Transcript Chapter 3 Sections 3.5 – 3.7 Vector Data Representation object-based “discrete objects” Vector Data Concepts objects represented by    points lines polygons topology   relationship of objects without respect to coordinates.

Chapter 3
Sections 3.5 – 3.7
Vector Data Representation
object-based
“discrete objects”
Vector Data Concepts
objects represented by
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points
lines
polygons
topology
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relationship of objects
without respect to coordinates
Representation of Vector Data
coordinates
forms
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point: single coordinate
line: string of coordinates with start and
end nodes
polygon: closed loop of coordinates
node vs. vertex
Vector data in ArcView
Must choose form for theme
Cannot mix forms in single theme
Vector Data Model - concepts
spaghetti data model
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fig p. 85
no identities
graphical elements
graphical entities
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requires feature identifier
ArcView - shapefiles
 main file
 index file
 database table
Vector Data Model representation
cartographic representation
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number of arcs and nodes needed to represent
data
may vary with scale
affects accuracy & precision as scale changes
cartographic symbolization
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appropriate form may vary with scale
polygon vs point
Vector Data Model
numerical format
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determined by programmer
double-precision, floating-point is best
Topological Data Model
uses relationships between vector data
of the same form
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arc-node
used for line and polygon data
arcs and nodes are shared
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uses less storage space
simplifies analyses
Topological Data
point: unique coordinates
line
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from & to nodes, intermediate vertices
has unique ID #
may share nodes with other lines (connectivity)
may cross without sharing a node
polygon
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comprised of arcs (lines) and their nodes
has unique ID #
always minimum of two polys: inside and outside
Topological Relationships
properties of geometric figures that do
not change when the shape changes
elements
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adjacency
containment
connectivity
Topological Relationships
point to point: no relationship
line to line
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may share nodes with other lines
(connectivity, adjacency)
may cross without sharing a node
Topological Relationships
polygon
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may share nodes (connectivity, adjacency)
may share arcs (lines)
 (connectivity, adjacency)
 right and left polygons
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may contain another polygon
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 shared arc
 polys are right and left
Use of Topology
data input
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spaghetti digitizing
remove topological errors
polygons identified
very important for later use
spatial searches
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look for shared nodes and arcs
Complex Spatial Objects
holes/islands/enclaves
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contained poly
multiple polys
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common identifier
Topological Errors
fig p. 92
interfere with analysis
must be corrected
Georelational Data Model
ArcView
points, lines & polygons stored
separately
entities stored separately
attribute data stored separately
Object-Oriented Data Model
specially designed software
user-specific
based on the data objects considered
Relationship Between
Representation & Analysis
Raster
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less compact data
structure
simple data model
analysis of spatial
variability
analysis of spatial
relationships of
environmental data
Vector
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compact data structure
complex data model
analysis of distribution
and location of individual
objects
works well with
topological relationships
(ie. land parcels & roads)
difficult overlay
processing
Chapter 4: Data Quality &
Data Standards
Data Quality
“fitness for use”
varies with
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intended use
scale
method of collection
quality of product may only be as good
as the lowest quality data used to
produce it
Data Quality
need for metadata: includes records
relevant to data quality
need for standards: define acceptable
quality
need for training in all areas
Measures of data quality
reliability
accuracy
currency
relevance
timeliness
intelligibility
completeness
known precision
concise
intelligibility
convenience
integrity
More considerations
projection
scale
classification scheme
cartographic quality
metadata
transfer format
Accuracy
how closely the data represent the real
world
limited by
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data collection equipment and technique
intended use
cost
Precision
exactness of representation
numerical data
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number of significant digits
does not imply accuracy
need varies with scale
categorical data
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level of detail
number of categories
residential vs type of residential
Error
deviation, variation, & discrpeancy
lack of accuracy & precision
types
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gross
sytematic
random
Error Sources
table p. 107
original source material
data collection
data automation and compilation
data processing and analysis
inherent & operational
Uncertainty
degree of doubt
accuracy and precision are not known
error is not known (but may be large)
greater when data from multiple
sources & scales are mixed
importance of metadata!!!
Components of data quality
lineage (data history): list p. 109
positional accuracy
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“one line width”
varies with scale
tables p. 109 & 110
attribute accuracy
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numerical
categorical
Components of data quality
logical consistency
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with real world
within model & system
between data sets & files
 boundary errors
 layering errors
completeness
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spatial
thematic
Components of data quality
temporal accuracy
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precision of temporal measurements
age of data
semantic accuracy
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labeling
Using components of data
quality
level of quality desired will vary with
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scale
intended application
transferring data from one application
or scale to another may not be
appropriate
must examine the metadata
Assessment of data quality
positional accuracy
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random sample
root mean square
error (RMSE)
fig p. 113
examine results for
patterns &
concentrations
attribute accuracy
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random sample
error matrix
fig p. 114
errors of inclusion &
exclusion
percent correctly
classified
Kappa Index of
Agreement (p. 116)
Assessment of data quality
considerations
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data checks: field vs. reference file
more precision, less accuracy (sometimes)
sample size & scheme (p. 118)
 original & reference
 varies with data needs and real-world structure
of data to be collected
Error Management
QA/QC
SOPs
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standardized methodology
designed to avoid common errors
important error sources
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digitizing
coordinate transformation
Error Propagation
end product accumulates errors of source
data
fig p. 120 (overly simplified)
complexity
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error characteristics differ
overlay operations differ in type of influence
data set contributions to final product differ
may attempt to reduce at each stage via
examination of product
Error Management
sensitivity analysis
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vary input layers & note effect on results
helps in system design
helps focus input data quality efforts
may use in analyses (create varying scenarios)
reporting data quality
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numerical measures
error matrices
shadow map (p. 123)
Data Standards
reference document that provides rules,
guidelines & procedures
allows
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interaction between entities
benchmark for variation
types
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de facto (by popular use)
de jure (developed by organization)
regulatory
table p 124
Data standard components
standard data products
data transfer standards
data quality standards
metadata standards
Standards
International
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ISO
current, proposed & developing
National
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Spatial Data Transfer Standard (table p.
129)
Standards and GIS
Development
interoperability
data infrastructure