Predictive Modelling for Urban Growth

Local Chapter Nairobi, Kenya Chapter

Coordinated byKenya ,

Status: Completed

Project Duration: 06 Mar 2023 - 17 Apr 2023

Open Source resources available from this project

Project background.

Urban population growth in African cities, including Nairobi, has been significant in recent decades. Nairobi, the capital city of Kenya, has experienced a rapidly growing population, due in part to rural-urban migration and natural population growth. According to the United Nations, the population of Nairobi was estimated to be just over 3 million in 2019, and it is projected to continue growing in the coming years.

Other African cities have also experienced significant urban population growth, including Lagos in Nigeria, Cairo in Egypt, and Kinshasa in the Democratic Republic of the Congo. These cities have attracted large numbers of people seeking better economic opportunities and a higher quality of life.

However, this rapid urbanization has also brought challenges such as housing shortages, traffic congestion, and a strain on city services and infrastructure. In response, African governments and international organizations are working to develop urban planning and management strategies to address these issues and ensure sustainable urban growth in the future.

The problem.

There are several problems related to urban population growth in African cities that can be addressed using machine learning:
1. Housing and land use planning: Machine learning algorithms can be used to analyze and predict housing demand, identify optimal land use patterns, and support urban planning decisions.
2. Traffic congestion: Machine learning models can be trained to analyze traffic patterns and predict congestion in real-time, helping city planners to optimize traffic flow and reduce congestion.
3. Environmental monitoring: Machine learning algorithms can be used to monitor and predict environmental indicators, such as air and water quality, to support sustainable urban development.
4. Public services optimization: Machine learning can be used to analyze data on population growth, demographics, and public services usage to optimize resource allocation and improve service delivery in urban areas.
5. Predictive maintenance: Machine learning algorithms can be used to predict when public infrastructure such as roads, bridges, and buildings will require maintenance, reducing the costs and disruptions associated with reactive maintenance.

Project goals.

- Create a housing demand and land use prediction model that can support urban planning decisions and ensure sustainable urban growth. - Develop a traffic management system that can optimize traffic flow and reduce congestion in urban areas using machine learning algorithms. - Create an environmental monitoring system that can provide real-time data on air and water quality, and support sustainable urban development. - Optimize the allocation of resources for public services, such as healthcare and education, using machine learning algorithms that analyze population growth and demographics. - Reduce the costs and disruptions associated with reactive maintenance by using machine learning algorithms to predict when public infrastructure will require maintenance. - Develop a tool that can support decision-making by city governments and urban planners by providing insights and predictions based on data analysis. - Improve the quality of life for residents of African cities by addressing the challenges posed by rapid urbanization, such as housing shortages and traffic congestion.

Project plan.

  • Week 1

    Week 1: Data collection and cleaning

    Identify relevant data sources, such as demographic data, traffic data, and environmental data.
    Collect and compile the data into a structured format.
    Clean and pre-process the data to ensure that it is ready for analysis.

  • Week 3

    Week 4-5: Model development and training Choose appropriate machine learning algorithms based on the goals of the project and the nature of the data. Develop and train the machine learning models using the pre-processed data. Evaluate the performance of the models using various metrics, such as accuracy, precision, and recall.

  • Week 4

    Week 6-7: Model refinement and improvement Refine the models based on the results of the performance evaluations. Iteratively improve the models by adding new features, adjusting the algorithms, or incorporating additional data.

Learning outcomes.

Knowledge of machine learning algorithms: Participants will gain hands-on experience with various machine learning algorithms and techniques, including regression analysis, decision trees, and neural networks. Understanding of urban planning and management: Participants will gain a deeper understanding of the challenges and opportunities associated with urban population growth and urban planning, and how machine learning can be applied to address these challenges. Data analysis skills: Participants will develop skills in collecting, cleaning, and analyzing large datasets, and using the insights gained from these datasets to inform decision making. Collaboration and stakeholder engagement: Participants will learn about the importance of collaborating with stakeholders, including city governments, urban planners, and local communities, and will gain experience in engaging with these stakeholders to develop solutions that meet their needs. Communication skills: Participants will develop skills in communicating complex technical concepts to a non-technical audience, and in presenting data-driven insights and recommendations to decision-makers. Problem-solving skills: Participants will develop their problem-solving skills by applying machine learning algorithms to real-world problems and developing solutions that have the potential to have a positive impact on the lives of people in African cities. Understanding of the social impact of technology: Participants will gain an understanding of the role that technology can play in addressing social and environmental challenges, and will develop a sense of responsibility for using technology for the greater good.

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