Python GIS Projects for Practice
Geographic Information Systems (GIS) combined with Python’s powerful libraries create endless possibilities for spatial analysis, mapping, and geospatial problem-solving. Whether you’re a beginner looking to enter the field or an experienced developer wanting to expand your spatial analysis skills, these Python GIS projects will help you build a solid foundation and impressive portfolio.
Essential Python GIS Libraries
Before diving into projects, familiarize yourself with these core libraries:
- GeoPandas: Extends pandas for geospatial data manipulation
- Folium: Creates interactive web maps
- Shapely: Geometric operations and spatial analysis
- Rasterio: Reads and writes raster datasets
- Pyproj: Cartographic projections and coordinate transformations
- Contextily: Adds basemaps to matplotlib plots
- OSMnx: Downloads and analyzes street networks from OpenStreetMap
Beginner Projects (Getting Started)
1. Interactive Choropleth Map
Skills: Data visualization, web mapping, statistical analysis Libraries: Folium, GeoPandas, Pandas
Create an interactive choropleth map showing population density, income levels, or COVID-19 cases by state or county. This project teaches basic geospatial data handling and web map creation.
Key Learning Outcomes:
- Loading and manipulating shapefiles
- Joining attribute data to geographic boundaries
- Creating color-coded maps based on data values
- Adding interactive features like tooltips and popups
Data Sources:
2. GPS Track Analyzer
Skills: Point data analysis, route visualization, distance calculations Libraries: GeoPandas, Matplotlib, Shapely
Analyze GPS tracking data from fitness apps or GPS devices. Calculate distances, speeds, elevation changes, and create visualizations of routes.
Key Learning Outcomes:
- Working with point geometries
- Calculating distances and bearing between points
- Creating line geometries from point sequences
- Basic spatial statistics
Data Sources:
- Export your own GPS data from Strava, Garmin Connect, or similar apps
- OpenStreetMap GPS traces
3. Weather Station Map
Skills: API integration, point mapping, data visualization Libraries: Folium, Requests, GeoPandas
Create an interactive map showing current weather conditions from multiple weather stations. Include temperature, precipitation, and wind data with custom markers.
Key Learning Outcomes:
- Integrating real-time data via APIs
- Creating custom map markers
- Handling JSON data and coordinate systems
- Real-time data visualization
APIs:
Intermediate Projects (Building Skills)
4. Urban Heat Island Analysis
Skills: Raster analysis, satellite imagery, temperature modeling Libraries: Rasterio, GeoPandas, Matplotlib, Scikit-learn
Analyze satellite thermal imagery to identify urban heat islands and correlate them with land use patterns, vegetation cover, and urban density.
Key Learning Outcomes:
- Processing satellite imagery
- Raster calculations and analysis
- Statistical correlation analysis
- Heat map visualization techniques
Data Sources:
5. Network Analysis and Routing
Skills: Graph theory, shortest path algorithms, network analysis Libraries: OSMnx, NetworkX, GeoPandas
Download street network data and perform routing analysis. Find optimal routes, calculate accessibility measures, and analyze network connectivity.
Key Learning Outcomes:
- Graph-based spatial analysis
- Shortest path algorithms (Dijkstra’s algorithm)
- Network centrality measures
- Isochrone analysis (travel time polygons)
Extensions:
- Emergency service coverage analysis
- Public transit accessibility studies
- Delivery route optimization
6. Land Cover Classification
Skills: Machine learning, remote sensing, raster classification Libraries: Rasterio, Scikit-learn, Matplotlib, NumPy
Use satellite imagery to classify different land cover types (forest, urban, water, agriculture) using supervised machine learning algorithms.
Key Learning Outcomes:
- Remote sensing principles
- Feature extraction from imagery
- Supervised classification techniques
- Accuracy assessment and validation
Data Sources:
Advanced Projects (Professional Level)
7. Wildfire Risk Assessment Model
Skills: Risk modeling, multi-criteria analysis, predictive modeling Libraries: GeoPandas, Rasterio, Scikit-learn, SciPy
Develop a comprehensive wildfire risk assessment model incorporating terrain, vegetation, climate data, and historical fire patterns.
