Training Course on GIS for Decision Support Systems - GIS Software

Module 1: Introduction to Decision Support Systems (DSS) and GIS

Theory:

Definition and role of DSS in policy, planning, and management.

Evolution from traditional DSS to spatially enabled DSS.

Architecture of DSS (data layer, model layer, interface layer).

GIS Relevance: GIS as a core enabler of DSS—spatial data visualization, querying, and analytics.

Applications: Urban planning, disaster response, business logistics, and environmental management.

Case Study: GIS-based DSS for flood evacuation planning.

Exercise: Explore a sample DSS architecture and identify where GIS components fit.

Module 2: Data Sources, Collection, and Management for DSS

Theory:

Types of data: spatial (raster/vector), non-spatial (socio-economic), and real-time streams (sensors, IoT).

Importance of metadata, standards, and data interoperability.

GIS Tools & Methods:

ArcGIS Data Management tools, QGIS DB Manager, PostGIS integration.

ETL (Extract, Transform, Load) workflows.

Applications: Multi-source data integration for transportation planning or healthcare allocation.

Case Study: Integrating satellite imagery, census data, and OpenStreetMap for urban DSS.

Exercise: Import, clean, and organize datasets into a geodatabase for later use in DSS modeling.

Module 3: Spatial Analysis Fundamentals for DSS

Theory:

Core techniques: buffering, overlay, spatial joins, and clustering.

Multi-Criteria Decision Analysis (MCDA) as a foundation of GIS DSS.

GIS Tools: ArcGIS Spatial Analyst, QGIS Processing Toolbox.

Applications: Site selection for facilities (schools, hospitals, warehouses).

Case Study: Using suitability analysis for renewable energy site planning.

Exercise: Perform a weighted overlay analysis to identify the best location for a new hospital.

Module 4: Modeling and Simulation in GIS-DSS

Theory:

Spatial modeling for predicting scenarios.

What-if simulations for risk analysis and policy decisions.

Tools: ArcGIS ModelBuilder, Python scripting, QGIS Graphical Modeler.

Applications: Land-use change modeling, transportation demand forecasting.

Case Study: Modeling urban sprawl over a 20-year horizon.

Exercise: Build a model to simulate population growth impacts on land use.

Module 5: Decision Support for Urban and Regional Planning

Theory:

GIS role in master planning, zoning, and public service delivery.

Spatial equity analysis for urban services.

Applications: Smart cities, transportation planning, green infrastructure.

Tools: ArcGIS Urban, QGIS Urban Planning Plugins.

Case Study: DSS for optimizing public transport routes.

Exercise: Use GIS to identify underserved areas in a city for new service delivery points.

Module 6: Environmental and Natural Resource Decision Support

Theory:

GIS in environmental conservation, land-use planning, and EIA (Environmental Impact Assessment).

Ecosystem services mapping and natural capital accounting.

Applications: Watershed management, deforestation monitoring, wildlife corridor planning.

Tools: ArcGIS Pro Environmental Extensions, QGIS GRASS GIS.

Case Study: DSS for prioritizing forest areas for conservation.

Exercise: Create a GIS-based conservation priority map using biodiversity and land cover data.

Module 7: Risk, Disaster, and Emergency Management DSS

Theory:

Risk and vulnerability mapping.

Real-time GIS for disaster early warning and response.

Crisis simulation and decision-tree modeling.

Applications: Flood zoning, wildfire evacuation planning, humanitarian logistics.

Tools: ArcGIS Disaster Response Solutions, QGIS InaSAFE plugin.

Case Study: DSS for cyclone evacuation route planning in coastal regions.

Exercise: Develop a hazard risk map and propose optimal shelter locations.

Module 8: Visualization and Dashboard Development for Decision-Making

Theory:

Principles of effective cartographic communication in DSS.

Turning raw data into actionable insights.

Tools: ArcGIS Dashboards, ArcGIS Experience Builder, QGIS Web Mapping Plugins, Tableau/Power BI integration.

Applications: Real-time monitoring dashboards for public health, transportation, or crisis management.

Case Study: COVID-19 response dashboards used globally.

Exercise: Build an interactive dashboard that shows key indicators for disaster preparedness.

Module 9: Integration of GIS with Other Decision Support Tools

Theory:

Linking GIS with statistical, predictive, and optimization models.

Cloud-based DSS integration for scalability and collaboration.

Tools: Python (GeoPandas, scikit-learn), R integration with GIS, PostGIS, Google Earth Engine.

Applications: Business intelligence, predictive policing, climate adaptation planning.

Case Study: GIS + AI integration for crop yield prediction in agriculture.

Exercise: Connect a GIS dataset to Power BI and create a combined geospatial BI report.

Module 10: Capstone Project and Case Studies

Theory: Review of workflows and DSS best practices.

Practical Focus: Students work in groups to design and present a GIS-based DSS application.

Project Examples:

Flood risk DSS with real-time updates.

Urban mobility DSS for optimizing bus stops and routes.

Environmental DSS for prioritizing renewable energy sites.

Deliverable: Final presentation with maps, dashboards, and recommendations for decision-makers.