GIS Raster Data ModelsOE-701, Fundamentals of GIS and GPS

What are the two types of Data Models?(e.g., models for graphically representing geographic space)

Note: A database structure need seldom be made to suit a data model. But a well prepared data model is vital for a successful GIS analysis.GIS Basic Data Models Vector and Raster

Raster Data Models (Structure)One model for representing geographic spaceSpatial locations are implicitRelationships between entities/objects are explicitPoints associated with single grid cellLines are a connected sequence of cellsAreas are a sequence of interconnected cells

Vector vs. Raster ImagesSource: Delany p 18

Raster Graphic RepresentationSource: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 99. fig. 4.10.The Raster structure illustrates points, lines, and areas utilizing the confines of cells for representing geographic areas. Raster models dont provide explicit locational information.

Raster Data: Descriptionconsists of a matrix of homogeneous grid cells (usually square in shape) each raster map layer has two origins: the cartesian coordinate origin at the bottom left, referencing a cell's position to a real-world location. the row and column index origin at the top left, referencing a cell location within the grid matrix individual grid cells in raster images are referred to as "picture elements" or "pixels"

Raster Data SourcesSatellite imagery Landsat data; SPOT data Existing cell-based data DEM; Arc/Info Grid; GRASS; IDRISI Scanned imagery aerial photographs; hard copy maps Vector--to--raster conversion

Raster Data: Resolutionthe area within a grid cell (i.e. cell size) defines the spatial resolution of the raster the smaller the cell, the greater the resolution and accuracy (more detailed feature representation) there is a trade-off between resolution and cost of storage and processing

Resolution ExamplesAerial Images: Digital Orthophoto Quadrangles (DOQs) 1 m, 2.5 m, 10 m & 30 m resolutionsSatellite Images: MODIS: 250 m to 1 km per pixel Others: DEMs: 10m & 30mScanned maps, vector conversions varies greatly

Digital Orthophoto: ExampleImage Source:Korte GIS Book. p 75

Spatial Resolution: Selected Satellite SystemsImage Source:Korte GIS Book. p 77

Extent/Scale/Resolution: Selected Satellite SystemsAdvanced Very High Resolution Radiometer (AVHRR)

DEMsImage Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p 94. Fig. 3.17.

Conversion: Vector to RasterConversion of vector data to raster data: (a) Coded polygons; (b) a grid with the appropriate cell size overlaid on top of the polygons (dots represent the center of each grid cell; (c) each cell is assigned the attribute code of the polygon to which it belongs.

Conversion: Raster to VectorSource: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 76. fig.4.30. Conversion of raster data to vector: (a) each raster cell is assigned an attribute value; (b) boundaries are set up between different attribute classes; (c) a polygon is created by storing x and y coordinates for the points adjacent to the boundaries.

Conversion ErrorsImage Source: Clarke, Keith C. (2001). 3rd Ed. Getting Started with Geographic Information Systems. p 96.

Raster Data: Valueseach grid cell stores an associated value that defines which class, group, category, or member the cell belongs the value is either an integer, floating point, or No Data value. Cells with No Data value are excluded during calculations and analysis

Raster Data: Cell measurement valuesnominal: identifiers with no relation to a fixed point or a linear scale. e.g., zip codes; soil types ordinal: lists of discrete classes with inherent order but without magnitude or relative proportions. e.g. primary, secondary, college, graduate interval: classes not only with natural sequence, but also with meanings attached to the distance between sequential values. e.g., time of day, the Fahrenheit temperature scale, PH value ratio: variables with the same characteristics as interval variables, but in addition, they have a natural zero or starting point. e.g., age, distance, income

Raster Cells: CodingSource: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 70. fig. 4.19.A line number and column number define the cells position in the raster data. The data are then stored in a table giving the number and attribute value of each cell.

Spectral Resolution: Selected Satellite SystemsImage Source:Korte GIS Book. p 78

Real World > Coded Grid CellsSource: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 68. fig. 4.18Raster data can be visualized as a grid lying over the real world terrain. Each grid cell has a code stored in the database describing the terrain within that particular cell.

