An overview of geospatial data, its importance, sources, and applications in various fields such as environmental science, urban planning, and disaster management.
Introduction to Geospatial Data Analysis
Key concepts, workflow, tools, and best practices
| Aspect | Description |
|---|---|
| Definition | Information that is tied to a location on the Earth’s surface (latitude, longitude, altitude, or a geographic identifier). |
| Types | • Vector – points, lines, polygons (e.g., city locations, road networks, land parcels). • Raster – grid cells/pixels (e.g., satellite imagery, digital elevation models). |
| Attributes | Non‑spatial data attached to each feature (population, land‑use class, temperature, etc.). |
| Coordinate Reference Systems (CRS) | Framework that defines how coordinates map to real‑world locations (e.g., WGS 84, NAD83, UTM zones). |
| Concept | Why It Matters |
|---|---|
| Spatial Resolution | Determines the level of detail (e.g., 30 m Landsat vs. 10 m Sentinel‑2). |
| Spatial Extent | Geographic area covered by a dataset; influences processing time and storage. |
| Projection & Datum | Misaligned projections cause distortion; always re‑project to a common CRS before analysis. |
| Topology | Relationships among features (adjacency, connectivity, containment) used in network analysis and error checking. |
| Spatial Autocorrelation | “Near things are more alike than distant things.” Basis for many statistical models (Moran’s I, Getis‑Ord G*). |
| Scale | The level of geographic detail; results can change dramatically when moving from local to regional scales. |
Define the Question
Data Acquisition
Data Preparation
Exploratory Spatial Data Analysis (ESDA)
Analysis Techniques
Validation & Accuracy Assessment