GIS Introduction to Raster Module PDF

Introduction to the Raster Data Model The Raster Data Model offers an alternative to the vector model by representing the world as a continuous field rather than discrete objects. This model is especially useful for phenomena that vary continuously, like temperature, elevation, or land cover. Key...

Introduction to the Raster Data Model The Raster Data Model offers an alternative to the vector model by representing the world as a continuous field rather than discrete objects. This model is especially useful for phenomena that vary continuously, like temperature, elevation, or land cover. Key Features of the Raster Data Model 1.​ Grid-Based Representation ○​ Space is divided into square grid cells (or lattice/tessellation). ○​ Each cell represents an attribute value for a specific spatial location. ○​ This differs from vector models, where geometry is associated with multiple or single points, lines, or polygons. 2.​ Spatial Resolution ○​ Spatial resolution refers to the size of each grid cell, measured as ground distance. ○​ Finer resolutions (smaller cell sizes) capture more detail but require more storage. ○​ Spatial extent, on the other hand, refers to the total area covered by the raster. 3.​ Attribute Storage ○​ Each raster layer can store only one variable. ○​ Rasters can store qualitative data (categorical, nominal, ordinal) or quantitative data (numerical). ○​ Examples: ​ Qualitative Raster: Land use types (e.g., forest, water). ​ Quantitative Raster: Elevation or temperature measurements. 4.​ Geo-referencing ○​ Raster cells are geo-referenced using the X, Y coordinate of the top-left cell. ○​ The spatial position of other cells is calculated using the raster's resolution, number of rows/columns, and cell order. Types of Raster Data 1.​ Binary Rasters: Only two values, typically 0 (absence) and 1 (presence). 2.​ Integer Rasters: Whole numbers representing categories (e.g., land cover types) or rounded quantitative data. 3.​ Floating Point Rasters: Continuous data with decimal precision (e.g., rainfall or temperature). 4.​ Character Rasters: Cells represented by strings or letters, though less common compared to integer rasters for qualitative data. Raster Data Structure  1.​ Header Information ○​ Includes the number of rows/columns, cell size (spatial resolution), and starting coordinates. ○​ May include optional information, like legends, for easier interpretation. 2.​ Cell Order and Storage ○​ Attributes are typically stored row-by-row, left-to-right, starting from the top-left corner. ○​ The scan order (how attributes are printed) may vary depending on the file format. 3.​ Storage Efficiency ○​ Unlike vectors, rasters store only one coordinate for the entire layer, making reconstruction faster and more storage-efficient. ○​ This is why "raster is faster" compared to vector data. Important Takeaways ​ Rasters are ideal for continuous phenomena and require separate layers for each attribute. ​ Spatial resolution determines detail, while extent refers to the total study area. ​ Raster data is computationally efficient due to its simple structure, storing one spatial coordinate and calculating others as needed. ​ The type of raster data influences its applications, ranging from simple presence/absence maps to detailed quantitative analyses. By understanding the fundamentals of raster data, you can effectively apply this model to GIS applications and compare it with vector-based approaches.