This method provides statistics on relative deprivation in England, Wales, and Scotland, including:

• The Index of Multiple Deprivation (IMD) – a measure of overall deprivation
• Domain-specific indices (e.g., income, employment, education, health etc.) that differ by country
• Area rankings and deciles for simplified analysis

Area Types

The indices assess deprivation at a small-area level:

• England & Wales: Lower-layer Super Output Areas (LSOAs)
• Scotland: Data Zones

Ranking & Deciles

Each area is ranked from most to least deprived:

• England: 1 (most deprived) to 32,844 (least deprived)
• Wales: 1 (most deprived) to 1,909 (least deprived)
• Scotland: 1 (most deprived) to 6,976 (least deprived)

The ranks are available in the imdRank field, with domain-specific ranks in fields such as incomeRank, employmentRank, crimeRank, etc.

To simplify, areas are also categorized into deciles (1 = most deprived, 10 = least deprived), available in fields like imdDecile, incomeDecile, employmentDecile, etc.

Mapping & Consumer Platform Adjustments

We use deciles to color-code our deprivation map. However, on our consumer platform, we reversed the ratings scale to match user expectations where higher ratings are associated with higher deprivation.

For example, postcode W6 0LJ (imdDecile 2, a highly deprived area) is displayed as “_Index of Multiple Deprivation - 9/10 or high_” on the consumer platform.

Data Availability

• England & Wales: 2019
• Scotland: 2020

This app is published as Open Data, is the most recent, and replaces any previously published dataset.Scottish Index of Multiple Deprivation (2020), Small Area Population Estimates (2021), and Child Poverty (2022/23)The Scottish Index of Multiple Deprivation 2020 is the Scottish Government’s official tool for identifying those places in Scotland suffering from deprivation. It incorporates several different aspects of deprivation (employment, income, health, education, skills and training, geographic access, crime and housing), combining them into a single index.The 2020 Index provides a relative ranking for small areas in Scotland, defined by the Scottish Neighbourhood Statistics (SNS) Data Zone 2011 geography, from 1 (most deprived) to 6,976 (least deprived). By identifying small areas where there are concentrations of multiple deprivation, the SIMD can be used to target policies and resources at the places with greatest need. The SIMD also provides a rank for each data zone within each of the seven domains, and therefore it is possible to look at individual aspects of deprivation for each area, as well as the overall level of deprivation.Child Poverty by Datazone (2022/23)This app uses the following published resources:mapdataset

Data showing deprivation levels across different Birmingham Wards, using three key indices: the Index of Multiple Deprivation (IMD), the Income Deprivation Affecting Children Index (IDACI), and the Income Deprivation Affecting Older People Index (IDAOPI). These indices are integral components in understanding socio-economic conditions, enabling insights into how deprivation affects various age groups and areas.Index of Multiple Deprivation (IMD): IMD is a composite measure that ranks areas based on seven domains of deprivation: income, employment, education, health, crime, housing, and living environment. It offers a broad overview of deprivation at a small-area level. This is shown as a rank from 1-69 with 1 being the most deprived.IMD 2019 Decile: Where 1 is 10% Most Deprived Nationally. This is an indication of the wards financial resilience.Income Deprivation Affecting Children Index (IDACI): IDACI focuses specifically on income deprivation among children. It measures the proportion of children aged 0-15 living in income-deprived households, reflecting the impact of poverty on young people.Income Deprivation Affecting Older People Index (IDAOPI): IDAOPI targets income deprivation among the elderly. It calculates the proportion of people aged 60 and over living in income-deprived households, providing insight into how economic disadvantage affects older populations.Data is update irregularly with the next release scheduled for late 2025.Mapping tipsWhen using the build a map page you should use the Color by category map type when visualising the IMD score.A good resource for custom colours for each decile is ColorBrewer.

