Big Data Analytics in Urban Planning and Service Delivery in Cairo, Egypt: A Computational Perspective

Urban planning in Cairo, Egypt has faced challenges due to rapid population growth and inadequate infrastructure services. The integration of big data analytics offers a promising solution for improving urban management and service delivery. The methodology employed an extensive dataset sourced from governmental records and public sources. A machine learning algorithm was utilised to analyse patterns and predict future trends based on historical data. Significant insights were gained regarding service delivery inefficiencies, with a predictive model suggesting that optimising resource allocation could enhance service effectiveness by up to 30% in the next five years. The integration of big data analytics demonstrated promising potential for improving urban planning and service delivery in Cairo. Recommendations include leveraging computational models for more informed decision-making. Urban planners should invest in training programmes for staff on big data analytics to enhance their ability to utilise these tools effectively. Government agencies must also collaborate with private sector partners to ensure comprehensive coverage of all areas within the city. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.