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The review evaluates the impact of big data analytics on geospatial decision-making in smart cities by integrating various data sources and methods.

June 26, 2026Open Access

Big Data Analytics for Geospatial Decision-Making in Smart Cities: A Review of Spatial Data, GeoAI and Urban Digital Twins

Key Points

The review evaluates the impact of big data analytics on geospatial decision-making in smart cities by integrating various data sources and methods.
Conducted a structured narrative review to analyze geospatial decision-making processes.
Explored the integration of spatial data, GeoAI, and urban digital twins in existing literature.
Addressed the technological and conceptual gaps within GIScience and smart city research.
Identified integration challenges across GIS, urban analytics, and decision frameworks in smart cities.
Highlighted the importance of interpretable and context-sensitive decision-making supported by urban digital twins.
Concluded that effective geoinformation systems require more than just data abundance; they need to facilitate actionable urban insights.

Abstract

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions.

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