## [1] "Updated on 2020-08-07 08:17:00"
Produced by WorldPop (www.worldpop.org) at the University of Southampton, UK
Reading Guide
Data
This report is based on mobility data produced for the Disease Prevention Maps tools by the Facebook Data for Good Program (https://dataforgood.fb.com/tools/disease-prevention-maps/), with access facilitated by the COVID-19 Mobility Data Network (https://www.covid19mobility.org/).
These data represent people who use Facebook in the UK and have location services enabled. Data are aggregated at a 600m x 600m sized tiles and vectors (lines) are drawn connecting all areas to each other. These lines provide data in both directions (going from area A to B and from area B to A) We are provided:
- The starting point of each line
- The ending point of each line
- The number of people who traveled along this line in both directions for the 45 days preceding the collection of the data (noted on the bottom of every set of figures)
- The number of people who traveled along this line in both directions for a given time period.
- The length of the line in Euclidean distance (as the crow flies, not through the existing travel network).
- Data are aggregated in 8 hour blocks, one of these blocks for the UK is from 2100 to 0500. We treat this as belonging to the date that 2100 is in.
Pointers on evaluating the data
- It’s best to look at percent change in trips and total distance traveled as two views of a “mobility†metric. For example, if the number of trips goes up but the total distance traveled goes down, it likely means that people are moving a bit more but mainly going shorter distances, perhaps even just exercising or walking around the neighborhood.
- When looking at the travel network remember that people will live at the boundaries of the area of interest, therefore, it may just be short distance movements that are resulting in people traveling from one location to another. Long distance travel connections are more difficult to rationalize and warrant further investigation.
- You’ll often see an uptick in movement and total distance travelled on the weekends. This is generally normal behavior, though deviation from this during lock down measures should be evaluated.
- Keep an eye on the Y axis, it may be log scaled to better show the data. The labels are correct but rates of change are more extreme than they appear.
Key Takeaways
- A general upward trend in movement is evident across many parts of the UK, but not all. There are some new travel patterns for the regions with most travel that should be evaluated to ensure that the networks make sense and are expected.
UK Summary
City Specific Analysis
Most Travel
Manchester
Glasgow City
Birmingham
Camden and City of London
Wandsworth
Tower Hamlets
Ealing
Lewisham and Southwark
Merton, Kingston upon Thames and Sutton
Harrow and Hillingdon
Hackney and Newham
Hounslow and Richmond upon Thames
Redbridge and Waltham Forest
Staffordshire CC
Greater Manchester North East
Greater Manchester South West
Leeds
Greater Manchester South East
Brent
Barking & Dagenham and Havering
Tyneside
Hertfordshire
Warwickshire
South Nottinghamshire
Liverpool
Lambeth
Nottingham
Kensington & Chelsea and Hammersmith & Fulham
Leicestershire CC and Rutland
South and West Derbyshire
Bath and North East Somerset, North Somerset and South Gloucestershire
Wolverhampton
East Merseyside
South Hampshire
Dudley
Greater Manchester North West
Bexley and Greenwich
Berkshire
Mid Lancashire
Bristol, City of
West Surrey
Calderdale and Kirklees
Bradford
Haringey and Islington
Sheffield
North Lanarkshire
Barnet
Walsall
Barnsley, Doncaster and Rotherham
Solihull
Enfield
Sunderland
Wakefield
Sandwell
Central Hampshire
Devon CC
Durham CC
East Riding of Yorkshire
Leicester
Medium Travel
Stoke-on-Trent
East Surrey
Coventry
South Lanarkshire
South Teesside
Cheshire East
Hartlepool and Stockton-on-Tees
Edinburgh, City of
Kingston upon Hull, City of
Kent Thames Gateway
Buckinghamshire CC
Bromley
Chorley and West Lancashire
Inverclyde, East Renfrewshire and Renfrewshire
Essex Thames Gateway
Derby
Plymouth
Medway
North Nottinghamshire
Southampton
Worcestershire
Belfast
East Derbyshire
Cardiff and Vale of Glamorgan
Portsmouth
Cheshire West and Chester
Central Bedfordshire
Croydon
Gwent Valleys
North Hampshire
Wirral
West Essex
Oxfordshire
Blackpool
Norwich and East Norfolk
East Lancashire
Cambridgeshire CC
Sefton
Mid Kent
West Northamptonshire
Warrington
Thurrock
Northumberland
Breckland and South Norfolk
West Kent
Heart of Essex
North Yorkshire CC
Central Valleys
Monmouthshire and Newport
Dorset CC
West Sussex (North East)
East Lothian and Midlothian
Milton Keynes
Bridgend and Neath Port Talbot
Flintshire and Wrexham
Suffolk
Blackburn with Darwen
Peterborough
Lincolnshire
Least Travel
East Dunbartonshire, West Dunbartonshire and Helensburgh & Lomond
Swansea
Somerset
Shropshire CC
Telford and Wrekin
Antrim and Newtownabbey
Bedford
Darlington
Bournemouth and Poole
Lancaster and Wyre
Essex Haven Gateway
North Northamptonshire
Perth & Kinross and Stirling
Brighton and Hove
Ards and North Down
Torbay
Clackmannanshire and Fife
West Sussex (South West)
West Lothian
North and North East Lincolnshire
Southend-on-Sea
East Ayrshire and North Ayrshire mainland
Falkirk
East Sussex CC
Wiltshire
York
North and West Norfolk
Mid and East Antrim
South West Wales
South Ayrshire
Lisburn and Castlereagh
East Kent
Cornwall and Isles of Scilly
Armagh City, Banbridge and Craigavon
East Cumbria
Gloucestershire
West Cumbria
Newry, Mourne and Down
Swindon
Conwy and Denbighshire
Gwynedd
Isle of Anglesey
Angus and Dundee City
Mid Ulster
Causeway Coast and Glens
Derry City and Strabane
Powys
Herefordshire, County of
Dumfries & Galloway
Inverness & Nairn and Moray, Badenoch & Strathspey
Caithness & Sutherland and Ross & Cromarty
Fermanagh and Omagh
Aberdeen City and Aberdeenshire
Isle of Wight
Scottish Borders
Lochaber, Skye & Lochalsh, Arran & Cumbrae and Argyll & Bute
Na h-Eileanan Siar (Western Isles)
Orkney Islands
Shetland Islands
Regional Specific Analysis
Most Travel
London
South East