Urban Growth Analysis
Project Overview
This project involved analyzing urban expansion patterns in a rapidly growing metropolitan area using remote sensing data and spatial statistics. The goal was to identify growth trends and predict future development patterns to assist in urban planning decisions.
Methodology
I collected Landsat satellite imagery spanning 20 years and performed supervised classification to identify urban areas across different time periods. Using QGIS and spatial analysis tools, I calculated urban growth rates, directional trends, and identified factors influencing development patterns.
Key Challenges
One of the main challenges was distinguishing between different types of urban development (residential, commercial, industrial) using medium-resolution satellite imagery. I addressed this by incorporating additional data sources and using advanced classification techniques.
Results and Impact
The analysis revealed that urban growth was occurring primarily along transportation corridors and was strongly influenced by topography and existing infrastructure. The findings were used by local planning authorities to update their comprehensive plan and guide future infrastructure investments.
Project Gallery
Landsat imagery classification showing urban areas across different time periods
Multiple GIS data layers used in the analysis
Supervised classification workflow in QGIS
Map showing urban expansion from 2000 to 2020
Chart showing annual urban growth rates by district
Analysis of directional trends in urban expansion
Predicted urban growth patterns for 2030
Methodology
Landsat imagery classification showing urban areas across different time periods
Multiple GIS data layers used in the analysis
Supervised classification workflow in QGIS
Results
Map showing urban expansion from 2000 to 2020
Chart showing annual urban growth rates by district
Analysis of directional trends in urban expansion
Predicted urban growth patterns for 2030