Lecture 7 - Raster Data Flashcards
1
Q
What is a raster data model?
A
Divides geographic area into regular grid of cells in specific sequence (identified by row and column)
- each cell contains a single attribute value
- space-filling: every location corresponds to a cell in the raster (regular tessellation that can be conceptualized as a matrix-like series of cells)
- large file
2
Q
How are feature coordinates expressed in raster?
A
They are implict
- store grid origin (cell in upper left corner)
- store grid resolution: minimum linear dimension of smallest unit of geographic space sampled
- find coordinates indirectly
3
Q
How are attributes expressed in raster?
A
They are explicit
- often only a single attribute assigned to a cell
- otherwise, key identifier in each cell links to related database files containing multiple attributes for each grid cell
4
Q
How do you create a raster file?
A
Although this is rarely done, here are the conceptual steps:
- choose grid resolution (choose raster cell size 1/2 length of smallest feature on map)
- set data type (integer, real)
- overlay grid over study area
- assign attribute code to each grid cell
- repeat process for each map layer
5
Q
What are raster input methods?
A
- manual raster coding
- raster scanning
- existing digital raster data
- RS imagery
- vector to raster conversion
6
Q
What is the manual raster coding input method?
A
Uses spreadsheet, text editor, or digitizer (not popular)
- overlay transparent grid on existing map
- record attribute for each cell (decision rules for mixed cells)
- some digitizer software allows coding of attributes from digitizer puck
7
Q
What is raster scanning input method?
A
Scans maps or aerial photos
- several scanner types: drum scanners for large docs and desktop for small docs
- problems: resampling, editing, raster to vector conversion
8
Q
What is the existing digital raster data input method?
A
- elevation data commonly available in raster form (govt agencies)
- much raster data already in digital form as images
- resampling likely needed so pixels in images coincide with cells in other data layers
9
Q
What is the remote sensing data input method?
A
- airborne imagery: air photos
- satellite imagery: landsat, radarsat
10
Q
What is the vector to raster conversion input method?
A
- coded polygons
- grid overlay with appropriate cell sizes
- Each cell is assigned the attribute code of the polygon which it belongs to
11
Q
How would you convert back to vector from raster?
A
- each raster is assigned attribute value
- boundaries set up b/w different attribute classes
- polygon is created by storing x and y coordinates for the points adjacent to boundaries
12
Q
What conversion errors can occur?
A
- polygons turn blocky if converted back and forth
- may depict incorrect info
13
Q
What does the type of cell value being used depend on?
A
- depends on feature being coded and the GIS used
14
Q
What kind of data is grid-cell representation often used for?
A
- categorical data (nominal/ordinal scale)
- quantitative data (interval/ratio scale)
15
Q
What can raster cell data be coded as?
A
- whole numbers (integers)
- real values (decimal)
- alphabetic values (text)
16
Q
What are the different cell measurement values?
A
- nominal
- ordinal
- interval
- ratio
17
Q
Explain nominal cell measurement values
A
- categories with no order
- identifiers with no relation to a fixed point or linear scale
- legend or linked table provides meaning
- ex. postal codes, soil types
- ex 2. 0 = no data, 1 = residential, 2 = commercial, etc.
18
Q
Explain ordinal cell measurement values
A
- lists of discrete classes with inherent order, but without magnitude or relative proportions
- cell value has meaning
- ex. primary, secondary, undergrad, graduate
19
Q
Explain interval cell measurement values
A
- classes not only with natural sequence, but also with meanings attached to distance b/w sequential values
- cell values have meaning
- ex. time of day, temperatures
20
Q
Explain ratio cell measurement values
A
- same characteristics as interval variables, but have a natural zero as a starting point (can’t be negative)
- cell values have meaning
- ex. age, distance, income
21
Q
What is spatial resolution?
A
The area within a grid cell (cell size) defining how much and area the pixel covers
- smaller the cell, greater resolution and accuracy is
- always trade-off b/w resolution and cost of storage and processing
22
Q
How do coded grid cells work?
