Spatial Data Analysis
Learning Outcomes:
- Explain the fundamental concepts of spatial data, coordinate reference systems, and spatial data types used in geographic information analysis.
- Apply spatial analysis techniques including point pattern analysis, spatial interpolation, and overlay operations using Python-based geospatial tools.
- Analyze spatial patterns and relationships in real-world datasets to derive actionable geographic insights through Exploratory Spatial Data Analysis (ESDA).
Topics:
- Spatial Data Types: Vector and Raster
- Coordinate Reference Systems and Map Projections
- Spatial Data Wrangling with Geo-Pandas
- Exploratory Spatial Data Analysis (ESDA)
- Point Pattern Analysis
- Spatial Autocorrelation: Moran’s I and LISA
- Spatial Interpolation: IDW and Kriging
- Overlay Analysis and Spatial Joins
- Spatial Clustering and Hotspot Analysis
- Raster Analysis and Terrain Modeling
Published at :
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