Key Learning Outcomes:
- Multi-criteria decision analysis
- Weighted overlay analysis
- Risk mapping and visualization
- Model validation and sensitivity analysis
Data Integration:
- Digital elevation models (USGS)
- Vegetation indices (MODIS)
- Climate data (NOAA)
- Historical fire perimeters (NIFC)
8. Real Estate Price Prediction with Spatial Features
Skills: Spatial econometrics, machine learning, market analysis Libraries: GeoPandas, Scikit-learn, Matplotlib, Seaborn
Create a machine learning model that predicts real estate prices using both traditional features and spatial variables like proximity to amenities, schools, and transportation.
Key Learning Outcomes:
- Spatial feature engineering
- Distance-based analysis
- Hedonic price modeling
- Cross-validation with spatial data
Spatial Features to Include:
- Distance to public transportation
- School district boundaries
- Crime incident density
- Walkability scores
9. Pandemic Spread Simulation
Skills: Spatial epidemiology, agent-based modeling, simulation Libraries: Mesa, GeoPandas, Matplotlib, NetworkX
Build an agent-based model to simulate disease spread across a geographic area, incorporating population density, mobility patterns, and intervention strategies.
Key Learning Outcomes:
- Agent-based modeling principles
- Spatial interaction modeling
- Monte Carlo simulation
- Policy scenario analysis
Project Implementation Tips
Setting Up Your Environment
# Essential packages installation
pip install geopandas folium rasterio shapely pyproj contextily osmnx
pip install matplotlib seaborn scikit-learn jupyter
Best Practices
- Data Management: Always check coordinate reference systems (CRS) and ensure consistency
- Performance: Use spatial indexing for large datasets
- Visualization: Choose appropriate color schemes and ensure accessibility
- Documentation: Document data sources, processing steps, and assumptions
- Version Control: Use Git to track your project development
Common Challenges and Solutions
Challenge: Coordinate Reference System (CRS) mismatches Solution: Always check and transform CRS using gdf.to_crs()
Challenge: Large file processing Solution: Use chunking, spatial indexing, or cloud-based solutions
Challenge: Web deployment Solution: Use Streamlit, Dash, or Flask for interactive web applications
Building Your Portfolio
Documentation Standards
- Include detailed README files
- Provide data source citations
- Document methodology and assumptions
- Include requirements.txt for dependencies
Presentation Tips
- Create compelling visualizations
- Write clear, concise explanations
- Include interactive elements when possible
- Show both code and results
Sharing Your Work
- GitHub repositories with organized code
- Jupyter notebooks with narrative explanations
- Interactive web applications
- Blog posts explaining your approach
Additional Resources and Learning Materials
Online Courses
- Coursera GIS Specialization
- Automating GIS Processes (University of Helsinki)
- Introduction to Python GIS (Penn State)
Books
- “Python Geospatial Development” by Erik Westra
- “Learning Geospatial Analysis with Python” by Joel Lawhead
- “Geoprocessing with Python” by Chris Garrard
Communities and Forums
Data Sources
- OpenStreetMap – Global map data
- USGS Earth Explorer – Satellite imagery and elevation data
- Natural Earth – Country and administrative boundaries
- Global Administrative Areas – Administrative boundaries worldwide
- WorldPop – Population density data
Conclusion
Python GIS projects offer an excellent pathway to develop both programming and spatial analysis skills. Start with simpler visualization projects to build confidence, then gradually tackle more complex analysis and modeling challenges. Each project in this guide builds upon previous skills while introducing new concepts and techniques.
Remember that the key to success is consistent practice and gradually increasing complexity. Don’t hesitate to combine multiple techniques within a single project, and always consider the real-world applications and implications of your analysis.
The intersection of Python programming and GIS opens doors to careers in urban planning, environmental science, public health, transportation, and many other fields where spatial thinking and data analysis are crucial. Start with projects that interest you personally, as motivation is key to pushing through the learning curve and building impressive portfolio pieces.
Whether you’re analyzing urban development patterns, tracking environmental changes, or solving logistics problems, Python GIS provides the tools to turn spatial questions into actionable insights.
