Training on Visualization and Reporting of Spatial Big Data

Module 1: Introduction to Spatial Big Data Visualization

• Defining spatial big data and visualization challenges

• Principles of geospatial visual analytics

• Overview of visualization platforms (desktop, web, cloud)

• Case study: Visualizing global climate datasets

Module 2: Data Preprocessing and Preparation for Visualization

• Cleaning and transforming big spatial datasets

• Data aggregation and generalization techniques

• Handling multi-source datasets (satellite, sensor, IoT)

• Case study: Preprocessing datasets for urban population density mapping

Module 3: Mapping and Geovisualization Techniques

• Cartographic principles for big data

• Static vs. dynamic map visualizations

• Advanced thematic mapping (heatmaps, choropleths, cluster mapping)

• Case study: Agricultural yield mapping

Module 4: Cloud-Based Visualization Platforms

• Google Earth Engine for big data visualization

• ArcGIS Online and cloud-based geospatial dashboards

• Real-time visualization using sensor data

• Case study: Disaster early warning dashboards

Module 5: Interactive Dashboards for Spatial Data

• Building dashboards with Power BI and Tableau

• Linking spatial layers with non-spatial datasets

• Publishing and sharing interactive products

• Case study: M&E dashboards for development projects

Module 6: Visualization with Python and R

• Python libraries: matplotlib, geopandas, plotly, kepler.gl

• R packages: ggplot2, leaflet, shiny apps for interactive visualization

• Automating visualization workflows with scripts

• Case study: Time-series visualization of climate data

Module 7: 3D and Advanced Visualization Techniques

• 3D geovisualization with ArcGIS Pro and QGIS plugins

• Terrain models and 3D city models

• Virtual reality (VR) and augmented reality (AR) in geospatial visualization

• Case study: Smart city visualization for urban planning

Module 8: Storytelling with Spatial Big Data

• Principles of data storytelling and narrative building

• Communicating results to non-technical audiences

• Designing reports with maps, infographics, and visuals

• Case study: Reporting on deforestation trends

Module 9: Sector-Specific Applications of Visualization

• Agriculture: crop monitoring dashboards

• Urban: infrastructure planning maps

• Climate: visualizing emissions and temperature anomalies

• Disaster risk management: hazard exposure mapping

• Case study: Climate resilience reporting

Module 10: Reporting, Ethics, and Future Trends

• Structuring geospatial reports for decision-makers

• Open data, ethics, and responsible visualization

• Future of big data visualization: AI and machine learning-driven visuals

• Group project: Designing a visualization and reporting workflow for a sector-specific case (urban, agriculture, climate, or disaster)