Decoding Gray Urban Spaces

Urban spaces are recognizably dynamic and multifaceted; they shape our behaviors, interactions, and overall quality of life. Among these, there exist underutilized areas often referred to as “gray spaces”. These neglected or invisible urban spaces present unexploited opportunities for development; however, they pose significant risks if not strategically identified and addressed. This paper proposes a matrix-based systematic approach using space syntax analysis that analyzes the relationship between spatial configurations and human behavior to detect and map gray spaces within a specific urban context. The method overlays the space syntax analysis with additional contextual and spatial characteristic layers to enrich the understanding of gray spaces and provide a more comprehensive picture of their potential for development in Al Darb El Ahmar, Cairo, Egypt as a case study. The research findings provide valuable insights to inform sustainable and resilient interventions.

Run the paper's methodology on any city in the world, directly in your browser. Draw a zone of study, fetch the street network from OpenStreetMap, run a depthmapX-equivalent angular segment analysis (choice & integration at pedestrian and vehicular scales), detect gray-space candidates, and decode them through the paper's contextual matrix — then export GeoJSON, CSV or a printable report. Everything runs client-side; no data leaves your device.

Maged, M., Ismail, A., Elsayed, E., & Mohareb, N. (2024). Decoding Gray Urban Spaces: A space syntax approach to detecting and identifying underutilized urban areas. Proceedings of ASCAAD 2024, 411–425.