raster data model

Raster Data ModelRaster Data Model

How to Represent Point Features in How to Represent Point Features in Raster Data ModelsRaster Data Models

How to Represent Line Features in How to Represent Line Features in Raster Data ModelsRaster Data Models

How to Represent Polygon Features How to Represent Polygon Features in Raster Data Modelin Raster Data Model

Raster Data ModelsRaster Data Models

For Raster Model there are ◦Array of pixels (each pixel representing a

specific value)◦A matrix consisting of rows and columns with

each grid or pixel representing a specific value

Raster Data ModelRaster Data Model

Raster data is an abstraction of the real world where spatial data is expressed as a matrix of cells or pixels with spatial position implicit in the ordering of the pixels

They store each cell in the matrix regardless of whether it is a feature or simply 'empty' space

Typically these cells are square and evenly spaced in the x and y directions

Raster Data Model - ExampleRaster Data Model - Example

Cell ValueCell Value

Cell values can be either positive or negative, integer, or floating point

Integer values are best used to represent categorical (discrete) data

Floating-point values to represent continuous surfaces

Cells can also have a ‘No Data’ value to represent the absence of data

Grid Size and ResolutionGrid Size and ResolutionPixel/cell refers to the smallest unit of

information available in an image or raster map

Cell dimension specifies the length and width of the cell in surface units, e.g. the cell dimension may be specified as 30 meters on each side

volume of data increases as the cell dimension gets smaller

Reducing the cell dimension by four causes a sixteen fold increase in the number of cells

Smaller cell size provides greater spatial detail

Grid Size and ResolutionGrid Size and Resolution

Generic Structure for a GridGeneric Structure for a Grid

Raster Data ModelRaster Data Model

Smaller Cell Size

• Higher resolution

• Higher feature spatial accuracy

• Slower display

• Slower processing

• Larger file size

Smaller Cell Size

• Higher resolution

• Higher feature spatial accuracy

• Slower display

• Slower processing

• Larger file size

16 m

Larger Cell Size

• Lower resolution

• Lower feature spatial accuracy

• Faster display

• Faster processing

• Smaller file size

Larger Cell Size

• Lower resolution

• Lower feature spatial accuracy

• Faster display

• Faster processing

• Smaller file size

16 m

Data Accuracy with CellData Accuracy with Cell

Value is correct when variable value is uniform over the raster cell

In case of within cell variation then average, central or most common value prevails

Mixed Pixel Problem

Types of Raster DataTypes of Raster Data

Thematic Raster◦ Like a map describes the features

and characteristics of an area and their relative position in space

◦ Cell values are measured quantity or classification of a particular phenomenon (either integers or real numbers

◦ Stored in a single bandImage Raster

◦ Cell values represent reflected or emitted light/energy

◦ Usually in 3 bands◦ Satellite image or scanned

photographs

Rasters – As Thematic MapRasters – As Thematic Map

By grouping the values of multispectral data into classes (such as vegetation type) and assigns a categorical value

Rasters – As Base MapRasters – As Base Map

Raster – As Surface MapRaster – As Surface Map

Rasters provide an effective method of storing the continuity as a surface

Rasters – As Attributes of a FeatureRasters – As Attributes of a Feature

Rasters used as attributes of a feature may be digital photographs, scanned documents, or scanned drawings related to a geographic object or location

Raster Attribute TableRaster Attribute Table

Raster values and other attributes are stored in the Value Attribute Table (VAT)

A thematic raster contains at least two items in its VAT◦Value:

Represents some characteristics being mapped◦Count:

Number of cells that share the same value

Raster Attribute TableRaster Attribute Table

Zone and RegionZone and Region

Cells with same value makeup ‘Zone’ ◦The size of the zone is

defined by the ‘count’ item

A set of contiguous cells with the same value is called a ‘Region’

Raster OverlayRaster Overlay

Raster OverlayRaster Overlay

Raster OverlayRaster Overlay

‘‘NoData’ ValueNoData’ Value

Represents missing or unknown information

When a cell is vacant, it’s assigned ‘NoData’ value

‘NoData’ remain always ‘NoData’ for ESRI rasters unless specifically requested

Combining 2 or more ESRI rasters will retain ‘NoData’ values in the outer raster

Acquiring Raster DataAcquiring Raster Data

Satellite Remote SensingAerial ImagingUSGS Raster sources

◦DOQQ: Digital Orthophoto Quarter Quads are rectified scanned aerial photographs

◦DRG: Digital Raster Graphics are scanned USGS topo sheets

◦DEM: Digital elevation model (DEM) is a digital representation of ground surface topography

Data SourcesData Sources

Digitizing existing mapsScanning existing mapsDigital photogrammetric map productionEntry of computed coordinates from field

measurements

PRODUCT NAME DATA TYPE

DESCRIPTION SCALE

USGS DEM "Digital Elevation Model" Raster grid Elevation x,y,z values used for 3 dimensional display and topographic analysis.

1:24,0001:100,0001:2,000,000

USGS DOQQ "Digital OrthophotoQuarter Quad"

Raster TIFF Georeferenced digital orthorectified aerial photography

1:12,000

USGS DRG "Digital Raster Graphic" Raster TIFF Georeferenced digital scans of USGS topo sheets.

1:24,000

DEM

DOQQ

DRG

Raster Data – A Simple Data StructureRaster Data – A Simple Data Structure

A simple data structure—A matrix of cells with values representing a coordinate and sometimes linked to an attribute table

Advantages of Raster Data FormatsAdvantages of Raster Data FormatsCan represents different types of continuous

surfaces and ability to perform surface analysisComputing/processing is fastSurface data faster to displayOverlaying maps is easyIntegration of remotely sensed imagery is

straightforwardTiling facilitates easy handling of large dataGood for accomplishing complex analysis

operations through complex raster expressions (A huge variety of complex spatial and advanced statistical analyses are supported)

Only solution for some application which can not handled by vector◦ Hydrologic modeling, spread of wild fire, air pollution

dispersion etc.