A
- a line number and column number define cell’s position in raster data
- data stored in table giving number and attribute value of each cell
23
Q
What are the methods for encoding cells?
A
- presence/absence
- centroid of cell
- dominant type
- percent occurence
24
Q
Explain the presence/absence encoding method
A
- single feature like a well (point) or river (line) is identified as occurring in a cell, no matter how much space it occupies
- for polygons, the polygon which covers highest proportion of cell is recorded
25
Explain the centroid-of-cell encoding method
- presence of entity is recorded only if portion of it occurs directly at central point of each cell
- only good for areal (manmade areas) data
- better for continuously variable quantities, like elevation
- constraining, so not typically used
26
Explain the dominant type encoding method
- encodes presence of entity if it occupies more than 50% of cell
- most used method for polygons
27
Explain the percent occurrence encoding method
- if 3 types land use occur in a cell, then each type would be represented as a percentage of cell it occupies
- only good for areal data
- can only show percentage for one variable, so how do we know what the other percentages are?
28
Describe raster map layers
- layer comprised of one set of cells and associated values (multiple items of info require multiple layers)
- raster data can represent a multiplicity of things (visual images, discrete value, continuous value, null values)
- ex. elevation, counties, roads all would be 3 layers in raster GIS
29
What are the data organization methods and why do we want to organize data?
Useful to organize a raster into a 1D data stream for computer file storage and processing.
1. band interleaved by line (BIL)
2. band interleaved by pixel (BIP)
3. band sequential (BSQ)
- can convert b/w them all
30
Explain the band interleaved by line (BIL) data organization method
- rows follow each other for each characteristic
31
Explain the band interleaved by pixel (BIP) data organization method
- all values for a pixel grouped together
- good if focus on multiple area characteristics
- bad if you want to remove or add a layer
32
Explain the band sequential (BSQ) data organization method
- stores each characteristic in separate file (ex. elevation file, temp file, etc.)
- good for compression and if focus is on one characteristic
- bad if focusing on one area
33
What is the main issue with raster data storage?
- databases consist of many separate grid layers, thereby compounding the file size issue (large storage requirements)
34
What are the common methods of raster data storage?
1. run length encoding
2. chain encoding
3. block encoding
4. quadtrees
35
Explain run length encoding
- code sequence of cells with same attribute value
- save data storage space by counting runs of equal values in cells and storing counts
- limited b/c file is read left to right, one row at a time
- does not work for column redundancy
- basically useless if there is no redundancy at all
- ex. XXXXWWRRRX turns into 4X 2W 3R 1X
36
Explain chain encoding
- similar to run length, but scans rows and columns to define 2D regions with same cell values
- defines region boundary by giving starting point (origin) and cardinal direction to follow as we progress around boundary
- steps: assign number 1-4 to cardinal directions; assign how many grids to move in each direction; assign grid cel value for entire area
37
Explain block encoding
- 2D run length encoding where areas of common cell values are represented with a single value
- square blocks used to tile the area to be represented (array - series of square blocks of largest size possible)
- store origin (centre or bottom left) and radius of each square
38
Explain quadtrees
- recursive subdivision of raster cells into quadrants with the same cell value until a square is homogeneous
- uses variable cell resolution to reduce data storage requirements
- hierarchical tree where each level has four-way branching
- efficient for relatively homogeneous areas (highly dependent on embedding of spatial objects in image space)
39
What are the disadvantages of raster data structure?
- reduced spatial accuracy of discrete objects (decreases reliability of area and distance measures)
- need for large storage capacity
- highly complex analyses can be slow
- some spatial relationships (ex. contiguity and connectivity) may be altered or lost (linear features may be discontinuous, blocky, or merged with others; some features may not be represented)
40
What are the advantages of raster data structure?
- abundant data sources (RS, aerial photos, scanning)
- easy to conceptualize as a method of representing space
- raster programs (algorithms) often computationally simpler and faster than vector
- sampling is done uniformly across space
- better for modelling continuous features than vector
- better for analyses that involve spread, flow, or diffusion processes (ex. surface modelling, overlay)
