Profiling SA’s system of towns - Introducing

the CSIR/SACN South African Settlement

Typology Van Huyssteen, E., Mans, G., le Roux, A., Maritz, J., Ngidi, M. & Maditse, K. CSIR, Built Environment Date: February 2016

For more information contact evhuyssteen@csir.co.za

Citation: Van Huyssteen, E., Mans, G., le Roux, A., Maritz, J., Ngidi, M. & Maditse, K. 2016.

Profiling SA’s system of towns - Introducing the CSIR/SACN South African Settlement Typology.

CSIR document. Available on stepsa.org

Introduction

It is estimated that almost 78% of South Africa’s population reside within cities and towns throughout

the so-called urban and rural landscapes. Of the 78%, it was calculated in 2011 that 42% resided

within the four City Regions, namely Gauteng, Cape Town, eThekwini and Nelson Mandela Bay; 7.5%

lived within Cities, approximately 14% in several large Service Towns and the remainder in smaller

towns. Given that a further 12% of South Africans actually reside in high density ‘rural’ settlements

and that a mere 14% of the country’s population reside further than 20km away from a formal town or

city1, it is evident that South Africa can no longer afford to ignore the fact that this country’s

development challenges will need to be addressed within its network of cities, towns and settlements.

Over the last couple of years, the Council of Scientific and Industrial Research (CSIR), together with a

number of other role players, has invested in building capability to provide spatial planning, analysis,

modelling and platform support to government, with a focus on strengthening strategic regional, inter-

regional and intergovernmental planning, resource allocation and monitoring and evaluation in South

Africa. The functional settlement typology provides a mechanism to profile, identify, calculate and

analyse a set of development information and trends pertaining to the range of towns and cities, as

well as high density rural settlements across South Africa. It enables understanding and analyses of

the network of settlements, towns and cities and the hierarchical and functional relationships between

them (Figure 1), especially related to government and economic service provision and migration.

1 For a definition of settlements (City Regions, Cities, Towns, etc.) see Table 1.1

Due to its wide application by the National Planning Commission (NPC) in the National spatial

diagnostics report (stepsa.org); by the Department of Rural Development and Land Reform (DRDLR)

in understanding development dynamics in the priority rural districts; by Statistics South Africa in

analysing household survey data and to inform discussion regarding urban v/s rural and by the

Department of Cooperative Government (DCOG) in the development of the latest Urban Development

Framework (Van Huyssteen et al, 2013), the typology was refined and updated in 2013-2014. Since

then, the new typology has been used as key input to profile the growth of towns, as well as the

extent of change in population dynamics within towns and cities in the latest draft of the Integrated

Urban Development Framework (IUDF) and State of Cities Report (South African Cities Network,

2016).

Figure 1: South African town and settlement functions and sizes (2013)

Background

The Functional Settlement Typology was originally developed by the CSIR as part of the National

Spatial Trends Overview project (2008-2009) commissioned by the South African Cities Network, The

Presidency and former DPLG to inform Cabinet discussions on urban development policy aspects

and the process of developing a National Urban Development Framework (SACN et al, 2009). It was

developed through an interactive technical and consultative process, overseen by an expert reference

group. The typology development was widely consulted, presented and utilised within development

planning, and government policy and planning units (Van Huyssteen et al, 2009 & Spocter et al,

2010). It is based on a classification of settlement patterns (Figure 2). For more information on the

development process and core building blocks (Figure 3) visit http://stepsa.org/settlement_typology.

Figure 2: South African settlement patterns

Figure 3: Original methodology of the CSIR/SACN functional settlement typology

Categories

The spatial distribution of cities, towns and settlements according to the typology is set to represent

the functional role that cities and towns play in their regional contexts, not a mere hierarchical

calculation of population and/or economic production or services. The categories and definitions are

set out in Table 1. It’s important to note that place names of the biggest and most well-known

towns/settlement are used for ease of reference but refer to an area that includes the functional town

and its close surroundings (especially thus integrating former apartheid towns within one functional

group) to enable an understanding not only of formal economic activities but also the extent of

population in the area and thus also the functional role of the bigger town area in providing access to

services and opportunities. Metro’s and secondary cities are included within the City regions and ‘city

area’ category within the typology.

