Smart Spatial Planning in Egypt: An Algorithm-Driven Approach to Public Service Evaluation in Qena City

TL;DR

This study introduces a novel, algorithm-driven spatial planning model tailored for Qena City in Egypt, utilizing Voronoi diagrams and Python to evaluate and improve public service coverage based on local characteristics.

Contribution

It develops a localized planning standards model and an automated spatial analysis algorithm, addressing the gap between national standards and local urban needs.

Findings

Average service coverage is 81.3%.

Ambulance stations have 99.8% efficiency.

Parks and open spaces have only 10% coverage.

Abstract

National planning standards for public services in Egypt often fail to align with unique local characteristics. Addressing this gap, this study develops a tailored planning model for Qena City. Using a hybrid methodology (descriptive, analytical, and experimental), the research utilizes Python programming to generate an intelligent spatial analysis algorithm based on Voronoi Diagrams. This approach creates city-specific planning criteria and evaluates the current coverage of public facilities. The primary contribution of this study is the successful derivation of a localized planning standards model and the deployment of an automated algorithm to assess service efficiency. Application of this model reveals a general service coverage average of 81.3%. Ambulance stations demonstrated the highest efficiency (99.8%) due to recent upgrades, while parks and open spaces recorded the lowest coverage (10%) caused by limited land availability. Spatial analysis indicates a high service density in midtown (>45 services/km^2), which diminishes significantly towards the outskirts (<5 services/km^2). Consequently, the Hajer Qena district contains the highest volume of unserved areas, while the First District (Qesm 1) exhibits the highest level of service coverage. This model offers a replicable framework for data-driven urban planning in Egyptian cities.