed on the accompanying dashboard. Counts of household impacts are displayed alongside total household counts for each MSOA and Indices of Economic Insecurity, produced by Smith et al. (2020) and used with permission.
This project has resulted in the development of an interactive dashboard, showing counts of household level impacts at the MSOA level for Yorkshire and the Humber. Although patterns of household level impacts are difficult to see from these maps, this work has explored how to use proxy data in order to identify individual and household level impacts from Covid-19 restrictions, and begun to unpack the complexities of combining data at the household level. This is something that must continue going forward as academics and policy makers continue to face the challenges that accompany understanding the social and spatial impacts of the emergence of lockdown.
Through this work, it has become apparent that certain Covid-19 specific datasets do not exist yet (such as the uptake of ‘support bubbles’) so assumptions have to be made on the extent of impacts. This detail should be added in to future tools when possible. Where data do exist, they are often lacking spatial resolution and so it has to be assumed that patterns have coarse geographies. This detail should be added in to future predictions when possible. Going forward, work must utilise more specific and detailed datasets.
The use of SPENSER as a micro-population has been foundational to understanding the impact of restrictions on individuals and households. It is recommended that any work going forward on this matter also uses small area population data as without it, any patterns of social and spatial impacts of emergence from lockdown will be coarse from the start.
Value of the research
The Covid-19 pandemic, with its associated lockdowns and restrictions, has brought vast change to the routines of families across the world. This work has had a small part in deciphering what these changes could mean for those across Yorkshire and the Humber. Dashboards with mapping have shown to be an important tool for understanding how health impacts of Covid are distributed, this same logic applies to how lockdown restrictions combine spatially.
The dashboard is currently being transitioned to its permanent online home and a link to this will be posted here in due course.
- Covid-19 causes health, social and economic impacts.
- Creation of a dashboard that displays different flavours of lockdowns.
- Supports pre-existing conclusions regarding the impact of Covid-19 lockdowns.
- Interrogation of complex layers of information aids policy reform.
- Current data are insufficient to capture Covid-19 lockdown impacts.
- Urban Analytics
- Spatial Inequality
- Interactive Visualisation
Rosalind Martin, Data Scientist Intern at LIDA
Prof Rachel Franklin, Professor of Geographical Analysis at Newcastle University
Prof Susan Grant-Muller, Chair in Technologies and Informatics at the University of Leeds
Prof Alison Heppenstall, Professor in Geocomputation at the University of Leeds
Dr Vikki Houlden, Lecturer in Urban Data Science at the University of Leeds
Consumer Data Research Centre
This project was funded by the Consumer Data Research Centre (CDRC).
Funding for SPENSER is provided by The Alan Turing Institute, project reference R-LEE-004.
Smith, D., Moon, G. and Roderick, P. 2020. Indices of Economic Insecurity: Version 2, August 2020. GeoData Institute, University of Southampton. [Online]. [Accessed 18th March 2020. Available from: https://www.mylocalmap.org.uk/iaahealth/