1. Raster Data Models: Data Compression Why? –Save disk space by reducing information content –Methods Run-length codes Raster chain codes Block codes Quadtrees

2. Raster Data Compression Models: Run-length Encoding From An Introduction to Geographic Information Systems, Heywood et al. (2002) Reduces data volume on a row-by-row basis by indicating string lengths for various values

3. Raster Data Compression Models Quadtrees: Recursively divide an area into quadrants until all the quadrants (at all levels) are homogeneous 1 1 3 3 2 1 1 3 2 2 3 2 2 33 2 NW NE 1 2 SW 3 SE 22 33

4. Comparison between Raster and Vector Data Structure Real World FeaturesRasterVector A Raster model tells what occurs everywhere, while a vector model tells where everything occurs.

5. Which Model to Choose: Raster or Vector? It depends on: spatial data characteristics: Vector model: linear features such as rivers, roads, political boundaries Raster model: temperature, elevation the software available: less restrictive now than a few years before. Most software handles both vector and raster data the training of the key individuals historical precedent many big GIS projects are continuous works for many years, and many new works depend on what’s have been done and what’s been collected

6. Advantages and Disadvantages Advantages of Vector Model: Allows accurate measurement of areas, distance, and geographic representation less demanding for data storage Allows topology, thus facilitating error detection in the database Disadvantages of Vector Model: Data acquisition and input are time consuming Not suitable for continuous Geographic phenomena Computationally intensive for some spatial analysis

7. Advantages and Disadvantages Advantages of Raster Model: Simple and straight forward data structure Suitable for continuous geographic phenomena Computationally efficient for spatial analysis Disadvantages of Raster Model: Data redundancy for homogeneous areas, thus large data volume Limited accuracy of location, thus corresponding areas, distance

8. Vectorization and Rasterization Vectorization: conversion of raster data to vector format Rasterization: conversion of vector data to raster format Data conversion are required in many applications due to multiple layers of data may come from different sources that do not meet the application needs.

9. Vectorization Vector data are often preferred because of the following: smaller file size, and thus easier handling by computers for flexible to adjust to different scale more efficient to handle topology e.g. common edge can be easily define base on left and right side polygons Vector data format is often preferred in most GIS software and projects

10. Vectorization Points & polys - relatively simple points: if cell=value, then a vector point is created at cell centroid with attribute=value polygons: polygon with attribute=value is created for all adjoining cells=value; poly boundary follows exterior of cells Lines - more complex must somehow determine: start/end/intersection points (nodes) for lines shape points along lines (vertices) topological relationships

11. Vectorization Starting point: Raster representation of vector world

12. Vectoriation Steps Step 1 Line thinning Line thinning: thin multicell linear features to one cell width

13. Vectoriation Steps Step 2 Line extraction Line extraction: determine locations of line begin/end intersections -- generate nodes determine line shape points -- generate vertices

14. Vectoriation Steps X-axis Y-axis raster origin (2,2) (x0,y0) Step 3: assign x-y coordinate to each nodes and vertex x3=X0+2*cellsize y2=y0-2*cellsize Real world origin (0,0)

15. Vectoriation Steps Step 4: construct topology Determine how to connect the nodes and vertices

16. Information Loss Lost information

17. Rasterization Simpler compare to vectorization. Important Issues: cell size cell values

18. Cell size: Smaller cell size, better representation of vector data, more data – fine spatial resolution Larger cell size, less data, but more information loss! -- coarse spatial resolution How to determine cell value cell center (centroid) majority weighting weighted values based on priority/importance Rasterization

19. value at centroid assigned to the cell simple, but can over-represent small area values

20. Rasterization value covering the majority of area assigned to cell simple; “fairer” representation than centroid approach

21. Rasterization analyst assigns priority weights based on importance of different values “most important” value present is assigned to cell assures presence of crucial geographic phenomena