Disadvantages of Raster Data Disadvantages of Raster Data FormatsFormats

Spatial inaccuracies due to the limits imposed by the raster dataset cell dimensions.

Very large datasets needs more memory space and more processing time◦ Changing cells to one-half the current size

requires as much as four times the storage space

There is also a loss of precision

Advantages/Disadvantages of Advantages/Disadvantages of Raster and VectorRaster and Vector

Raster Vector

Precision in graphics

Traditional cartography

Data volume

Topology

Computation

Update

Continuous space

Integration

Discontinuous

Source: http://www.geom.unimelb.edu.au/gisweb/GISModule/GIST_Raster.htm

Homework 1 (T)Homework 1 (T)1. Read Chapter 2 of the Text Book (Bolstad) –

(specially the sections covered in class lectures)

Conversion Between Raster and Conversion Between Raster and Vector Data ModelsVector Data Models

http://web.pdx.edu/~jduh/courses/geog492w09/Week2b.pdf

Spatial Data ConversionSpatial Data Conversion

Vector to Raster or RasterizationRaster to Vector or Vectorization

Converted data is less accurate than original data

Vector to Raster (V2R)Vector to Raster (V2R)Assign a cell value for each position

occupied by vector features

Vector to Raster Encoding Vector to Raster Encoding MethodsMethods

Center Cell Method◦The center location of the cell determines the

raster value encoded from the vector dataMajority of Cell Method

◦The value in the vector dataset that covers the majority of the cells determines the cell value

Weighted Cell Method◦Analyst determines which vector value is most

important by weighting the optionsPercent of cell method

◦Encodes the cell by multiple values based on the percentage of the cell taken up by each feature

Vector to Raster Encoding Vector to Raster Encoding MethodsMethods

Conversion of Vector Conversion of Vector PointPoint FeatureFeature

Represented by a value in a raster cellAssigned to the cell containing the point

coordinateHave at least the dimension of the raster cell

after conversion

Problem:Problem:

If the cell size is too large, two or more vector points may fall in the same cell

To avoid this problem a cell size is chosen having the diagonal dimension smaller than the distance between the two closest point features

Conversion of Vector Conversion of Vector LineLine Feature Feature

Output depends on the input algorithm used

Raster cells may be coded using different criteria/rules

1. Assign a value to a cell if a vector line intersects with any part of the cell– Line connections maintained– Wider lines

Conversion of Vector Conversion of Vector LineLine Feature Feature

2. Assign a cell as occupied by a line only when the cell center is “near” a vector line segment

– May lead to discontinuity in lines– Thinner linear features

Conversion of Vector Conversion of Vector AreaArea Feature Feature

Boundaries among different polygons are identified as in vector to- raster conversion for lines◦Assign the cell to the area if more than one half

the cell is within the vector polygonOR

◦Assign a raster cell to an area feature if any part of the raster cell is within the area contained within the vector polygon

Interior regions are then identifiedEach cell in the interior region is assigned

a given value

Raster to Vector (R2V)Raster to Vector (R2V)

Point, line, or area features represented by raster cells may be converted to corresponding vector data coordinates and structures

The quality and resolution of the raster image are key factors for the quality and accuracy of the vectorized data

R2V - Point FeatureR2V - Point Feature

A single raster cell represents point feature

Each vector point feature is assigned the coordinate of the corresponding cell center

R2V - Linear FeatureR2V - Linear Feature

Linear features represented in a raster environment may be converted to vector lines

Conversion to vector lines typically involves identifying the continuous connected set of grid cells that form the line.

Cell centers are typically taken as the locations of vertices along the line

Lines may then be “smoothed” using a mathematical algorithm to remove the “stair-step” effect.

R2V - Linear FeatureR2V - Linear Feature

R2V - Area FeatureR2V - Area Feature

Each raster cell is assigned an attribute value

Boundaries are set up between different attribute classes

A polygon is created by storing x and y coordinates for the points adjacent to the boundaries

R2V - Conversion ErrorsR2V - Conversion Errors

Example:

Vector to raster conversion generally involves a loss in precision

ArGIS Tools for ConversionArGIS Tools for ConversionSpatial Analyst, ArcScan and ArcToolbox

Conversion Tools◦Raster to polygon conversion◦Contour Generation◦Surface Interpolation from point data◦Etc.

Raster Operation in ArcGISRaster Operation in ArcGISSimple Mathematical Operations

Conditional AnalysisConditional Analysis

Conditional Tool: ArcToolbox> Spatial Analysis Tools > Conditional Toolset

ExtractionExtraction

Extraction Tool: ArcToolbox> Spatial Analysis Tools > Extraction Toolset

◦Extract by Attribute

ExtractionExtraction

◦Extract by Mask

ReclassifyReclassify

To reassign raster values in order to create new values

Spatial Analyst > Reclass Toolset

Single Output Map AlgebraSingle Output Map Algebra

Spatial Analyst > Map Algebra toolsetTo write single line equations with map

algebra expressions Examples:

Cell Statistics ToolsCell Statistics Tools

ReferencesReferencesChapter 2 of the text http://mason.gmu.edu/~mvenigal/David P. Lusch, 1999Ron Briggs UT Dallashttp://www.sli.unimelb.edu.au/gisweb/http://bgis.sanbi.org/GIS primer/page_15.h

tmhttp://webhelp.esri.com/arcgisdesktop/9.2/http://gis.esri.comhttp://www.satimagingcorp.com/characteri

zation-of-satellite-remote-sensing-systems.html