One Object: Multiple Attribute LayersSource: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 70. fig. 4.20.Only one attribute value may be assigned to each cell. Objects with several attributes are represented with a number of raster layers, one for each attribute.

Raster Data Model Method: GRID/LUNR/MAGI (early 80s method)Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 104.fig. 4.12 (a)..

Raster Data Model Method: IMGRID GIS (also early method)Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 104.fig. 4.12 (b).

Raster Data Model Method: Map Analysis Package (MAP)Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 104.fig. 4.12 (c).

Raster Data Input: Presence/Absence MethodSource: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 145. fig. 5.8 (a).

Raster Data Input: Centroid-of-Cell MethodSource: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 145. fig. 5.8 (b).

Raster Data Input: Dominant Type MethodSource: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 145. fig. 5.8 (c).

Raster Data Input: Percent Occurrence MethodSource: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 145. fig. 5.8 (d).

Raster Data: Methods of Compacting Four common methods of storing data Run-length codes Raster chain codes Block codes Quadtrees

Compacting Data Model: Run-length Encoding

Compacting Data Models: Raster Chain & Block Codes

Compacting Data Model: QuadtreesSource: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 107. fig. 4.13 (d).

GIS Graphic Models: Characteristic DifferencesSource: Bernhardsen, Tor. (1999). 2nd Ed. Geographic Information Systems: An Introduction. p 7___. fig. 4.32.

Raster Data Structures/ModelsAdvantagesSimple data structuresLocation-specific manipulation of attribute data is easyMany kinds of spatial analysis and filtering may be usedMathematical modeling is easy because all spatial entities have a simple, regular shapeThe technology is cheapMany forms of data are available

Raster Data Structures/ModelsDisadvantagesLarge data volumesUsing large grid cells to reduce data volumes reduces spatial resolution; loss of information & inability to recognize phenomenologically defined structuresCrude raster maps are inelegant though graphic elegance is becoming less of a problemCoordinate transformations are difficult & time consuming unless special algorithms & hardware are used and even then may result in loss of information or distortion of grid cell shape.

Instead of representing points w/their absolute location, they are represented as a single grid cell. The assumption is that somewhere inside the grid cell, a point object can be found.Although a wide variety of raster shapes are possible (e.g. triangles, hexagons) generally a series of rectangles, or more often, squares called grid cells are used.source: http://classes.csumb.edu/SBS/SBSC227S-01/world/notes/raster.htmlsource: http://classes.csumb.edu/SBS/SBSC227S-01/world/notes/raster.html

Moderate Resolution Imaging Spectrometer observes the earth in 36 wavelength bands ranging from visible to thermal wavelengths with spatial resolutions ranging from 250 m to 1 km per pixel. From the Center for Space Research at the University of Texas at Austin.

SPOT Satellites: Spectral modes of acquisitionhttp://www.spot.com/HOME/system/imexplo/welcome.htmA digital elevation model is a digital model or 3-D representation of a terrain's surface commonly for a planet (including Earth), moon, or asteroid created from terrain elevation data. Many GIS have tools for automatically converting between raster & vector modelsVectorization = converting raster to vectorRasterization = converting vector to rasterNormally, some information/data are lost in conversions; consequently, converted data are less accurate than original data.

source: http://classes.csumb.edu/SBS/SBSC227S-01/world/notes/raster.html

Cell locations, defined in terms of rows and columns, may be transformed to rectangular ground coordinates by assigning coordinates to the upper left cell of a raster (cell 0,0). If the raster is to be oriented north south, the columns are aligned along the northing axis and rows along the easting axis.Spot Satellites: Spectral modes of acquisitionhttp://www.spot.com/HOME/system/imexplo/welcome.htm

