Lecture 2 - Spatial Data Models

Question 1

What is a data model?

Answer 1

• model refers to simplified abstract view of complex reality (a system of entities, phenomena, and processes)
• data model is a computer-based representation of the real world

Question 2

How do we represent reality in GIS?

Answer 2

• geographical variation in real world is infinitely complex, so set of rules is required to convert real variation into discrete info

Question 3

What is the fundamental problem with GIS?

Answer 3

Representations are rarely perfect, complete, or universally accepted

Question 4

What is the thematic approach?

Answer 4

It allows us to organize real world complexity into simple representations to facilitate understanding of natural relationships

Question 5

What are the 3 things that go into representing reality through GIS?

Answer 5

1. Selection: select real world objects to be included in virtual model
2. Representation in a standard way: real world objects must be represented by virtual objects
3. Quantification: comps store numeric values that are assigned to real world features in the GIS

Question 6

What are fields?

Answer 6

• conceptualizes geographical space as being populated by continuous phenomena
• properties that vary continuously over space (a single value can be recorded at every point on the earth’s surface
• ex. temp, elevation

Question 7

What are objects?

Answer 7

• geographic space populated by sets of discrete spatial entities (ex. features)
• objects have identifiable boundaries and are represented by graphical elements (ex. points and lines)
• ex. trees, countries

Question 8

What are the 2 GIS data types?

Answer 8

1. Spatial Data

2. Attribute Data

Question 9

What is spatial data?

Answer 9

• describes absolute/relative location of geographic features
• contains location and shape of geographic features
• represented through points, lines, and polygons
• ex. geographic coordinates of a forest

Question 10

What is attribute data?

Answer 10

• describes characteristics of spatial features
• characteristics can be qualitative or quantitative
• aka tabular data
• ex. forest crown closure, dominant species, height

Question 11

Give examples of characteristics that define objects

Answer 11

• type
• attributes
• relations
• geometry
• quality

Question 12

What are the different types of quantitative data? Explain.

Answer 12

• ordinal: categories that have meaningful order but unknown how big value difference is between them
• ratio: interval categories that have a definite zero (ex. distance)
• interval: interval categories (ex. age groups)

Question 13

What are examples of raster data sources?

Answer 13

• satellite imagery
• aerial photos
• scanned maps
• digital orthophotography (scanned photos mathematically rectified to eliminate displacement effects so its view always appears perpendicular to the ground)
• scanned documents
• raster images

Question 14

What are examples of vector data sources?

Answer 14

Object representation described by points and coordinates

• digitized maps
• GIS data

Question 15

What is the raster data model?

Answer 15

• implementation of the field conceptual model
• incorporates use of grid-cell data structure
• commonly used in natural resource planning
• process:
  1) divide world (study area) into square cells
  2) register corners to the earth (coordinates)
  3) discrete objects represented as collections of cells
  4) represent fields by assigning attribute values to cells
  5) used to represent continuous fields

Question 16

What are the main characteristics of raster data?

Answer 16

• no explicit coding of coordinates required (implicitly in cells layout)
• each raster cell (pixel) can contain only one discrete value
• allow for sophisticated mathematical modelling

Question 17

What is a vector data model?

Answer 17

• implementation of object conceptual model
• through 3 discrete elements (plp)
• spatial location of features (coordinates) is explicit and more compact than raster
• commonly used in urban planning/analysis
• ideal for computer mapping and spatial database management

Question 18

Describe the dimensions of the discrete elements used in vector data models.

Answer 18

Point: zero dimensional object
Lines: 1D (points and lines)
Polygon: 2D (vertices/points and lines all connect)

Question 19

Give examples of plp.

Answer 19

Points: wells, trees, sample locations
Lines: roads, streams
Polygons: lakes, forests, states

Question 20

What are the pros and cons of raster data?

Answer 20

Pros:
- no coordinates stored; location implied by cell
- data analysis quick and easy
- good for mathematical modelling & quantitative analysis
- discrete data accommodated equally as well as continuous data and integrates both data types
- compatible with raster-based output devices
Cons:
- cell size determines resolution
- usually difficult to represent linear features (depending on resolution)
- processing attribute data for large datasets overwhelming
- increased processing requirements (conversions)
- most output maps don’t conform to cartographic needs

Question 21

What are the pros and cons of vector data?

Answer 21

Pros:
- output more aesthetically pleasing
- no conversion required
- accurate geo location of data maintained
- efficient encoding of topology
Cons:
- vertex locations need be stored explicitly
- effective analysis requires conversion to topological structure, which is time consuming
- complex algorithms and processing intensive, so limited functionality of large data sets
- continuous data not effectively represented
- spatial analysis/filtering within polygons impossible