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Understanding Raster and Vector Data Formats in GIS

Explore the differences between raster and vector data formats in GIS, their models, coding methods, and mapping techniques. Learn the advantages, disadvantages, and complexities of each format to make informed decisions for spatial data projects.

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Understanding Raster and Vector Data Formats in GIS

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  1. Chapter 3 Raster & Vector Data

  2. Introduction Spatial data in GIS has two primary data formats: Raster & Vector. Raster uses a grid cell structure, where as vector is more like a drawn map. Each data format has its advantages and disadvantages , and the GIS professional recognizes that many projects have need for both.

  3. Raster & Vector data Models • In the Rasterformat: • - a landscape scene is gridded, and each cell in the grid is given a single landscape identity, usually a number, text label. • The cell is the minimum mapping unit, it is the smallest size at which any landscape feature can be represented and shown • All the features in the cell area are reduced to a single identity which is the dominant feature. • Even where “ nothing “ exists ( no data) , the cells must be coded.

  4. In the Vector format: - vector implies the maplike drawing of features, with the generalizing effect of a raster grid minimized. - shape is better retained, much like an actual map. The lines are continuous & not broken into a grid structure. • In the Vectorformat: • - vectors are data elements describing position and direction. • - vector implies the map-like drawing of features, without the generalizing effect of a raster grid. • - The lines are analog which means they are not broken into cells or fragments, but continue from start to finish in a continuous manner. • - shape is better retained, much like an actual map. • Vector is much more spatially accurate than the raster format. • Where no data occurs, nothing is entered and “no data” or no wells are presumed. • Vector is much simpler and easier than raster for data visualization.

  5. Raster Data • RASTER CODING • In the data entry maps can be digitized at a selected cell size and each cell assigned a code or value. • The cell size can be adjusted according to the grid structure (100x200 cells ) or by ground units ( each cell = 30 meters), also termed resolution . • Resolutiondepends on the needs of the project • There are three basic and one advanced scheme for assigning cell codes: • Presence\Absence • Cell Center • Dominant Area • Percent Coverage

  6. Presence/Absence • Record a feature if some of it occurs in the cell space. • A practical way of coding point & line features, because they do not take up much area of cell • This method is complicated when two or more occur in the same cell space, or when the two features are of different types. • Cell Center • The method involves reading the center of the cell and assigning the code accordingly. • When there are multiple features in a cell area, the one in the center “ wins” the code. • It is not good for points or lines, because they do not necessarily pass through the exact center

  7. Dominant Area • A common method is to assign the cell code to the feature with the largest ( dominant) share of the cell. • This is suitable primarily for polygons, although line features could be assigned according to which one has the most linear distance in a cell. • Percent Coverage • A more advanced method is to separate each feature for coding into individual themes and then assign values that show its percent cover in each cell. • A quick addition of all cells gives the area coverage of each feature. • This is more difficult to work , but provides higher spatial accuracy • Useful for transitional cells, contains several features

  8. Coding Problems • Some raster code assignment programs used the dominant class, whereas others read the very center of the cell . Assigning attribute codes to each cell can be a problem when the cells cover several features or classes. Problems of coding are made more apparent in the greatly magnified views. • Raster systems generalize a landscape & yield spatial and classification inaccuracies. This might not be important for some purposes but it could be critical for others . One possible solution is to increase the number of cells making each one smaller & therefore more sensitive to accurate classification.

  9. RASTER MAPPING • A major problem with the raster structure is that the shape of features is forced into an artificial grid cell format. • The coded cells represents raster cells, which force uneven lines into straight ones, this result in errors of spatial accuracy in terms of size and exact locations • The shaded maps show how codes are shaded or colored for better visualization ( called a choropleth map) • Two important changes are made in the raster-cell shaded map: • Shape change of the diagonal features • Width increase of the linear features • because of minimum mapping cell sizes

  10. Raster Resolution - Increasing the number of cells on a data set increases spatial resolution, which helps to increase spatial accuracy. - One advantage to using relatively few cells is the associated short processing time and ease of analysis. - The true shape might be unimportant to a project in which the emphasis is fast analysis.

  11. Raster gridding & linear features A higher density of cells in a raster system usually implies more accurate measurement ( but not as good as vector) . The size of the raster cells is therefore important. However the purpose of the project usually determines accuracy needs and resolution.

  12. Vector Data • Vector features appear more realistic than raster features and have better spatial accuracy. • Vector features are defined by their shapes, and the vector system is a coordinate-based data structure,meaning that each shape point is located by X-Y coordinates. • At the beginning and the end of every line or polygon feature is a node. At each bend is a vertex , Point features are stand alone nodes • A shape is recoded by using the coordinates of its shape points, Chains connect the shape points to draw the feature’s outline

  13. chains are vectors or data structure paths that are not part of the actual stored data elements; they are not real lines , they appear on the monitor • Vector system data files store only the coordinates of each node and vertex; the hardware draws the connecting chain segments. • Nodes & vertices are the real , stored data structure elements , chains is a virtual component. • The vector data structure is also known as arc-node modelbecause it uses chains ( arcs ) and end points( nodes) .

  14. Raster Vs Vector • Only the vertices & nodes are stored. • Raster cells are stored & displayed as cells. • It is a node representing a precise coordinate position. • A point is a single cell • A simple line consists of a sequence of cells. • A line consists of 2 nods and a chain that connects them. • in Addition to the 2 nodes & chain vertices mark changes in direction • A more complex line consists of connected cells, in stair-step fashion when they are diagonal • uses single node & several vertices to mark the boundary direction changes. • polygons fill the area within their borders with cells.

  15. ** for programming reasons, usually only one data type is in a GIS file( point file, line file, polygon file). One reason is to avoid confusion between features that may appear alike but are different. For presentation and mapping , the final coverage can have a mix of feature types .

  16. Raster to Vector Feature Conversion The process of changing raster features to vector features normally involves connecting cell centers . There is no way to know precisely where the feature actually existed within the cell area originally, so the center is the logical vertex location. This uncertainty causes problems in accuracy . The final product appears very accurate because its vector, but it’s not more than chains connected with “guessed” vertices.

  17. Note: some GIS’s can make the new vector a version look smoother by “softening” the sharp vertices, but again the user can be fooled into believing the nice appearance is spatially accurate, when in reality it’s not.

  18. Raster and Vector Pros and Cons Raster advantages: • Simple Data Structure. • Easy Analysis • Low-Tech hardware • Remote sensing imagery • Easy Modeling

  19. Raster disadvantages: • Spatial inaccuracies • implicit structure • because of generalization, a raster format cannot tell precisely what exist at a given location • Low Resolution ,because of generalization • Large data set, each cell must have a code , even where nothing exist.

  20. Vector Advantages: • More map-like, data is more accurate, displays are pleasing to the eye • High resolution • High Spatial accuracy • Vector formats have storage advantages • The general public understand what is shown on vector maps • Vector data can be topological

  21. Vector Format Disadvantages: • Complex Structure • Require more powerful, high-tech machines • increased management needs, and thus more Expensive • Demanding teaching • Introductory training

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