Figure 2: South African settlement typology by category and location

Figure 5: CSIR/SACN Functional settlement system with relative size of population and formal economic productivity

Table 1: CSIR/SACN Functional Settlement Typology Categories

CATEGORY DESCRIPTION IMAGE REPRESENTING CHARACTERISTICS

City Region Areas

Population >1million, Government & Economic Services Index >7 EXAMPLES: Global city region: Gauteng Coastal City regions: Cape Town City Region, eThekwini City Region, Nelson Mandela Bay City Region

CATEGORY DESCRIPTION IMAGE REPRESENTING CHARACTERISTICS

City Areas

Population 500 000 – 1million Government & Economic Services Index 2-5 EXAMPLES (this includes metro’s and secondary city areas) Polokwane Mbombela Pietermaritzburg Buffalo City Mangaung

Regional Service Centres (Bigger and medium sized towns with key roles in the surrounding hinterland)

Regional Centre 1

Population 300 000 -500 000 Government & Economic Services Index 1-2 EXAMPLES Regional Service Centres - high population numbers and high economic activity: Rustenburg Witbank/Middelburg New Castle Richards Bay Regional Centre 2

Population 100 000 - 300 000 Government & Economic Services Index >0.3 EXAMPLES Regional Service Centres - high population numbers in densely settled areas: Mthata Queenstown Thohoyandou Tzaneen Grahamstown Mmabatho

Regional Centre 3

Population 40 000 - 100 000 Government & Economic Services Index >0.25 EXAMPLES Regional Service Centres - low population numbers playing a key role in sparsely populated areas: Kimberley Upington Worcester

Service Town (Medium to smaller sized towns)

Population mostly >20 000 Significant role in hinterland (service Index 0.065-0.25) EXAMPLES Saldanha Groblersdal Piet Retief Bothaville Lichtenburg

Local and Niche Towns

Population size varies widely Service role in immediate surroundings (Service Index 0.001-0.065) EXAMPLES Local Towns: Alice (EC), Koppies (FS) Niche Towns: Clarens (FS), Prince Albert (WC), Riebeeck-Kasteel (WC)

High Density Settlement Areas

Rural Nodes in High Density Settlement Areas - Meso Zones with >100 people/square km OR more than 10 people/square km PLUS Economic activity in service sector - identified as areas within high density settlement areas, with highest levels of access to household income High Density Settlement Areas - Meso Zones with >100 people/square km OR more than 10 people/square km PLUS Economic activity in service sector. These areas typically have very little economic activity, no consolidated town centre/nodes, and a spread out morphological structure.

Rest of South Africa

Less densely populated areas, Sparsely populated areas, mountainous, national parks

Table 1: Functional settlement type (CSIR/SACN Settlement Typology, 2013)

Results

Findings from analyses based on the profiles made available on the StepSA platform, also illustrate

the crucial role that these populated places, and especially city region areas play as economic

engines and job baskets within South Africa. An estimated 57% of the formal economy alone is being

generated in the City Regions. We add to the City Regions the network of Cities and major towns and

this includes more than 80% of the total South African economy. Table 2 provides an overview of the

extent (land area in square kilometres), population and economic activity (using GVA as indicator) for

the various categories of place.