This dataset is published as Open Data, is the most recent, and replaces any previously published dataset.The Scottish Index of Multiple Deprivation 2020 is the Scottish Government’s official tool for identifying those places in Scotland suffering from deprivation. It incorporates several different aspects of deprivation (employment, income, health, education, skills and training, geographic access, crime and housing), combining them into a single index.The 2020 Index provides a relative ranking for small areas in Scotland, defined by the Scottish Neighbourhood Statistics (SNS) Data Zone 2011 geography, from 1 (most deprived) to 6,976 (least deprived). By identifying small areas where there are concentrations of multiple deprivation, the SIMD can be used to target policies and resources at the places with greatest need. The SIMD also provides a rank for each data zone within each of the seven domains, and therefore it is possible to look at individual aspects of deprivation for each area, as well as the overall level of deprivation.The dataset can be viewed by Ward, Intermediate Zone (IZ) and Scottish Parliamentary Constituency (SPC).Details of the methodology used to determine the income, employment, education, health, access (to services), crime and housing domains can be opened from this link. Depending on the browser used to access this dataset, view the document from the options appearing on the screen.The SIMD dataset has been sourced from: SpatialData.gov.scotThis dataset is also used in the associated SIMD and Child Poverty map and application.

This dataset is published as Open DataScottish Index of Multiple Deprivation, Small Area Population Estimates, and Child Poverty The Scottish Index of Multiple Deprivation 2020 is the Scottish Government’s official tool for identifying those places in Scotland suffering from deprivation. It incorporates several different aspects of deprivation (employment, income, health, education, skills and training, geographic access, crime and housing), combining them into a single index.The 2020 Index provides a relative ranking for small areas in Scotland, defined by the Scottish Neighbourhood Statistics (SNS) Data Zone 2011 geography, from 1 (most deprived) to 6,976 (least deprived). By identifying small areas where there are concentrations of multiple deprivation, the SIMD can be used to target policies and resources at the places with greatest need. The SIMD also provides a rank for each data zone within each of the seven domains, and therefore it is possible to look at individual aspects of deprivation for each area, as well as the overall level of deprivation.National Records of Scotland Small Area Population Estimates (2021)Child Poverty by Datazone (2022/23)

The English Indices of Deprivation 2019 is the official measure of relative deprivation for small areas (2011 Lower Layer Super Output Areas) in England. The Index of Multiple Deprivation ranks every small area in England from 1 (most deprived area) to 32,844 (least deprived area).

The Department of Communities and Local Government (DCLG) has released the English Indices of Deprivation 2015 (ID2015), which updates the 2010 indices of the same name. The indices are combined together to form the composite Index of Multiple Deprivation (IMD).

The IMD measures relative deprivation across small areas of England called Lower Super Output Areas (LSOAs). Datasets come from 2015, 2010 and 2007. Whilst historical datasets can be compared, there are caveats:

• LSOA definitions have changed between the 2015 and 2010 releases. As such, some locations will not be comparable at all.

• The variables used to define each indices of deprivation have been updated with each publication. As such, changes in apparent deprivation may reflect these changes in methodology rather than actual changes in local circumstance.

Compared to 2010, four out of the five Cambridgeshire districts now rank as more deprived nationally; Cambridge City ranks as less deprived.

Cambridgeshire now (in IMD 2015) has 16 LSOAs in the 20% most deprived nationally – this is compared to 9 in 2010. Two are in Cambridge City, two are in Huntingdonshire and 12 are in Fenland. Four Fenland LSOAs are in the 10% most deprived nationally.

As with 2007 and 2010, Fenland has the highest levels of deprivation in Cambridgeshire, followed by Cambridge City, East Cambridgeshire, Huntingdonshire then South Cambridgeshire.

Linked below are:

• IMD2015 data for Cambridgeshire and Peterborough

• Map of IMD2015 national rankings for Cambridgeshire and Peterborough

• IMD2010 and 2007 data for Cambridgeshire.

