State of Deprivation
Micro Guide
A Note on Using This Guide:
Like the other NSH dashboards, the State of Deprivation dashboard is best understood as a point of departure for deeper interrogation, not a finished analysis. It is intended to be used in conjunction with other NSH dashboards and municipal datasets for a more comprehensive view of the data landscape. The sections below model one way of reading the data: they surface patterns, raise the planning questions those patterns suggest, and point to where further investigation would sharpen the answer, using additional municipal data, project records, or local knowledge.
The State of Deprivation dashboard presents two complementary lenses for understanding socioeconomic disadvantage: the South African Index of Multiple Deprivation (SAIMD) on Page 2, and the South African Multidimensional Poverty Index (SAMPI) on Page 3. Together, they offer a starting point for moving beyond income-based poverty measures towards a more textured understanding of how deprivation is experienced across different dimensions of life.
Note: The analysis below refers to the following filters applied to both pages: Eastern Cape (Province); Buffalo City (Municipality); 2022 (Year) for Page 2, and 2016 (Year) for Page 3. The insights, therefore, reflect patterns of deprivation in Buffalo City Metropolitan Municipality. Note that the two pages draw on different data sources and publication years, and that the dashboard does not show variation within the municipality. The analysis is intended to guide interpretation rather than to provide a complete account of all deprivation dynamics.
SAIMD is a composite index that measures socioeconomic deprivation across four key dimensions: material deprivation, living environment deprivation, employment deprivation, and education deprivation. In 2022, the average SAIMD score for Buffalo City was 70 out of 100, a figure that, on a scale where higher scores reflect greater deprivation, suggests the municipality was experiencing significant multidimensional disadvantage.
The bar chart and map show this score for the selected geography. To see how Buffalo City compares with other municipalities, select the whole Eastern Cape; with a single municipality selected, they show only that municipality’s own score. The four sub-dimension maps below break this score down by dimension, but they do not show variation within the municipality, so understanding where deprivation is concentrated at neighbourhood level requires additional data.
The four sub-dimension maps for material, living environment, employment and education deprivation are a useful starting point for planners. Each map shows a colour gradient of relative deprivation severity only when a whole province is selected, which lets you compare municipalities within that province. When a single municipality is selected, the map shows one colour with no gradient, so it is not a relative view and the colour carries no significance on its own. Reading the dimensions together rather than in isolation is important: a municipality may score well on education access but poorly on employment or show strong housing conditions while experiencing severe material deprivation. The combination of high employment deprivation and high material deprivation, for example, may signal not just poverty but a structural absence of economic opportunity, a condition with implications for how planning interventions are designed and targeted, and one worth testing against local data.
SAMPI provides a different but complementary view of deprivation. Where SAIMD measures deprivation as a relative index across geographies, SAMPI identifies individuals and households living in multidimensional poverty by assessing simultaneous deprivation across health, education, living standards, and economic activity. The SAMPI headcount for Buffalo City in 2016 was 7.54%, indicating that approximately 1 in 13 residents experienced poverty across multiple dimensions simultaneously.
This figure is significant in two ways. First, it captures poverty beyond income – not merely whether people lack income, but whether they are simultaneously deprived across the things that constitute a dignified life. Second, because the SAMPI data used here is from 2016, it provides a baseline against which more recent changes in deprivation conditions can be assessed. Conditions may have shifted considerably since 2016, and SAMPI should be read alongside more recent data sources where available.
The municipality-level bar chart and map show Buffalo City’s SAMPI headcount for the selected geography. Selecting the whole Eastern Cape allows comparison with other municipalities, while a single-municipality selection shows only Buffalo City’s own figure. Neither view shows where poverty is concentrated within the metro. For a metro with a significant urban core, KuGompo City (previously East London) and Qonce (previously King William’s Town), a 7.54% SAMPI headcount is consistent with the persistent pockets of deep poverty that often characterise South African metros, where formal economic activity and severe informal settlement deprivation coexist within the same municipal boundary. Confirming whether, and where, this applies in Buffalo City requires finer-grained data, such as the ward-level Census (when it becomes available) and other survey sources (see Explore Alternative Data Sources section below).
The deprivation dashboard offers a municipal-level view of the scale and dimensions of socioeconomic deprivation. But deprivation is not a self-contained condition. It is produced and sustained by intersecting pressures: where people live, what services they can access, whether there is work, how safely they can move through their environment, and whether the infrastructure around them supports or undermines their wellbeing. To plan effectively for communities experiencing deprivation, these connections need to be made explicit.
