August/September Flashcards
(35 cards)
Vector Data
Geographic features represented using points, lines and polygons
Raster Data
Geographic features represented using a grid of cells (pixels)
What is vector data best suited for?
Discrete features, a need for precision (boundaries and linear features)
What is raster data best suited for?
Continuous data, gradients, generally more complex data (satellite images, elevation maps)
Attribute Layers
Tabular data linked to spatial features
Base Maps
Provide background geographical context (e.g. street maps)
Why do we need projections?
- Flattening Earth’s spheroid surface
- Preserving spatial accuracy
- Creating maps that are usable for practical mapping applications
Conformal
Preserves shape but distorts area
Equal-Area
Preserves area but distorts shape
Equidistant
Maintains accurate distances from certain points
Azimuthal
Preserves direction from a central point
What are the types of distortion?
Shape, area, distance and direction
Mercator Projection
Common for navigation, maintains straight lines for compass bearings
UTM
Divides the world into zones, used for regional mapping
Robinson Projection
Balances shape and size distortion, used for world maps
Datum Transformation
Necessary when combining datasets with different datums, involves reprojecting data
What are the key aspects of data quality?
Accuracy, precision, completeness, timeliness, and consistency
Digitizing
The process of converting geographic features on a map into digital format
What are the types of digitizing?
Manual and automated
What are the main digitized features?
Points, lines and polygons
What are the steps of digitizing?
- Prepare the base map
- Select digitizing tool
- Trace features manually
- Assign attributes to digitized features
- Save and export
What are some challenges of digitizing?
- Inaccuracies via poor quality and resolution
- Georeferencing and tracing errors
- Topology errors (e.g. disconnected lines)
GIS Analysis Toolpack
Tools for spatial queries, data extraction, overlay analysis, proximity and statistical analysis
Overlay
Combine 2 layers to find spatial relationships
- Understanding the relationship between geographic attributes