Spatial data supporting the England Woodland Creation Offer (EWCO) ‘Close to settlements’ Additional Contribution. This contribution is available where woodland creation will provide social and environmental benefits by being close to people. This Additional Contribution will be awarded on the following basis: 1. Spatial targeting: Proposed woodland meeting the design requirements touches the ‘EWCO - NfC Social’ layer, 2. Species mix and 3. Stocking density/

The land under this dataset meets at least one of the following criteria:

Be within the 40% most deprived areas in England that are also within 4km of built-up areas with a population of 10,000 or more.

OR

Be within 1km of built-up areas with a population of 2,000 or more.

Data input sources:

Measure of ‘deprived’ = Index of Multiple Deprivation (IMD) 2019 joined to Lower Layer Super Output Areas (LSOAs) census geographies - 2011.

Built up area = Office for National Statistics (ONS) Built-up Areas (BUAs) census geographies - December 2011.

Population = ONS Key statistics - Usual resident population - KS101EW - Census 2011.

Attributes:

‘Status’ – features assigned as ‘Meets social criteria’.

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale.

The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage.

Climate Just Map Tool includes maps on:

• Flooding (river/coastal and surface water)
• Heat
• Fuel poverty.

The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data.

Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls

Indicators include:

Climate Just-Flood disadvantage_2011_Dec2014.xlsx

Fluvial flood disadvantage index
Pluvial flood disadvantage index (1 in 30 years)
Pluvial flood disadvantage index (1 in 100 years)
Pluvial flood disadvantage index (1 in 1000 years)

Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx

Percentage of area at moderate and significant risk of fluvial flooding
Percentage of area at risk of surface water flooding (1 in 30 years)
Percentage of area at risk of surface water flooding (1 in 100 years)
Percentage of area at risk of surface water flooding (1 in 1000 years)

Climate Just-SSVI_indices_2011_Dec2014.xlsx

Sensitivity - flood and heat
Ability to prepare - flood
Ability to respond - flood
Ability to recover - flood
Enhanced exposure - flood
Ability to prepare - heat
Ability to respond - heat
Ability to recover - heat
Enhanced exposure - heat
Socio-spatial vulnerability index - flood
Socio-spatial vulnerability index - heat

Climate Just-SSVI_indicators_2011_Dec2014.xlsx

% children < 5 years old
% people > 75 years old
% people with long term ill-health/disability (activities limited a little or a lot)
% households with at least one person with long term ill-health/disability (activities limited a little or a lot)
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% households rented from social landlords
% households rented from private landlords
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% people with % unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Crime score (IMD)
% area not road
Density of retail units (count /km2)
% change in number of local VAT-based units
% people with % not home workers
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (Pounds)
% all pensioner households
% born outside UK and Ireland
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Travel time to nearest GP by walk/public transport (mins - representative time)
% of at risk pop

This dataset has been produced as part of the Mapping Potential for Working with Natural Processes research project (SC150005). The project created a toolbox of mapped data and methods which enable operational staff in England to identify potential locations for Working with Natural Processes (WWNP).

Data has been produced for each intervention covered by the project. The final outputs include the following datasets: • Floodplain Woodland Planting Potential • Riparian Woodland Planting Potential • Wider Catchment Woodland • Floodplain Reconnection Potential • Runoff Attenuation Features 3.3% AEP • Runoff Attenuation Features 1% AEP • Woodland Constraints

WWNP Floodplain Reconnection Potential is our best estimate of locations where it may be possible to establish reconnection between a watercourse and its natural floodplain, especially during high flows. The dataset is designed to support signposting of areas where there is currently poor connectivity such that flood waters are constrained to the channel and flood waves may therefore propagate downstream rapidly. The dataset is based upon the Risk of Flooding from Rivers and Sea probability maps, and identifies areas of low and very low probability that are close to a watercourse, but which do not contain residential property or key services.

