Analysis and predictions of land use/land coverage changes with the CA-Markov model

Using geospatial analysis and predictive modeling to better understand how urban growth may shape the future of land use in the City of Guelph

Tirdad Naeimi, SMART Centre

About the project

This research examined land use and land cover changes in the City of Guelph between 2001 and 2022 and used predictive modeling to forecast future trends through 2037. The project explored how urban growth influences the distribution of developed, natural, and other land types over time.

The challenge

As communities grow, changes in land use can have significant implications for infrastructure planning, environmental sustainability, and resource management. The project sought to better understand historical patterns and provide data-driven insights into potential future development scenarios.

Approach

• Analyzed satellite imagery and geospatial datasets spanning more than two decades
• Examined historical land use and land cover changes across the City of Guelph
• Applied a Cellular Automata-Markov (CA-Markov) model to forecast future trends
• Evaluated patterns of urban expansion and land use transformation
• Assessed implications for long-term planning and sustainability

Results

• Identified significant land use changes between 2001 and 2022
• Forecasted future land use and land cover patterns through 2037
• Demonstrated the potential for continued urban expansion in the region
• Generated evidence-based insights to support planning and policy discussions
• Established a framework for future geospatial and predictive modeling research

Why it matters

This research provides valuable information for organizations involved in land use planning, environmental management, and community development. The findings help:

• Support informed urban planning and growth management decisions
• Improve understanding of long-term land use trends
• Identify opportunities to balance development and environmental stewardship
• Strengthen evidence-based policy development
• Advance the use of predictive modeling in sustainability planning

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