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Spatial Modelling > Representing Spatial Data > Flashcards

Representing Spatial Data Flashcards

1
Q

Name the four different approaches to spatial analysis

A

Spatial data manipulation
Spatial data analysis
Spatial statistical analysis
Spatial modeling

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2
Q

Spatial data manipulation

A

The ability to input, manipulate and transform data once it has been created

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3
Q

Spatial data analysis

A

It describes datasets and explores identified patterns and processes

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4
Q

Spatial statistical analysis

A

Uses spatial statistical techniques to deduce the possibility of modeling theories and results obtained from a dataset

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5
Q

Spatial modeling

A

Predicted spatial outcomes of the investigated phenomena can form the basis of a model to determine effects of processes

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6
Q

Give reasons why modern methods of spatial analysis seem to be poorly represented in the tool kits provided by the typical GIS

A

The understanding of spatial data from a GIS view and a spatial analysis view differ.
Spatial analysis is not widely understood.
The spatial analysis perspective can sometimes obscure the advantages of GIS.

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7
Q

GIS data models

A

They are ways of dividing geographic space so that it can be represented digitally.

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8
Q

Discrete data

A

Independent numbers

Abrupt boundaries

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9
Q

Example of discrete data

A

cover-type map

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10
Q

Continuous data

A

range of numerical values

spatial gradient

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11
Q

Example of continuous data

A

elevation map

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12
Q

Point objects

A

Defined as an X,Y and Z coordinates that represents an entity on the ground.

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13
Q

Line objects

A

A connection between 2 or more points.

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14
Q

Area objects

A

A collection of connecting lines that create a polygon.

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15
Q

Vector data

A

A collection of points in geographic coordinates.

Represent objects as points, lines and polygons.

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16
Q

Vector data advantage

A

File sizes are generally small.
Quite precise in defining objects.
Store multiple attributes with each object.

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17
Q

Vector data disadvantages

A

Location of each vertex needs to stored explicitly.
Data structures are homogeneous objects.
Don’t have information about the variation within an object.

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18
Q

Raster data

A

Use regular grid to cover space.
Records an attribute value for each location of the grid cell.
Data structure is continuous.
Each location in space has a value assigned to it.

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19
Q

Raster data advantages

A

no geographic coordinates stored
data analysis is usually easy to program
discrete and continuous data

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20
Q

Raster data disadvantages

A

cell size determines the resolution
rasterization introduces data integrity
most output maps don’t conform to high-quality cartographic needs

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21
Q

Spatial object

A

Describe the world as a space made up of discrete units that have a defined spatial reference such as geographic coordinates.
Vector.

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22
Q

Spatial fields

A

Describe the world as a collection of spatial
distributions of phenomena. A spatial field is, by definition, continuous.
Raster.

23
Q

Object view

A

Digital representation of all or part of an entity
classified into different types
instantiated by specific objects
associated behavior

24
Q

Field view

A 
spatial continuity
self definition
every location has a value
sets of values taken together define a field
can represent categorical data
25
Q

Real-world entity

A

identifiable
relevant
describable

26
Q

Entity representation in a digital database

A

object

27
Q

Database objectives

A

management
measurement
analysis
modeling

28
Q

Entity display on map

A

map shows users something about the real world

29
Q

Complications that arise from the object-field view of the world

A

Objects are not always what they appear to be
objects are usually multidimensional
objects don’t move or change
objects don’t have simple geometries
objects depend on the scale of analysis
objects might have fractal dimension
objects can be fuzzy and/pr have indeterminate boundaries

30
Q

Attribute

A

any characteristic of an entity selected for representation

31
Q

Scales for attribute description

A

constraint on analysis methods choices and inferences drawn

32
Q

Scales for attribute description measurements

A

made using a definable process
gives reproducible results
outcomes are as valid ad as possible

33
Q

Scales for attribute description implications

A

measurer knows what is measured and perform necessary operations
repetition of process yields same results and similar results with different data
measurements are true not accurate

34
Q

Nominal data

A

categorical
lowest level
categories are either mutually inclusive or exclusive
label or name

35
Q

Ordinal data

A

categorical
ranked classes
intervals between ranks are unknown

36
Q

Interval data

A 
numerical
ranked classes
known intervals
no zero value
measure differences not absolute magnitudes
37
Q

Ratio data

A

similar to interval

inherent zero values

38
Q

Transformation between data types

A

geometric intersection
buffer
point-in-poylgon
map overlay

39
Q

Point to point conversion

A

mean centre

40
Q

Point to line conversion

A

network graphs

41
Q

Point to area conversion

A

proximity polygons
TIN
point/line buffer

42
Q

Point to field conversion

A

interpolation
kernal density estimation
distance features

43
Q

Line to point conversion

A

intersection

44
Q

Line to line conversion

A

shortest distcance path

45
Q

Line to area conversion

A

area buffer

polygon overlay

46
Q

Line to field conversion

A

distance surface

47
Q

Area to point conversion

A

centroid

48
Q

Area to point conversion

A

graph of area skeleton

49
Q

Area to area conversion

A

watershed delineation

50
Q

Area to field conversion

A

pycnophylatic interpolation/surface models

51
Q

Field to point conversion

A

surface specific points

52
Q

Field to line conversion

A

surface network

53
Q

Field to field conversion

A

equivalent vector field

54
Q

Fractal dimension

A

ratio providing a statistical index of complexity comparing how detail in a pattern changes with the scale at which it is measured

Spatial Modelling (4 decks)

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