9: Raster Data

Question 1

UNDERSTAND CHARACTERISTICS OF RASTER DATA STRUCTURE

Answer 1

Basic mapping unit: cell/pixel

• spatial resolution size of cell defines the level of spatial detail
• location of cells specified as x,y coordinates
• regions: groups of adjacent cells of the same value
• called a grid dataset

Question 2

AWARENESS OF RASTER MANIPULATION

Answer 2

Raster will allow a user to perform basic manipulations on a raster, or grid data set. Some basic manipulations include:

• reclassify
• convert
• preparation for analysis (set extent and mask)

Question 3

BASIC GIS RASTER ALGEBRA OPERATIONS

Answer 3

Map algebra - way to do mathematical functions with grids/raster layers in GIS

• arithmetic (+,-,x,/)
• relational (>,

Question 4

LOCAL, FOCAL AND ZONAL OPERATIONS

Answer 4

Methods of combining raster layers
Local - 1 to 1 (drilling down)
Focal - Neighbourhood to 1
Zonal - Input raster area as a cookie cutter

Question 5

Cell resolution

Answer 5

What is the length and height of each cell, normally square so the same

High resolution refers to rasters with small cell dimensions - lots of detail, lots of cells, small cells, large file size

Raster can be very inefficient because every cell must have a value even if nothing is there

Question 6

Cell value assignment scheme

Answer 6

value of a cell may be…

• an average for that cell
• total value for the cell
• most common value in the cell
• value at the cell’s centre

Question 7

Raster data sources

Answer 7

Aerial photos
Satellite imagery
Scanned maps

Question 8

Saying about raster vs vector data

Answer 8

Raster is faster but vaster, vector only seems more corrector

Question 9

Raster functions in ArcMap

Answer 9

Map Algebra
Local
Focal / Neighbourhood
Zonal

Question 10

Raster functions in ArcMap

Answer 10

Spatial Analyst Tools:
Local
Map Algebra
Neighbourhood
Zonal

Question 11

Raster functions in ArcMap

Answer 11

Spatial Analyst Tools:
Local
Map Algebra
Neighbourhood (Focal)
Zonal

Question 12

Problems with map algebra

Answer 12

if layers are not coincident (not the same origin/alignment/extent)
or different cell sizes

think about metadata and how data should be added together

Question 13

Mathematical operators

Answer 13

Maximum
Sum
Average

Question 14

Re-classification operations

Answer 14

Binary example:
Cell value = “X” then replace with “1”
Cell value not equal to “X” then replace with “0”

Question 15

Operations on raster layers

Answer 15

Local
Focal (neighbourhood)
Zonal
Global
Boolean

Question 16

Local

Answer 16

Single layer reclassification
One cell from the first grid plus one in the second, equals one in the new grid.
‘drilling down’ through the layers
can +, -, x, /, etc…

Question 17

Zonal

Answer 17

Creates a raster surface of values’ statistics with discrete zones

The user defines the zones, can be any shape, use one raster as the input for the boundary (like a cookie cutter)

Question 18

Focal / Neighbourhood

Answer 18

Value of a pixel on output layer is determined by values of local pixels on input layer

Collect info in the neighbourhood of the original raster and put it into one cell

Example in a 9x9 group find the average and put in the cell in the middle of the group

• neighbourhood
• local neighbourhood
• context operators
• filtering (moving window)
• low pass filter (to blur)

Question 19

Focal neighbourhood shape/size

Answer 19

rectangular - 3x3, 5x5 or other (always odd #s)
circular - specify radius
donut
wedge - radius and angle

Question 20

Global

Answer 20

Value of a pixel on output layer is a function of values of all pixels on input layer

Example, distance from road or power line, each cell has a value of the distance from it