Week 8

Question 1

which data type is more locationally acurate (vector or raster)

Answer 1

Vector because of high res outline of boundary

Question 2

Main single advantage of raster analysis

Answer 2

• ability to analyse continuous surface variables (e.g slopes, density, proximity)
• suitable for surface analysis (e.g terrains, flow analysis, traffic travel time)

Question 3

Raster analysis formats

Answer 3

• contour elevation lines
• Red, Green, Blue (RGB) images
• Satellite images
• encoded Integer or real number data

Question 4

Raster cell value types

Answer 4

• georeferenced (e.i numerical value = land type)
• Cells have ground resolution
• integer (whole #), or real/floating (ei. 4.6)

Question 5

Zonal raster (with attributes)

Answer 5

• integer valued
• value signifies zone ID
• Zones need not be contiguous
• linked attribute table

Question 6

Raster (grid cell) overlay

Answer 6

• overlay of grid cells
• each cell has numerical values (can be any scale of measurement)
• two or more layers
• simpler than vector data overlay
• more efficient and no sliver problem
• layers have same coordinate system and projection
• layers comparable in measurement scale

Question 7

map algebra

Answer 7

• mathematic symbols for manipulating geographic data

- can perform all system analysts tools on arcGIS

Question 8

scale of analysis

Answer 8

• Local: per cell
• Neighbourhood: surrounding cells of single target cell
• Zonal: groups of cells defined by a zone
• Global: entire raster (e.i. distance, flow)

Question 9

can map algebra in local cell operation be done on both single layer rasters and multiple layer rasters?

Answer 9

Yes

• single layer: sin, log, sqrt
• Multiple layer: mean min

Question 10

Local cell operation

Answer 10

• Single cell algebra application (e.i mean of two layers)

- Reclassification (or grouping)

Question 11

Neighbourhood Operation

Answer 11

• set off cells to use aggregate functions on

Question 12

Zonal Operation

Answer 12

Two inputs

• Zones: raster with integer value for a group of cells (i.e. administrative area, catchment)
• Value raster: input values of interest to be summarised (i.e population, elevation)

Question 13

Global operation: type of distances

Answer 13

• Euclidean (cell to cell straight line distance)

- Incremental or path

Question 14

Arithmetic operators

Answer 14

• produce a new layer through the operations +, -, *, /

- Any arithmetic operation on NODATA results in NODATA

Question 15

Boolean operations - not, and, or, xor

Answer 15

• Input value of ‘0’ is FALSE and a non-zero value is TRUE
• Output values of TRUE are written as ‘1’
• NODATA=NODATA

Question 16

Combinatorial operators

Answer 16

-assign new output values to corresponding combination of input values

Question 17

Logical operators

Answer 17

ei. difference (DIFF), contained in (IN), over (OVER)

Question 18

Descriptive Cartographic models

Answer 18

• those that describe patterns and pattern associations

Question 19

Prescriptive cartographic models

Answer 19

• those that prescribe actions, solutions or allocations of space
• focus on solving a problem or proposing alternative options
• show user the benefits of manipulation existing attributes

Question 20

Raster analysis applications

Answer 20

• Land use analysis
• Terrain and hydrological analysis
• Environmental analysis

Question 21

Land use analysis

Answer 21

• suitability analysis

- simple models

Question 22

Suitability analysis def + e.g

Answer 22

• illustrates combination of preference maps
  e. g Finding best area of siting a ski resort
• combine layers: depth(m), slope (degrees) and sunlight (aspect direction).
• above factors easily obtain as rasters
• reclassify each factor to score
• Combine with map algebra as weighted combination (e.g weighted influence of snow=50%, slope=30%, sun= 20%

Question 23

Environmental analysis:

Answer 23

• illustrates analysis for data association
  e. g broad vegetation groups (BVG) are stratified by elevation. Output is a zonal summary of BVG and elevation to find statistical association (i.e mean and deviation)