Table 2: Comparative analyses of population and economic activity in South Africa (Van Huyssteen et al, 2013)

The profiles have also been used successfully in the analyses of urban and settlement growth and

changing demographic profiles in South Africa, which place the importance of towns and cities in the

country’s development future firmly on the urban and rural agenda. See the Policy Brief prepared in

this regard “Reaching development outcomes through a dedicated focus” available on

http://stepsa.org/pdf/policy_implication_notes

For more information on the updated CSIR/SACN South African Settlement Typology and related

analyse, research and implications contact:

Elsona van Huyssteen, CSIR

evhuyssteen@csir.co.za

Functional Settlement

Type (CSIR/SACN

2013v2)

Area_Km % of

National

Area

Population

2011

% of

National

Population

Service

Economy (Service Sector

GVA (xR1000))

Economic

Activity (*Total GVA

(xR1000))

Contribu-

tion to

Formal Nat

Econ Activity

CityRegions 20 575 1.65% 21 856 192 42.22% 758 652 1 185 948 56.77%

Cities 8 225 0.66% 3 876 064 7.49% 102 574 178 276 8.53%

TOTAL CITIES 28 800 2.30% 25 732 256 49.70% 861 226 1 364 224 65.30%

Regional Centres 18 079 1.45% 7 313 730 14.13% 141 580 229 697 10.99%

ServiceTowns 7 232 0.58% 2 720 372 5.25% 47 847 87 232 4.18%

TOTAL MAJOR TOWNS 25 311 2.02% 10 034 102 19.38% 189 427 316 929 15.17%

Local or Niche Towns 29 756 2.38% 4 327 891 8.36% 69 102 121 169 5.80%

Rural Nodes in High

density areas 928 0.07% 191 123 0.37% 2 537 4 850 0.23%

TOTAL SMALL TOWNS 30 684 2.45% 4 519 014 8.73% 71 639 126 019 6.03%

High Density

Settlements 59 276 4.74% 6 081 912 11.75% 40 074 73 587 3.52%

Sparse Rural Areas 1 070 931 85.66% 3 036 010 5.86% 51 830 184 994 8.86%

Dense Rural Areas 35 258 2.82% 2 366 803 4.57% 13 921 23 351 1.12%

TOTAL REST OF SA 1 165 465 93.22% 11 484 725 22.18% 105 826 281 932 13.50%

NATIONAL TOTALS 1250260 100.00% 51770097 100% 1228117 2089104 100%

* GVA Total excludes Construction sector

77.82%

SOURCE: CSIR GAP 2013 based on StatsSA Census 1996,2001,2011; SACN/CSIR Settlement Typology 2013v2, CSIR TAT (Temporal

Analyses Tool) 2013

2011 Population in cities & tow

ns

2011 Population in cities,towns &

Settlements

89.56%

References

SACN, the Presidency and DCOG (2009). National Spatial Trends Overview. CSIR: Unpublished

Report.

SACN/CSIR Settlement Typology (2013).

Spocter, M., van Huyssteen, E., Mans, G. and Green, C. (2010) Overview of Settlement Typologies

in South Africa. Unpublished Report, CSIR.

Van Huyssteen, E., le Roux, A. and Mans, G. (2013) Exploring the value of Settlement Typologies in

Urban v/s Rural Definitions. Unpublished Presentation to the IUDF Urban/Rural Definition

Working Group, 14 January 2013.

Van Huyssteen, E., Manfred, G., Maritz, J., le Roux, A. and Nigidi, M. (2013) Update of the

CSIR/SACN South African Settlement Typology. Unpublished Report, CSIR.

Van Huyssteen, E., Mans, G. and Ngidi, M. (2013) Reaching development outcomes through a

dedicated focus on cities, towns and settlements. StepSA Policy Brief Series: Policy Brief 3.

Accessed at http://stepsa.org/pdf/policy_implication_notes. December 2015.

Van Huyssteen, E., Mans, G., Maritz, J., van Niekerk, W., Le Roux, A., Green, C., et al. (2014).

Regional and temporal dynamics of South African cities and towns. Unpublished PG research

report. Pretoria: CSIR.

Van Huyssteen, E; Biermann, S.; Naudé, A. and le Roux, A. (2009) Advances in Spatial Analysis to

Support a more Nuanced Reading of the South African Space Economy, Urban Forum, 20 (2),

195-214).