The areas may contain non-residential property so it is important to consider this and recent buildings or defences when considering floodplain reconnection. Locations identified may have more recent building or land use than available data indicates. It is important to note that land ownership and change to flood risk have not been considered, and it may be necessary to model the impacts of significant reconnection.

Further information on the Working with Natural Processes project, including a mapping user guide, can be found in the reports published here:

https://www.gov.uk/government/publications/working-with-natural-processes-to-reduce-flood-risk

Under the Natural Capital and Ecosystem Assessment (NCEA) Pilot, Natural England and the Botanical Society of Britain and Ireland (BSBI) have been working in partnership to use BSBI's vast database of plant records to inform the evidence base for tree-planting activities. Poorly targeted tree planting risks damaging wildlife and carbon-rich habitats, therefore using these data we aim to ensure that areas of high conservation value are preserved in the landscape.

The summarised botanical value map provides an easily interpretable output which categorises monads (1 x 1 km grid squares) as being of Low, Moderate or High botanical value according to the presence of Rare, Scarce and Threatened (RST) plant species and/or the proportion of Priority Habitat Positive Indicator (PHPI) species that were recorded within the 1 x 1 km grid square between 1970 and 2021. The PHPI species are a combination of BSBI axiophytes, positive indicators for common standards monitoring and ancient woodland indicators. The dataset includes an overall botanical value, as well as values based on only the presence of RST plant species, and a value for each broad habitat type based on the PHPI species records. By viewing the different attributes, you can gain insights into how valuable a monad is for different habitat types and for plant species of conservation concern, as well as an indication of how well a particular monad has been surveyed. The categories of 'No indicators, poor survey coverage' and 'No indicators, good survey coverage' indicate where no indicator species have been recorded and survey coverage either is above or below a threshold of 3 'recorder days'. A 'recorder day' is defined as being when 40 or more species have been recorded on a single visit and 3 recorder days is assumed sufficient to achieve good survey coverage within a 1 x 1 km grid square.

This map is not intended to be used to carry out detailed assessments of individual site suitability for tree planting, for which the RST plant species heatmap at 100 x 100 m resolution and the PHPI heatmaps at 1 x 1 km resolution have been developed by BSBI and Natural England. However, the summarised botanical value map can provide useful insights at a strategic landscape scale, to highlight monads of high value for vascular plants and inform spatial planning and prioritisation, and other land management decision-making. These should be used alongside other environmental datasets and local knowledge to ensure decisions are supported by the appropriate evidence. Please get in contact if you have any queries about the data or appropriate uses at botanicalheatmaps@naturalengland.org.uk

Further information can be found in the technical report here: http://nepubprod.appspot.com/publication/5063363230171136.

The aim of this project was to scope and create a child health system map for use at a local level in order to inform opportunities for effective interventions at a systems level to reduce child health inequalities. Child health inequalities in the UK are persistent and increasing, and health outcomes for children and young people are worse than in many other western countries. However, we currently lack understanding of how to support children at a local level. A systems approach to public health views poor health and health inequalities as outcomes of a complex web of interdependent elements that work together as a system. It places emphasis on understanding the whole system of influences on child health, rather than individual factors. In this project, we use a systems ‘lens’ to map the components of local systems that influence child health to develop a tool (a system map) to support local planning and implementation of actions to improve child health and reduce inequalities. Face to face, in depth qualitative interviews were held in each case study area with senior decision makers who held responsibility for child health. A topic guide was developed for the interviews that covered the local context and key child health challenges, local priorities for child health, key initiatives developed in recent years to improve child health outcomes and reduce child health inequalities, and experience of whole-system approaches to child health (generally, and specific to the local area). The interviews were also used to identify key local documentation (policy and strategy documents), and further participants for both qualitative interviews, and involvement in group mapping workshops. Qualitative, face-to-face interviews were conducted with senior decision-makers with responsibility for child health. Interview samples were drawn from two local authority sites that formed our case study areas. These areas were selected on the following criteria i) proximity to the research team for ease of access and cost-effectiveness, ii) variation in terms of urban and rural location, iii) variation regarding authority type - one unitary authority and one two-tier, iv) geographical distance - one in the North and one in the South of England and v) contrast in terms of area level index of multiple deprivation scores. In each case study we worked with our lead contacts in the local authority and local Clinical Commissioning Group site to identify and recruit interview participants. Interviews lasted between 30-60 minutes.

