The Scottish Index of Multiple Deprivation is a relative measure of deprivation across 6,976 small areas (called data zones). If an area is identified as ‘deprived’, this can relate to people having a low income but it can also mean fewer resources or opportunities. SIMD looks at the extent to which an area is deprived across seven domains: income, employment, education, health, access to services, crime and housing.

SIMD is the Scottish Government's standard approach to identify areas of multiple deprivation in Scotland. It can help improve understanding about the outcomes and circumstances of people living in the most deprived areas in Scotland. It can also allow effective targeting of policies and funding where the aim is to wholly or partly tackle or take account of area concentrations of multiple deprivation.

SIMD ranks data zones from most deprived (ranked 1) to least deprived (ranked 6,976). People using SIMD will often focus on the data zones below a certain rank, for example, the 5%, 10%, 15% or 20% most deprived data zones in Scotland.

SIMD is an area-based measure of relative deprivation: not every person in a highly deprived area will themselves be experiencing high levels of deprivation.

Data zones in rural areas tend to cover a large land area and reflect a more mixed picture of people experiencing different levels of deprivation. This means that SIMD is less helpful at identifying the smaller pockets of deprivation found in more rural areas, compared to the larger pockets found in urban areas. SIMD domain indicators can still be useful in rural areas if analysed separately from urban data zones or combined with other data.

Data zones are the key geography for the dissemination of small area statistics in Scotland and are widely used across the public and private sector. Composed of aggregates of Census Output Areas, data zones are large enough that statistics can be presented accurately without fear of disclosure and yet small enough that they can be used to represent communities. They are designed to have roughly standard populations of 500 to 1,000 household residents, nest within Local Authorities, have compact shapes that respect physical boundaries where possible, and to contain households with similar social characteristics. Aggregations of data zones are often used to approximate a larger area of interest or a higher level geography that statistics wouldn’t normally be available for. Data zones also represent a relatively stable geography that can be used to analyse change over time, with changes only occurring after a Census.