Representing Spatial Data Flashcards
Name the four different approaches to spatial analysis
Spatial data manipulation
Spatial data analysis
Spatial statistical analysis
Spatial modeling
Spatial data manipulation
The ability to input, manipulate and transform data once it has been created
Spatial data analysis
It describes datasets and explores identified patterns and processes
Spatial statistical analysis
Uses spatial statistical techniques to deduce the possibility of modeling theories and results obtained from a dataset
Spatial modeling
Predicted spatial outcomes of the investigated phenomena can form the basis of a model to determine effects of processes
Give reasons why modern methods of spatial analysis seem to be poorly represented in the tool kits provided by the typical GIS
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.
GIS data models
They are ways of dividing geographic space so that it can be represented digitally.
Discrete data
Independent numbers
Abrupt boundaries
Example of discrete data
cover-type map
Continuous data
range of numerical values
spatial gradient
Example of continuous data
elevation map
Point objects
Defined as an X,Y and Z coordinates that represents an entity on the ground.
Line objects
A connection between 2 or more points.
Area objects
A collection of connecting lines that create a polygon.
Vector data
A collection of points in geographic coordinates.
Represent objects as points, lines and polygons.
Vector data advantage
File sizes are generally small.
Quite precise in defining objects.
Store multiple attributes with each object.
Vector data disadvantages
Location of each vertex needs to stored explicitly.
Data structures are homogeneous objects.
Don’t have information about the variation within an object.
Raster data
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.
Raster data advantages
no geographic coordinates stored
data analysis is usually easy to program
discrete and continuous data
Raster data disadvantages
cell size determines the resolution
rasterization introduces data integrity
most output maps don’t conform to high-quality cartographic needs
Spatial object
Describe the world as a space made up of discrete units that have a defined spatial reference such as geographic coordinates.
Vector.
Spatial fields
Describe the world as a collection of spatial
distributions of phenomena. A spatial field is, by definition, continuous.
Raster.
Object view
Digital representation of all or part of an entity
classified into different types
instantiated by specific objects
associated behavior
Field view
spatial continuity self definition every location has a value sets of values taken together define a field can represent categorical data
Real-world entity
identifiable
relevant
describable
Entity representation in a digital database
object
Database objectives
management
measurement
analysis
modeling
Entity display on map
map shows users something about the real world
Complications that arise from the object-field view of the world
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
Attribute
any characteristic of an entity selected for representation
Scales for attribute description
constraint on analysis methods choices and inferences drawn
Scales for attribute description measurements
made using a definable process
gives reproducible results
outcomes are as valid ad as possible
Scales for attribute description implications
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
Nominal data
categorical
lowest level
categories are either mutually inclusive or exclusive
label or name
Ordinal data
categorical
ranked classes
intervals between ranks are unknown
Interval data
numerical ranked classes known intervals no zero value measure differences not absolute magnitudes
Ratio data
similar to interval
inherent zero values
Transformation between data types
geometric intersection
buffer
point-in-poylgon
map overlay
Point to point conversion
mean centre
Point to line conversion
network graphs
Point to area conversion
proximity polygons
TIN
point/line buffer
Point to field conversion
interpolation
kernal density estimation
distance features
Line to point conversion
intersection
Line to line conversion
shortest distcance path
Line to area conversion
area buffer
polygon overlay
Line to field conversion
distance surface
Area to point conversion
centroid
Area to point conversion
graph of area skeleton
Area to area conversion
watershed delineation
Area to field conversion
pycnophylatic interpolation/surface models
Field to point conversion
surface specific points
Field to line conversion
surface network
Field to field conversion
equivalent vector field
Fractal dimension
ratio providing a statistical index of complexity comparing how detail in a pattern changes with the scale at which it is measured