Cross-referencing NSH dashboards gives a district- or municipal-level orientation to these connections. Drawing from municipal datasets and local knowledge, use the combined insights from these dashboards as a point of departure for deeper interrogation of how these dynamics play out in different areas and neighbourhoods.
Note: Where you are directed to open a separate dashboard for comparison purposes, ensure that preferred filters are applied before proceeding. If continuing with the example above, set Province to Eastern Cape and, where available, Municipality to Buffalo City (or District, where a dashboard filters only to that level) on each dashboard opened, as filter selections will not carry over automatically between views. Take note when using insights from different years.
Tip: The dashboards provide a district and municipal-level starting point. The questions below are prompts for deeper interrogation, and answering them well will usually require municipal datasets, such as project registers and ward-level service data, that show how these patterns play out in different areas.
Start by asking:
Living environment deprivation captures the absence of adequate housing and service delivery, precisely the conditions that MIG-funded infrastructure is designed to address. High scores on this dimension may indicate that water, sanitation, roads, or community facility investment has not yet reached these communities at a sufficient scale or quality.
Open the State of the Municipal Infrastructure Grant dashboard.
Look for MIG spending levels, project types, and household benefit figures at the district level. Note that the household benefit figure is an estimate drawn from municipal MIG applications of the households expected to be served once projects are complete, rather than a measure of households already served.
Questions to consider:
Start by asking:
Deprivation can often be very acute in areas absorbing rapid in-migration or informal settlement growth, where the pace of population change exceeds the capacity of municipalities to extend services. Understanding whether high-deprivation areas are also high-growth areas helps planners anticipate where conditions are likely to worsen without proactive investment.
Open the State of Population dashboard.
Look for population growth trends and settlement density patterns at the district and municipal level, and note which municipalities show rapid growth or densification.
Questions to consider:
Start by asking:
The relationship between deprivation and crime is well-documented. Material deprivation, unemployment, and poor living environments can create conditions in which certain types of crime are more likely to occur and in which communities feel less safe. Understanding this intersection helps planners think beyond crime as a policing issue and towards it as a spatial and social condition that planning interventions can help address.
Open the State of Crime and Safety dashboard.
Look for contact crime and property crime patterns for the municipality, and compare them with its sub-dimension deprivation scores.
Questions to consider:
Start by asking:
Education deprivation (a SAIMD sub-dimension) and health deprivation (captured through SAMPI) are directly shaped by the availability, accessibility, and quality of social amenities.
Open the State of Social Amenities dashboard.
Look for the overall distribution of schools, health facilities, libraries, and community infrastructure as a starting orientation for the municipality.
Questions to consider:
Deprivation data is not a passive description of where poverty exists. It is a planning input that can help shape where resources are directed, how services are designed, and which communities are prioritised for intervention. For municipal planners, SAIMD and SAMPI together provide a point of departure which, combined with other datasets, supports evidence-based targeting, moving beyond intuition or political preference towards a spatially grounded understanding of where need is greatest.
The multi-dimensional nature of deprivation has direct implications for how planners approach their work. A municipality cannot address multidimensional deprivation through housing policy or road investment alone. Effective planning requires matching the right interventions to the specific deprivation dimensions driving poor outcomes in a given community. The sub-dimension maps on Page 1 of the dashboard can help orient this kind of targeted diagnostic thinking at the municipal level, before finer-grained data is used to refine it.
For planners, deprivation insights can inform and prompt further investigation into decisions such as:
Prioritising infrastructure investment in areas where living environment and material deprivation scores are highest
Aligning social amenity provision with areas of education deprivation and health-related poverty
Designing economic development interventions in areas where employment deprivation is the dominant driver of the composite SAIMD score
Using SAMPI headcount data to help make the case for resource allocation in budget and IDP processes
Tracking changes in deprivation scores over time as one measure of whether planning and investment decisions are improving conditions on the ground
Critically, deprivation data challenges the tendency to treat all parts of a municipality as equivalent. Within a metro or district, deprivation is spatially uneven. The dashboard does not show this unevenness below the municipal level, so planning that relies on it alone risks directing resources to relatively less deprived areas and leaving the most vulnerable communities behind. Combining it with finer-grained municipal data helps guard against this.
To deepen the analysis, planners may consider combining dashboard insights with other datasets, including:
