Urban Sustainability Indexing and GIS Mapping: A Reproducible Framework and Intertemporal Comparability (Kazakhstan, 2000–2022)

Abstract

This study develops a reproducible GIS-based framework for assessing urban sustainability and applies it to Kazakhstan’s cities over the period 2000–2022. A composite Urban Sustainability Index (USI) is constructed from 29 objective indicators grouped into five thematic sub-indices: economic development, urban infrastructure, demography, social infrastructure, and environmental sustainability. The methodology combines min–max normalization, expert-based weighting, and standardized aggregation, ensuring intertemporal comparability and transparency of calculations. GIS is employed to visualize spatial differentiation and reveal territorial patterns of sustainability trajectories. A key methodological innovation of the study lies in ensuring long-term intertemporal consistency of the assessment while maintaining spatial interpretability under conditions of heterogeneous and partially constrained statistical data. Empirical results demonstrate moderate overall progress without convergence. A stable core of cities (Atyrau, Almaty, Astana, Pavlodar) consolidates its leading position but encounters an environmental ceiling that constrains further advancement. A transitional group exhibits incremental improvement limited by one or two structurally weak components, while a peripheral cluster remains locked in low sustainability due to a thin economic base and insufficient social provision. The findings confirm that isolated improvements in infrastructure or economic performance do not translate into higher sustainability outcomes without balanced development across all dimensions. Methodologically, the paper contributes a scalable and reproducible architecture for urban sustainability assessment that is suitable for integration into GIS-based monitoring systems and SDG 11 dashboards. The proposed framework is transferable to other national contexts with comparable data availability and provides a foundation for future extensions incorporating Earth observation data, subjective well-being indicators, and spatial–causal analysis.