Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1843–1850
Automated Spatial Data Processing Pipeline and GIS Decision-Support System for Large-Scale Urban Morphology Analysis
Oydin Shoumarova, Anna Vetlugina and Sabina Alieva
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Abstract
Rapid urban growth requires advanced computational tools and automated spatial information systems for processing large-scale geospatial data. This paper presents an automated GIS-based data processing pipeline and architectural framework for a Spatial Decision-Support System (SDSS) designed to compute, classify, and visualize complex spatial morphology indicators. Utilizing open-source geospatial data from OpenStreetMap, the developed system automates the extraction, geometric correction, and processing of vector data. It implements an algorithmic workflow based on the Spacematrix methodology to calculate multi-dimensional spatial indicators, including Floor Space Index (FSI), Ground Space Index (GSI), average building height (L), and Open Space Ratio (OSR). The core contribution of this study is a reproducible, data-driven ETL (Extract, Transform, Load) and spatial classification pipeline that handles geometric anomalies and executes data-driven zoning. The system was validated using a dataset of 464 spatial analysis units, automatically classifying them into four distinct morphological typologies. The results demonstrate the efficiency of the developed GIS pipeline as a scalable, morphology-sensitive component for Smart City digital twins and urban information environments.
Keywords
Spatial Decision-Support Systems Automated Data Pipeline Smart City Infrastructure GIS Algorithms Open Geospatial Data Spatial Data Science
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