Comparison of the Landsat 7 bandshttp://landsat.gsfc.nasa.gov/data/Browse/Comparisons/L7_BandComparison.html In practice, a single cell may cover parts of two or more objects or values. Normally, the value assigned is that of the object taking up the greater part of the cells area (or of the object at the middle of the cell, or based on an average computed for the whole cell). Number of ways to store & reference individual grid cell values, their attributes, coverage names, and legends.GRID/LUNR/MAGI is an early method used (back in early 1980s)In GRID/LUNR/MAGI model each grid cell is referenced individually & is associated w/identically positioned grid cells in all other coverages, like a vertical column of grid cells, each dealing w/a separate theme. Comparisons between coverages are performed on a single column at a time, one cell at a time. Counter intuitive to horizontal map reading (right to left)Land use coverage. Imagine a checker board (white sq=water (1); black=land (0)). Another early GIS raster data model Theme simplified to create only one attribute; Water (red=water (1); black = not water (0))Separate layers used for each category of land use (recreation, agriculture, industry,etc.) 1=present 0=absentIntegrates features from GRID/LUNR/MAGI and IMGRID.Each thematic coverage is recorded & accessed separately by map name or title. Each variable of the coverages theme are coded w/a separate number which can be accessed individually when retrieving the coverage.MAP model is most copied model in the marketplace.Although raster GIS systems traditionally allow single attributes to be stored individually per grid cell, some have evolved to include direct links to DBMS, extending utility of raster GIS. Minimizing the number of coverages and substituting multiple variables for each grid cell in each coverage.One of 4 basic methods of raster data input as defined by Berry & Tomlin (1984).For each grid cell on each coverage a decision is made on the basis of whether the selected entity exists within the given grid cell. hence the name: presence/absence.This is the only useful method for coding points & lines for grid systems, because these entities do not normally take up a large portion of a cells area.One of 4 basic methods of raster data input as defined by Berry & Tomlin (1984).Presence of an entity is recorded only if a portion of it occurs directly at the central point of each grid cell.Requires substantial calculations, since each central point needs to be calculated for each grid cell, then the object will have to be compared with the location of that point.Chances of point or line entities passing directly through the center of a given grid cell are slim. Therefore, centroid-of-cell method should be restricted for use with polygon entities.

One of 4 basic methods of raster data input as defined by Berry & Tomlin (1984).More common method, also considered to be one of the best is dominant type method.Encodes presence of an entity if it occupies more than 50% of the grid cell.Problems: (a) 3 dominant types intersect in one cell neither comprise 50%. (b) meandering river, elongated shapes. In both cases visual inspection may be preferable in determining coding.

One of 4 basic methods of raster data input as defined by Berry & Tomlin (1984).Idea is to give more detail, not by coding just the existence of each attribute but rather by separating each attribute out as a separate coverage, then recording the % of the area of each grid cell it occupies. e.g. map of land use divided into urban & rural categories separated into two more specific coverages (urban & rural). The % urban and rural would be recorded for each grid cell, with 0s entered for absence. If coded properly, urban & rural maps would be perfect compliments.Like centroid-of-cell, used exclusively for polygon data.Like GRID/LUNR/MAGI raster data model, has a problem with data explosion. A map of land use with 15 attributes explodes into 15 coverages, each 1 representing a single attribute.

Source: Demers, Michael. N. (2000). 2nd Ed. Fundamentals of Geographic Information Systems. p. 107.(b) Raster chain code assigns number 1-4 to indicate direction (N, S, E, W), then notes how many grids to move in each direction, along with assigning grid cell value for the entire area.(c) block codes are a modification of run-length encoding scheme, but work in 2 dimensions. Instead of giving starting & ending points, plus a grid cell code, square groups of cells are selected and assigned a starting point, say the center of a corner, pick a grid cell value, and tell the computer how wide the square of grid cells is, based on the number of cells.Somewhat more difficult compacting method. Limited use at this time: (1) Commercial system Spatial Analysis System (SPANS), from Tydac, and (1) experimental system called Quilt based on this scheme.Like block codes, operate on square group of cells, but in this case entire map is successively divided into uniform square groups of grid cells with the same attribute value. Starts w/the entire map & divides into quadrants (NW, NE, SW, SE). If any quadrant is homogeneous (contains grid cells w/same value) the quadrant is stored & no further subdivision is necessary. Each remaining quadrant is further divided into four quadrants, tested for the same rule (common attribute), until completed. Smallest representation is a single cell.Source: Principles of Geographic Information Systems. p 70

Source: Principles of Geographic Information Systems. p 70