Editorial

At the crossroads of datas: Using artificial intelligence for efficient and resilient planning in African cities

  • Received: 25 December 2023 Revised: 29 December 2023 Accepted: 30 December 2023 Published: 31 December 2023
  • By collecting and conducting in-depth analysis of official statistical data, empirical evidence, and exploring integrated artificial intelligence (AI) approaches in diverse African urban landscapes, this article examines the integration of formal and informal data to bolster urban resilience in Africa. It underscores the significance of this diverse data in crafting effective urban planning strategies tailored to the needs of African city-dwellers. The AI-driven analysis, amalgamating these datasets, provides a comprehensive understanding of urban realities and community needs in African cities. Additionally, the article emphasizes how harnessing so-called informal data—often overlooked yet crucial—alongside formal data enables more contextual and adaptive planning aligned with local needs and aspirations. Highlighting the transformative potential of data-driven approaches, the article presents AI strategies as proactive tools to address the challenges faced by African cities, fostering resilience amidst socio-economic and environmental shifts. Ultimately, it advocates for the integration of diverse data sources in urban planning, utilizing AI as a catalyst, underlining the importance of a holistic approach that values both formal and informal data for sustainable and resilient urban development in Africa.

    Citation: Cheikh Cisse. At the crossroads of datas: Using artificial intelligence for efficient and resilient planning in African cities[J]. Urban Resilience and Sustainability, 2023, 1(4): 309-313. doi: 10.3934/urs.2023019

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  • By collecting and conducting in-depth analysis of official statistical data, empirical evidence, and exploring integrated artificial intelligence (AI) approaches in diverse African urban landscapes, this article examines the integration of formal and informal data to bolster urban resilience in Africa. It underscores the significance of this diverse data in crafting effective urban planning strategies tailored to the needs of African city-dwellers. The AI-driven analysis, amalgamating these datasets, provides a comprehensive understanding of urban realities and community needs in African cities. Additionally, the article emphasizes how harnessing so-called informal data—often overlooked yet crucial—alongside formal data enables more contextual and adaptive planning aligned with local needs and aspirations. Highlighting the transformative potential of data-driven approaches, the article presents AI strategies as proactive tools to address the challenges faced by African cities, fostering resilience amidst socio-economic and environmental shifts. Ultimately, it advocates for the integration of diverse data sources in urban planning, utilizing AI as a catalyst, underlining the importance of a holistic approach that values both formal and informal data for sustainable and resilient urban development in Africa.



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