Planning innovation for better urban communities in sub-Saharan Africa: The education challenge and potential responses
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,009 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,002 | 0,014 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».