{"ID":2832257,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06431","arxiv_id":"2512.06431","title":"Smart Spatial Planning in Egypt: An Algorithm-Driven Approach to Public Service Evaluation in Qena City","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 (\u003e45 services/km^2), which diminishes significantly towards the outskirts (\u003c5 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.","short_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 a...","url_abs":"https://arxiv.org/abs/2512.06431","url_pdf":"https://arxiv.org/pdf/2512.06431v1","authors":"[\"Mohamed Shamroukh\",\"Mohamed Alkhuzamy Aziz\"]","published":"2025-12-06T13:36:57Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CY\"]","methods":"[]","has_code":false}