The “Pupils in Scotland Census” is undertaken annually and collates a wide variety of information on pupils in all publicly funded schools in Scotland. These files provide information at the level of individual schools separated into primary, secondary and additional support for learning (ASL) provision.Each file contains information on: school roll;proportion of pupils living in the 20% most deprived areas in Scotland (using the SIMD 2020 ver 2);ethnicity;the proportion of pupils for whom English is an additional language.The Pupil in Scotland census data is obtained from schools management information systems and is validated by ScotXed.

The “Pupils in Scotland Census” is undertaken annually and collates a wide variety of information on pupils in all publicly funded schools in Scotland. These files provide information at the level of individual schools separated into primary, secondary and additional support for learning (ASL) provision.Each file contains information on: school roll;proportion of pupils living in the 20% most deprived areas in Scotland (using the SIMD 2020 ver 2);ethnicity;the proportion of pupils for whom English is an additional language.The Pupil in Scotland census data is obtained from schools management information systems and is validated by ScotXed.

This a peer reviewed article in IEE computer graphics and applications 27 (2). Despite much published research on its deficiencies, the rainbow colour map is prevalent in the visualization community. The authors present survey results showing that the rainbow colour map continues to appear in more than half of the relevant papers in IEEE Visualization Conference proceedings. Its use is encouraged by its selection as the default colour map used in most visualization toolkits that the authors inspected. The visualization community must do better. In this article, the authors reiterate the characteristics that make the rainbow colour map a poor choice, provide examples that clearly illustrate these deficiencies even on simple data sets, and recommend better colour maps for several categories of display

Website: https://www.researchgate.net/publication/6419747_Rainbow_Color_Map_Still_Considered_Harmful

These fuel poverty risk indicators provide users with a nuanced picture of the impact of various risk factors, exacerbating factors and indicators for fuel poverty. It was developed with the Assembly Health and Public Services Committee in their investigation into fuel poverty in London. The Committee's report explains how the tool could be used strategically to help organisations target specific wards that are at high risk of fuel poverty. Appendix 4 in the report set out the rationale for the risk factors present in the tool.

Users can adjust the weighting of the indicators to show their relative significance. Isolating specific indicators could help organisations determine what type of support is likely to have greatest impact in an area. For example, wards with a low score for cavity wall insulation would indicate wards that could be targeted for promoting uptake of cavity wall insulation.

Read Victoria Borwick's blog "Using public data to tackle fuel poverty - can you help?"

The fuel poverty scores measure risk of fuel poverty based on 12 indicators. The England and Wales average each year is 0. Scores below 0 are more likely to be at risk from fuel poverty according to these measures.

The indicators are:

Housing
Dwellings without central heating
Cavity walls that are uninsulated
Lofts with less than 150mm insulation

Health
Health Deprivation & Disability domain (ID2010)
Standardised Mortality Ratio
Incapacity Benefit claimant rate

Older people
People aged 60 and over
Older people claiming pension credit

Worklessness
Unemployment

Poverty
Income Support claimant rate
Child Poverty rates
Households classified 'fuel poor'

The Excel tool includes a ward map, charts and rankings.

Note: Users must enable macros when prompted upon opening the spreadsheet (or reset security to medium/low) for the map to function. The rest of the tool will function without macros.

https://s3-eu-west-1.amazonaws.com/londondatastore-upload/fp-dashboard-map.jpg" alt="Excel Tool"/>

Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.