Geospatial Information Systems and Geospatial Data Modelling
Summary
Geospatial Information Systems (GIS) provide the framework for capturing, storing, analysing and visualising spatially referenced data to reveal patterns, relationships and trends in geographic phenomena. Central to GIS are two complementary data models: the raster model represents continuous fields by a grid of regularly spaced cells, each storing a value such as elevation or spectral reflectance, while the vector model represents discrete objects—points, lines and polygons—through coordinate pairs and topology. Advances in data acquisition—from satellite and airborne sensors to crowdsourced and sensor-network feeds—have vastly expanded the volume and variety of geospatial data. Concurrently, scalable cloud-based architectures, interoperable web services and standardised data schemas now enable the dynamic integration of heterogeneous datasets into unified modelling environments. These developments underpin applications ranging from land-use suitability and ecosystem assessment to urban digital twins and real-time hazard mapping. By coupling robust data structures with modular processing pipelines, modern GIS supports reproducible analysis, adaptive modelling and evidence-based decision-making across environmental, infrastructural and socio-economic domains.
Research from Nature Portfolio
Recent studies have assembled a global database of 400 research watersheds to characterise dominant hydrologic flow pathways, linking aridity, biome and terrain controls on subsurface and lateral flows. This resource benchmarks process-based models and supports scalable simulations of land–atmosphere interactions within unified geospatial frameworks. In semi-arid ecosystems, a fuzzy-logic-enhanced Multi-Criteria Decision Analysis integrated with GIS and geostatistical interpolation revealed that roughly 30 % of a 195 000 km² region is highly suitable for cultivation under data uncertainty, demonstrating the sensitivity of fuzzy-AHP weighting to terrain and soil criteria. Foundational work employing very high-resolution soil, topographic and climatic data classified national land into six suitability categories, exposing extensive cultivation on marginal lands and prompting proposals for cropland redistribution to improve sustainability.
Research from all publishers
Studies of web architectures for environmental Big Data have described OGC-compliant Web Processing Services, NoSQL storage and API-driven workflows that ingest, process and visualise heterogeneous geospatial streams in distributed, scalable environments. These frameworks support on-demand spatial queries, reproducible modelling and rapid integration of new data sources into operational GIS toolchains. Complementing these advances, the Observations Data Model Version 2 defines standardised schemas for time, location, provenance and methodology across sensor and specimen-derived datasets, enhancing interoperability and enabling seamless cross-discipline synthesis. A critical review of digital visualisation technologies highlights the role of virtual reality, augmented reality and digital twins—when linked to real-time sensor networks—in urban flood risk management, demonstrating how immersive platforms improve preparedness, early warning and stakeholder engagement in hazard scenarios.
Geospatial Information Systems and Geospatial Data Modelling publication trend
The graph below shows the total number of articles in geospatial information systems and geospatial data modelling across all publications each year (not limited to Nature Index journals).
Technical terms
Geographic Information System (GIS): A digital framework for capturing, storing, analysing and visualising spatially referenced data.
Raster data model: A representation of continuous fields as a grid of regularly spaced cells, each storing a numerical or categorical value.
Vector data model: A representation of discrete geographic features (points, lines, polygons) via coordinate pairs and topology.
Multi-Criteria Decision Analysis (MCDA): A set of methods for evaluating alternatives based on multiple criteria, often combining quantitative and qualitative factors.
Fuzzy logic: An approach allowing criteria to have degrees of membership, accommodating uncertainty and imprecision in decision-weighting.
OGC Web Processing Service (WPS): A standardised web service interface for executing spatial data processing operations over the Internet.
Observations Data Model Version 2 (ODM2): A community information model specifying standard structures for storing and sharing spatially discrete Earth observations.
Digital twin: A virtual replica of a physical system (e.g. an urban district) that is continuously updated with real-time data to support simulation and decision-making.
References
- Observations Data Model 2: A community information model for spatially discrete Earth observations. Environmental Modelling & Software (2016).
- Web technologies for environmental Big Data. Environmental Modelling & Software (2015).
- A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management. Sustainable Cities and Society (2023).
- Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem. Scientific Reports (2020).
- Iran’s Land Suitability for Agriculture. Scientific Reports (2017).
- Global patterns in observed hydrologic processes. Nature Water (2025).
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