1. Spatial data formats and their characteristics.
2. Spatial data structures (e.g., vector, raster, TIN).
3. Spatial data models and database management.
4. Data conversion and interoperability.
1. Network analysis (e.g., routing, service areas, accessibility).
2. Terrain analysis (e.g., slope, aspect, hillshade, hydrological modeling).
3. Spatial modeling and decision support.
4. Spatial statistics and spatial data mining.
1. Introduction to remote sensing data sources (e.g., satellite imagery, aerial photography, LiDAR).
2. Remote sensing data preprocessing (e.g., geometric correction, atmospheric correction, pan sharpening).
3. Integration of remote sensing data with GIS.
4. Remote sensing applications in environmental monitoring, land use/land cover analysis, and natural resource management.
1. Introduction to GIS programming languages (e.g., Python, R, JavaScript).
2. Developing custom GIS tools and models.
3. Automating GIS workflows and tasks.
4. Web-based GIS applications and APIs.
1. Cartographic principles and design.
2. Thematic mapping techniques.
3. Web-based GIS and interactive maps
4. Communicating spatial information effectively
1. Real-world GIS and remote sensing case studies.
2. Integrating concepts from the course in a final project.