Geospatial clustering is an analytical approach used to group locations based on their proximity on Earth’s surface. This project focuses on applying unsupervised machine learning techniques to latitude and longitude data to identify meaningful geographic patterns, enabling smarter location-based decisions.
Geospatial clustering is widely used across industries for tasks like:
- Logistics: Defining delivery zones and optimizing routes.
- Retail: Identifying dense customer areas for store placement.
- Urban Planning: Detecting high-demand zones for public transport.
- Crime Analysis: Locating crime hotspots for targeted policing.
These applications share two core needs:
- Analyzing location-based data.
- Revealing natural geographic groupings to drive strategy.
- Perform geospatial clustering on delivery pickup and drop-off locations.
- Visualize delivery density and geographic distribution.
- Identify key delivery zones for optimization.
- Detect and manage outliers caused by GPS or data entry errors.
- Python: Core programming language for data analysis.
- Libraries: Pandas, NumPy, Geopy for distance calculations, Plotly for visualization, and Scikit-learn for clustering.
- Algorithm: K-Means clustering for identifying delivery zones.
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Visual analysis revealed concentrated delivery activity in southern and central India.
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K-Means clustering grouped delivery points into meaningful zones:
- Central Delivery Zone: Maharashtra, Madhya Pradesh.
- Southern Delivery Zone: Tamil Nadu, Karnataka.
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Outlier clusters represented invalid geographic data, which was filtered out.
By identifying valid delivery zones and removing GPS errors, the project provides:
- Clear delivery zones for optimized route planning.
- Data-driven insights for resource allocation.
- A scalable methodology for expanding into new geographic regions.
- Geospatial cluster analysis and insights.
- Delivery zone mapping for logistics teams.
- Recommendations for improving service reach and efficiency.
This project demonstrates how unsupervised learning applied to latitude and longitude data can uncover valuable business insights. By visualizing and segmenting geographic patterns, organizations can make smarter, location-based decisions, ultimately improving efficiency and strategy.