Spatial Data Representation and Analysis
Overview of Spatial Data Representation
Landscape representation can be approached in two primary ways: continuous fields and discrete objects.
Continuous Fields vs Discrete Objects
Continuous field model:
Example: Elevation data representing changes in terrain.
Uses pixels to approximate a continuous surface.
Discrete object model:
Involves creating databases consisting of points, lines, and polygons (e.g., locations of buildings, trees).
Suitable for representing distinct entities.
Spatial Data Storage Methods
Two main methods for storing spatial data:
Continuous field models (raster databases)
Discrete object models (vector databases)
Continuous Surface Models
Create continuous surface models (e.g., elevation) using:
Mathematical equations valid at every point within a study area.
Simplification via square pixels for practical storage.
Pixel Resolution and Size
Definition of pixel resolution:
Represents the size of an area in real-world measurements (e.g., meters).
Determines the level of detail and spatial accuracy.
Implicitly stores both spatial and attribute data:
Each grid cell represents a spatial extent and contains specific data values.
Grid Representation in Raster Models
Grids consist of pixels/cells:
Each pixel has an associated size that corresponds to real-world distance.
The data is recorded directly in the cells, rather than using external tables for attributes.
Spatial Location Calculation
Based on one known corner of a pixel and its size:
Offers a simple method to determine coordinates using basic mathematics.
Coordinate calculation examples:
For a pixel set at a resolution of 1 meter, spatial location can be calculated from pixel position.
Challenges with Raster Data
Possible issues when using raster data:
Complexity of continuous surfaces makes data representation challenging.
Need for pixel size to be smaller than the object to retain detail.
Larger objects may result in pixelated representations.
Data Structures: Raster vs Vector
Raster:
Flexible for continuous data but inefficient for discrete objects.
Stores spatial information implicitly; higher data redundancy for large empty areas.
Computationally intensive when processing large databases.
Vector:
More suited for specific object representations (e.g., roads, rivers).
Explicit object data storage facilitates detailed analysis.
Efficient for datasets with fewer distinct objects.
Transformation Between Data Models
Converting between raster and vector data:
Complex shapes may result in loss of detail in raster representation.
The approximation process can lead to data inaccuracies unless pixel sizes are adequately small.
Raster Data Applications and Examples
Useful for various analyses, such as:
Terrain modeling and elevation analysis.
Combination of multiple raster datasets (multi-band) for enriched information (e.g., RGB color detection in cameras).
Satellite imagery and LiDAR data contribute to raster datasets.
Continuous data representation (elevation, surface features).
Retrieval of historical maps or photographs into raster formats for comparison over time.
Example of Continuous Data in Usage
Elevation model from LiDAR captures:
Spatially dense measurements converted into raster grids allow for detailed terrain analysis and visualization, such as slope and aspect calculations.
Summary and Future Learning
Continue to explore connections between raster and vector data structures in future weeks of the course.
Chapters 1 and 2 will support foundational knowledge in geometry, especially if prior courses were not taken.
Assessment through quizzes will incorporate material related to raster data modeling and its applications.