Planning innovation for better urban communities in sub-Saharan Africa: The education challenge and potential responses
Bibliographic record
Abstract
Cities in the sub-Saharan Africa region present challenges to the urban and regional planning profession, city managers, leaders, educationists and dwellers (Rakodi, 1997, 2001; McGill, 1988; Diaw, Nnkya & Watson, 2002). This is at a time when Africa is urbanising faster than any other region (UN-Habitat, 2008), calling for a rethinking of planning to respond to existing needs. Although the current urbanisation level is at 39.1% (UN-Habitat, 2008), it is projected to increase to over 50% by 2025. This outstanding demographic shift on the African continent and particularly in the sub-Saharan region presents current and future urban challenges. In addition to the future challenges, the unresolved question as to whether existing and much utilised models of urban development offer solutions to the planning needs in the region should be investigated, although it is important to recognise the failures of locally designed initiatives. The models have been critiqued widely (Brockerhoff, 2000; Arimah & Adeagbo, 2000) and this is not the focus of this article. However, it is necessary to recognise that the planning profession has relied on these models through the planning education system. Notwithstanding the challenges of resources, leadership, and political dispensations, planning education systems have played a role in influencing and shaping urban development in the region. Although planning models have been critiqued, planning education systems have received less attention in respect of their role in influencing the development pathways of cities in sub-Saharan Africa. Likewise, planning education systems have not adequately been viewed as points of entry in planning innovation for new urban Africa. Drawing from experiences of cities in the region, two urban development processes can be discerned: first, the explosion of some cities particularly former colonial administrative or economic hubs and, second, the fast growth of secondary cities. There are also many small rural trading centres and ‘hamlets’ with densities comparable to neighbourhoods of the large-cities. The latter, conceptualised in this article as urbanisation by implosion, is not properly accounted for in the national statistical reports. Several drivers are responsible for this urbanisation, including population dynamics, legislative designation, and increasing densities in rural trading centres. The challenges of social service provision, sustainable economic development, housing delivery, urban governance, spatial development guidance and urban environmental management are yet to be thoroughly analysed and rethought in planning education in the context of addressing the existing needs. This article examines the planning education system and how it has influenced the nature and shape of cities in sub-Saharan Africa, the outcome of which may not have substantively responded to existing needs. This article will also identify possible points of innovation in planning education that may create a difference in addressing the existing needs in sub-Saharan Africa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".